CFR 3.2 by Radcliffe Cardiology

Cardiac Failure Review Volume 3 • Issue 2 • Winter 2017

©Radcliffe Cardiology

Volume 3 • Issue 2 • Winter 2017

www.CFRjournal.com

Applying Heart Failure Management to Improve Health Outcomes: But WHICH One? Yih-Kai Chan, Alice M David, Caitlyn Mainland, Lei Chen and Simon Stewart

Sleep-Disordered Breathing During Congestive Heart Failure: To Intervene or Not to Intervene? Ali Valika and Maria Rosa Costanzo

Quality of Physician Adherence to Guideline Recommendations for Life-saving Treatment in Heart Failure: an International Survey Martin R Cowie and Michel Komajda

Whatever Happened to Measuring Ventricular Contractility in Heart Failure? Mark IM Noble

Automated 3D Echocardiography

Virtual image of human heart with cardiogram

ISSN: 2057-7540 CFR 3.2_FC.indd All Pages

Dilemmas in the dosing of heart failure drugs

3D Echo

2D Echo

Automated 3D echocardiography

Radcliffe Cardiology

Lifelong Learning for Cardiovascular Professionals

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ESC Congress Munich 2018 25-29 August Where the world of cardiology comes together

Spotlight: Valvular Heart Disease

Key dates: Mid Dec – 14 Feb

Abstract submission

Mid Jan – 1 March

Clinical Case submission

Mid March – 21 May

Late-Breaking Science submission

31 May

Early registration deadline

31 July

Late registration deadline

www.escardio.org/ESC2018 #ESCcongress

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Volume 3 • Issue 2 • Winter 2017

www.CFRjournal.com

Editor-in-Chief Andrew JS Coats Monash University, Melbourne, Australia and University of Warwick, Coventry, UK

William T Abraham

Alexander Lyon

Ali Ahmed

Theresa A McDonagh

The Ohio State University, USA

Imperial College London, UK

Washington DC VA Medical Center, USA

King’s College Hospital, UK

Inder Anand

Kenneth McDonald

John Atherton

Ileana L Piña

University of Minnesota, USA

St Vincent’s Hospital, Ireland

Royal Brisbane and Women’s Hospital, Australia

Montefiore Einstein Center for Heart & Vascular Care, USA

Michael Böhm

Saarland University, Germany

Kian-Keong Poh

National University Heart Center, Singapore

Alain Cohen Solal

Paris Diderot University, France

A Mark Richards

Henry J Dargie

University of Otago, New Zealand

Western Infirmary, Glasgow

Giuseppe Rosano

Carmine De Pasquale

St George’s University of London, UK

Flinders University, Australia

Jose Antonio Magaña Serrano

Frank Edelmann

National Medical Centre, Mexico

Charité University Medicine, Germany

Martin St John Sutton

Michael B Fowler

Hospital of the University of Pennsylvania, USA

Stanford University, USA

Allan D Struthers

Michael Fu

Ninewells Hospital & Medical School, UK

Sahlgrenska University Hospital, Sweden

Michal Tendera

David L Hare

University of Silesia, Poland

University of Melbourne, Australia

Michael Henein

Maurizio Volterrani

Adelino Leite-Moreira

Cheuk Man Yu

IRCCS San Raffaele Pisana, Italy

Heart Centre and Umea University, Sweden University of Porto, Portugal

The Chinese University of Hong Kong, Hong Kong

Managing Editor Rosie Scott • Production Jennifer Lucy • Senior Designer Tatiana Losinska Sales & Marketing Executive William Cadden • New Business & Partnership Director Rob Barclay Publishing Director Liam O’Neill • Managing Director David Ramsey • Commercial Director David Bradbury •

Editorial Contact Rosie Scot rosie.scott@radcliffecardiology.com Circulation & Commercial Contact David Ramsey david.ramsey@radcliffecardiology.com •

Cover image

credit: 7activestudio © www.istockphoto.com / Box 3 credit: nathan4847 © www.istockphoto.com

•

Design Tatiana Losinska

Radcliffe Cardiology

Lifelong Learning for Cardiovascular Professionals

Radcliffe Cardiology

Published by Radcliffe Cardiology. All information obtained by Radcliffe Cardiology and each of the contributors from various sources is as current and accurate as possible. However, due to human or mechanical errors, Radcliffe Cardiology and the contributors cannot guarantee the accuracy, adequacy or completeness of any information, and cannot be held responsible for any errors or omissions, or for the results obtained from the use there of. Where opinion is expressed, it is that of the authors and does not necessarily coincide with the editorial views of Radcliffe Cardiology. Statistical and financial data in this publication have been compiled on the basis of factual information and do not constitute any investment advertisement or investment advice. Radcliffe Cardiology, Unit F, First Floor, Bourne End Business Park, Cores End Road, Bourne End, Buckinghamshire, SL8 5AS © 2017 All rights reserved ISSN: 2057–7540 • eISSN: 2057–7559

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Established: March 2015 Frequency: Bi-annual Current issue: Winter 2017

Aims and Scope • Cardiac Failure Review aims to assist time-pressured physicians to stay abreast of key advances and opinion in heart failure. • Cardiac Failure Review comprises balanced and comprehensive articles written by leading authorities, addressing the most pertinent developments in the field. • Cardiac Failure Review provides comprehensive updates on a range of salient issues to support physicians in continuously developing their knowledge and effectiveness in day-to-day clinical practice.

Structure and Format • Cardiac Failure Review is a bi-annual journal comprising review articles, expert opinion articles and guest editorials. • The structure and degree of coverage assigned to each category of the journal is the decision of the Editor-in-Chief, with the support of the Editorial Board. • Articles are fully referenced, providing a comprehensive review of existing knowledge and opinion. • Each edition of Cardiac Failure Review is available in full online at www.CFRjournal.com

Editorial Expertise Cardiac Failure Review is supported by various levels of expertise: • Overall direction from an Editor-in-Chief, supported by the Editorial Board comprising leading authorities from a variety of related disciplines. • Invited contributors who are recognised authorities in their respective fields. • Peer review – conducted by experts appointed for their experience and knowledge of a specific topic. • An experienced team of Editors and Technical Editors.

Peer Review • On submission, all articles are assessed by the Editor-in-Chief to determine their suitability for inclusion. • The Managing Editor, following consultation with the Editor-in-Chief sends the manuscript to reviewers who are selected on the basis of their specialist knowledge in the relevant area. All peer review is conducted double-blind. • Following review, manuscripts are accepted without modification, accepted pending modification (in which case the manuscripts are returned to the author(s) to incorporate required changes), or rejected outright. The Editor-in-Chief reserves the right to accept or reject any proposed amendments.

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• Once the authors have amended a manuscript in accordance with the reviewers’ comments, the manuscript is assessed to ensure the revised version meets quality expectations. The manuscript is sent to the Editor-in-Chief for final approval prior to publication.

Submissions and Instructions to Authors • Contributors are identified by the Editor-in-Chief with the support of the Editorial Board and Managing Editor. • Following acceptance of an invitation, the author(s) and Managing Editor, in conjunction with the Editor-in-Chief, formalise the working title and scope of the article. • The ‘Instructions to Authors’ document and additional submission details are available at www.CFRjournal.com • Leading authorities wishing to discuss potential submissions should contact the Managing Editor, Rosie Scott rosie.scott@radcliffecardiology.com

Reprints All articles included in Cardiac Failure Review are available as reprints. Please contact the Publishing Director, Liam O’Neill liam.oneill@radcliffecardiology.com

Distribution and Readership Cardiac Failure Review is distributed bi-annually through controlled circulation to senior healthcare professionals in the field in Europe.

Abstracting and Indexing CFR is abstracted, indexed and listed in PubMed and Crossref. All articles are published in full on PubMed Central one month after publication.

Copyright and Permission Radcliffe Cardiology is the sole owner of all articles and other materials that appear in Cardiac Failure Review unless otherwise stated. Permission to reproduce an article, either in full or in part, should be sought from the publication’s Managing Editor.

Online All manuscripts published in Cardiac Failure Review are available free-to-view at www.CFRjournal.com. Also available at www.radcliffecardiology.com are manuscripts from other journals within Radcliffe Cardiology’s cardiovascular portfolio – including, Arrhythmia and Electrophysiology Review, Interventional Cardiology Review, European Cardiology Review and US Cardiology Review. n

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Contents

www.CFRjournal.com

Foreword

Andrew JS Coats and Giuseppe Rosano

Pathophysiology

Whatever Happened to Measuring Ventricular Contractility in Heart Failure? Mark IM Noble

Diagnosis

Natriuretic Peptide-based Screening and Prevention of Heart Failure

The Prognostic Role of Tissue Characterisation using Cardiovascular Magnetic Resonance in Heart Failure

Joe Gallagher, Chris Watson, Patricia Campbell, Mark Ledwidge and Kenneth McDonald

Robert D Adam, James Shambrook and Andrew S Flett

The Role of Automated 3D Echocardiography for Left Ventricular Ejection Fraction Assessment Ernest Spitzer, Ben Ren, Felix Zijlstra, Nicolas M Van Mieghem and Marcel L Geleijnse

Co-Morbidities

102

Hypertension and Frailty Syndrome in Old Age: Current Perspectives Izabella Uchmanowicz, Anna Chudiak, Beata Jankowska-Polan'ska and Robbert Gobbens

Drug Therapy

108

Dilemmas in the Dosing of Heart Failure Drugs: Titrating Diuretics in Chronic Heart Failure David Pham and Justin L Grodin

Clinical Practice

113

Applying Heart Failure Management to Improve Health Outcomes: But WHICH One? Yih-Kai Chan, Alice M David, Caitlyn Mainland, Lei Chen and Simon Stewart

Devices

116

Value of Telemonitoring and Telemedicine in Heart Failure Management Gian Franco Gensini, Camilla Alderighi, Raffaele Rasoini, Marco Mazzanti and Giancarlo Casolo

Clinical Practice

122

Predictors of Post-discharge Mortality Among Patients Hospitalized for Acute Heart Failure

130

Quality of Physician Adherence to Guideline Recommendations for Life-saving Treatment in Heart Failure: an International Survey

Ovidiu Chioncel, Sean P Collins, Stephen J Greene, Peter S Pang, Andrew P Ambrosy, Elena-Laura Antohi, Muthiah Vaduganathan, Javed Butler and Mihai Gheorghiade

Martin R Cowie and Michel Komajda

Co-Morbidities

134

Sleep-Disordered Breathing During Congestive Heart Failure: To Intervene or Not to Intervene?

140

The Future Role of Cardio-oncologists

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Ali Valika and Maria Rosa Costanzo

Radek Pudil

CARDIAC FAILURE REVIEW

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Foreword

Andrew JS Coats is the inaugural Joint Academic Vice-President of Monash University, Australia and the University of Warwick, UK and Director of the Monash Warwick Alliance

Giuseppe Rosano is Professor of Pharmacology, Director of the Centre of Clinical and Experimental Medicine at the IRCCS San Raffaele, Italy and Professor of Cardiology and Consultant Cardiologist (Hon) at St Georges University of London, UK

e have great pleasure in introducing the latest issue of Cardiac Failure Review to our readers. We are proud that over such a short period the quality of Cardiac Failure Review has been recognised so that the journal has been listed in Pubmed Central and its articles from renowned experts have been widely recognised and enjoyed. This issue tackles major areas of advance in heart failure. Mark Noble, one of the pioneers of the clinical use of measures of left ventricular contractility offers and personal insight and perspective into this area. We no longer hear as much concerning the central role reduced left ventricular contractility plays in the pathophysiology of chronic heart failure, nor the role that agents that increase contractility may play in the future. Of course this is largely because attempts in the 1980s and early 1990s to develop drugs to increase myocardial contractility were either neutral or harmful from the perspective of mortality, despite in many cases, improving symptoms and exercise capacity. Have we been measuring the right things? Perhaps the drugs that were developed in the past had unacceptable side-effects and that newer more targeted pharmacological effects or an appreciation of the complexity of heart failure pathophysiology1 may allow us to develop interventions to increase contractility without calcium overload, pro-arrhythmic effects or increased mortality. We await developments with interest. There remains substantial interest in developing newer, safer positive inotropic agents and devices, both in acute2–6 and in chronic heart failure.7,8 There is a very useful review of the role of natriuretic peptides in heart failure, something that has taken a mainstream position in all recent heart failure guidelines,9 but which still lags behind in routine clinical practice.10,11 Their role is continuing to extend to broader aspects of chronic heart failure care.12 Health-care systems have less well-developed systems for assessing and introducing new diagnostic tests compared to new therapies where the pathway is better understood and considerably more well-trodden, to the benefit of all our patients.13,14 These modern advances are being incorporated into developing world systems for heart failure management15,16 in a way that may allow them to speed up improvement in health care and avoid the exorbitant costs that is so problematic for the developed world health-care industries.

The natriuretic peptides are increasingly blurring the distinction between diagnostic test and therapeutic agent.17,18 There is also an excellent review on the prognostic value of cardiac magnetic resonance (CMR), which is indeed creeping ever more frequently into routine practice, despite the expense of the equipment,19–22 challenging the once uncontested role of advanced echocardiography, a review of 3D applications of which is also covered in this issue. Comorbidities in chronic heart failure play a very important role in the therapy and outlook of our patients,23 due to the increasing age of patients and our achievements in decades past to improve prognosis in many once rapidly fatal chronic disorders. We see reviews on frailty, sleep-disordered breathing, cardio-oncology and an overview by Simon Stewart of how to put it all together. All these topics have attracted considerable attention of late, and efforts abound to interpret the quality of care throughout the world by the use of patient reported outcomes and overall (including co-morbidity-related) quality of life.24 Frailty is seen as a major barrier to effective care, requiring specialist attention.25–28 Sleep apnoea can be the cause of very disabling symptoms for many of our chronic heart failure patients, and yet it remains a poorly understood and baffling complication to many heart failure specialists.29,30 Both central and obstructive sleep apnoea are common in chronic heart

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Foreword faiture, and even overlap in the same patient, yet the optimal screening tests31 and therapy32–34 for each may be very different. There has been much recent discussion on the importance of sleep and the complexity of therapy in the chronic health failure patient,35 and the excellent review by Maria-Rosa Costanzo is well recommended. Cardio-oncology is a rapidly expanding field, and Radek Pudil reviews the role of the emerging specialist cardio-oncologist. There is much similarity between systemic complications of cancer and chronic heart failure, even similar prognostic markers,36 raising the possibility that cardiovascular therapies may play a role in chronic cancer-related syndromes.37 Cancer is known to directly affect the heart,38 in addition to the well-documented toxic effects of many modern anticancer agents.39 Cancer can also be increased in chronic heart failure patients,40,41 as of interest are cardiovascular end-points in cancer trials.42 Finally, but very importantly, we have a review of the dosing choices for diuretics in heart failure, one of the first therapies in chronic heart failure, yet one with the fewest number of adequately-sized randomized clinical trials.

Acknowledgements The authors are proud to be the editors of Cardiac Failure Reviews. We acknowledge the importance of ethical publishing, and hereby state that we abide by the statement of ethical publishing in biomedical journals.43 n

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L ouridas G, Lourida K. Progressive nature of heart failure and systems biology. 2015. Available at: http:// dx.doi.org/10.17987/icfj.v3i0.88 (accessed on 6 November 2017). Di Tano G, De Maria R, Gonzini L, et al. The 30-day metric in acute heart failure revisited: data from IN-HF Outcome, an Italian nationwide cardiology registry. Eur J Heart Fail 2015;17(10):1032–41. DOI: 10.1002/ejhf.290; PMID: 26018852. Meredith AJ, Dai DL, Chen V, et al. Circulating biomarker responses to medical management vs. mechanical circulatory support in severe inotrope-dependent acute heart failure. ESC Heart Fail 2016;3(2):86–96. DOI: 10.1002/ehf2.12076; PMID: 28834638. Mebazaa A, Motiejunaite J, Gayat E, et al. Long-term safety of intravenous cardiovascular agents in acute heart failure: results from the European Society of Cardiology Heart Failure Long-Term Registry. Eur J Heart Fail 2017;8. DOI: 10.1002/ejhf.991; PMID: 28990358. Hauffe T, Krüger B, Bettex D, Rudiger A. Shock management for cardio-surgical ICU patients – the golden hours. Card Fail Rev 2015;1(2):75–82. DOI: 10.15420/cfr.2015.1.2.75; PMID: 28785436. Hauffe T, Krüger B, Bettex D, Rudiger A. Shock management for cardio-surgical intensive care unit patient: the silver days. Card Fail Rev 2016 May;2(1): 56–62. DOI: 10.15420/cfr.2015:27:2. PMID: 28785454. Chernomordik F, Freimark D, Arad M, et al. Quality of life and long-term mortality in patients with advanced chronic heart failure treated with intermittent lowdose intravenous inotropes in an outpatient setting. ESC Heart Fail 2017;4(2):122–9. DOI: 10.1002/ehf2.12114; PMID: 28451448. Maniadakis N, Fragoulakis V, Mylonas C, et al. Economic evaluation of cardiac contractility modulation (CCM) therapy with the optimizer IVs in the management of heart failure patients. 2015. Available at: http://icfjournal.org/index.php/icfj/ article/view/173 (accessed on 6 November 2017). Ponikowski P, Voors AA, Anker SD, et al. 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: The task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail 2016;18(8):891–975. DOI: 10.1002/ejhf.592; PMID: 27207191. Jackson CE, Haig C, Welsh P, et al. The incremental prognostic and clinical value of multiple novel biomarkers in heart failure. Eur J Heart Fail 2016;18(12):1491–8. DOI: 10.1002/ejhf.543; PMID: 27114189. D’Elia E, Vaduganathan M, Gori M, et al. Role of biomarkers in cardiac structure phenotyping in heart failure with preserved ejection fraction: critical appraisal and practical use. Eur J Heart Fail 2015;17(12):1231–9. DOI: 10.1002/ejhf.430; PMID: 26493383. Nair N, Gongora E. Correlation of coagulation pathway parameters with BNP, ejection fraction and NYHA class in heart failure. 2016. Available at: http://icfjournal.org/index.php/icfj/article/view/258 (accessed on 6 November 2017). Rutten FH, Gallagher J. What the general practitioner needs to know about their chronic heart failure patient. Card Fail Rev 2016;2(2):79–84. DOI: 10.15420/ cfr.2016:18:1. PMID: 28785457. Asphaug M, Skadberg Ø, Dalen I, Dickstein K. Natriuretic peptide levels taken following unplanned admission to a cardiology department predict the

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duration of hospitalization. Eur J Heart Fail 2016;18(12): 1499–1505. DOI: 10.1002/ejhf.604; PMID: 27502001. Okeahialam BN, Agbo HA, Ogbonna C, et al. Rarity of heart failure in a traditional African population; a rural community based study. 2016. Available at: http://icfjournal.org/index.php/icfj/article/view/275 (accessed on 6 November 2017). Khalil SI, Khalil S, Albadri HK, et al. Emergence of ischemic cardiomyopathy as the main cause of heart failure in urban Sudanese population. 2015. Available at: http://icfjournal.org/index.php/icfj/article/view/82 (accessed on 6 November 2017). Volpe M, Battistoni A, Mastromarino V. Natriuretic peptides and volume handling in heart failure: the paradigm of a new treatment. Eur J Heart Fail 2016; 18(4):442–4. DOI: 10.1002/ejhf.508; PMID: 27203476. Lee NS, Daniels LB. Current understanding of the compensatory actions of cardiac natriuretic peptides in cardiac failure: a clinical perspective. Card Fail Rev 2016;2(1):14–9. DOI: 10.15420/cfr.2016:4:2; PMID: 28848655. Haghikia A, Röntgen P, Vogel-Claussen J, et al. Prognostic implication of right ventricular involvement in peripartum cardiomyopathy: a cardiovascular magnetic resonance study. ESC Heart Fail 2015;2(4):139–49. Melero-Ferrer JL, López-Vilella R, Morillas-Climent H, et al. Novel imaging techniques for heart failure. Card Fail Rev 2016;2(1):27–34. DOI: 10.15420/cfr.2015:29:2. PMID: 28875038. Venero JV, Doyle M, Shah M, et al. Mid wall fibrosis on CMR with late gadolinium enhancement may predict prognosis for LVAD and transplantation risk in patients with newly diagnosed dilated cardiomyopathypreliminary observations from a high-volume transplant centre. ESC Heart Fail 2015;2(4):150–9. DOI: 10.1002/ehf2.12041; PMID: 27708858. Aschauer S, Kammerlander AA, Zotter-Tufaro C, et al. The right heart in heart failure with preserved ejection fraction: insights from cardiac magnetic resonance imaging and invasive haemodynamics. Eur J Heart Fail 2016;18(1):71–80. DOI: 10.1002/ejhf.418; PMID: 26449727. Triposkiadis F, Giamouzis G, Parissis J, et al. Reframing the association and significance of co-morbidities in heart failure. Eur J Heart Fail 2016;18(7):744–58. DOI: 10.1002/ejhf.600; PMID: 27358242. Ahmeti A, Ibrahimi P, Bytyçi I, et al. Use of the “Minnesota Living with Heart Failure Questionnaire” quality of life questionnaire in Kosovo’s heart failure patients. Available at: http://icfjournal.org/index. php/icfj/article/view/390 (accessed on 6 November 2017). de Vries NM, Staal JB, van der Wees PJ, et al. Patientcentred physical therapy is (cost-) effective in increasing physical activity and reducing frailty in older adults with mobility problems: a randomized controlled trial with 6 months follow-up. J Cachexia Sarcopenia Muscle. 2016;7(4):422-35. DOI: 10.1002/ jcsm.12091; PMID: 27239405. Vidán MT, Blaya-Novakova V, Sánchez E, et al. Prevalence and prognostic impact of frailty and its components in non-dependent elderly patients with heart failure. Eur J Heart Fail 2016;18(7):869–75. DOI: 10.1002/ejhf.518; PMID: 27072307. Calvani R, Marini F, Cesari M, et al. Biomarkers for physical frailty and sarcopenia: state of the science and future developments. J Cachexia Sarcopenia Muscle. 2015;6(4):278–86. DOI: 10.1002/jcsm.12051; PMID: 26675566. Yamada S, Kamiya K, Kono Y. Frailty may be a risk marker for adverse outcome in patients with

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congestive heart failure. ESC Heart Fail 2015;2(3): 168–170. DOI: 10.1002/ehf2.12052; PMID: 28834671. Pearse SG, Cowie MR. Sleep-disordered breathing in heart failure. Eur J Heart Fail 2016;18(4):353–61. DOI: 10.1002/ejhf.492. Cowie MR, Woehrle H, Oldenburg O, et al. Sleepdisordered breathing in heart failure – current state of the art. Card Fail Rev 2015;1(1):16–24. DOI: 10.15420/ cfr.2015.01.01.16; PMID: 28785426. Savage HO, Khushaba RN, Zaffaroni A, et al. Development and validation of a novel non-contact monitor of nocturnal respiration for identifying sleepdisordered breathing in patients with heart failure. ESC Heart Fail 2016;3(3):212–9. DOI: 10.1002/ehf2.12086; PMID: 28834663. Vazir A, Bronis K, Pearse S. Should we let sleeping dogs lie? Controversies of treating central sleep apnoea in HFrEF following the SERVE-HF study. Card Fail Rev 2016;2(2):113–4. DOI: 10.15420/cfr.2016:8:2; PMID: 28785464. Jagielski D, Ponikowski P, Augostini R, et al. Transvenous stimulation of the phrenic nerve for the treatment of central sleep apnoea: 12 months’ experience with the remede¯®System. Eur J Heart Fail 2016;18(11):1386–93. DOI: 10.1002/ejhf.593; PMID: 27373452. Stewart Coats AJ. SERVE-HF – was treating a central neurological disturbance of breathing control by a mechanism initially designed to keep open an obstructed airway always doomed to fail? 2015. Available at: http://j-atamis.org/index.php/icfj/article/ view/184 (accessed on 6 November 2017). Cowie MR. Central sleep apnoea: to treat or not to treat? Eur J Heart Fail 2016;18(11):1394–5. DOI: 10.1002/ ejhf.635. PMID: 27634624. Anker MS, Ebner N, Hildebrandt B, et al. Resting heart rate is an independent predictor of death in patients with colorectal, pancreatic, and non-small cell lung cancer: results of a prospective cardiovascular longterm study. Eur J Heart Fail 2016;18(12):1524–34. DOI: 10.1002/ejhf.670; PMID: 27910284. Akpek M, Ozdogru I, Sahin O, et al. Protective effects of spironolactone against anthracycline-induced cardiomyopathy. Eur J Heart Fail 2015;17(1):81–9. DOI: 10.1002/ejhf.196; PMID: 25410653. Kazemi-Bajestani SM, Becher H, Ghosh S, et al. Concurrent depletion of skeletal muscle, fat, and left ventricular mass in patients with cirrhosis of the liver. J Cachexia Sarcopenia Muscle 2016;7(1):97–9. DOI: 10.1002/jcsm.12093; PMID: 27066321. Murbraech K, Solheim O, Aulie HM, et al. The impact of cisplatinum-based chemotherapy on ventricular function and cardiovascular risk factors in female survivors after malignant germ cell cancer. ESC Heart Fail 2015;2(3):142–9. DOI: 10.1002/ehf2.12048; PMID: 28834675. Banke A, Schou M, Videbaek L, et al. Incidence of cancer in patients with chronic heart failure: a longterm follow-up study. Eur J Heart Fail 2016;18(3):260–6. DOI: 10.1002/ejhf.472; PMID: 26751260. von Haehling S. Adding insult to injury: heart failure and incident cancer. Eur J Heart Fail 2016;18(3):267–8. DOI: 10.1002/ejhf.491; PMID: 26833681. Vaduganathan M, Prasad V. Cardiovascular risk assessment in oncological clinical trials: is there a role for centralized events adjudication? Eur J Heart Fail 2016;18(2):128–32. DOI: 10.1002/ejhf.457; PMID: 26663426. Shewan LG, Coats AJ, Henein M. Requirements for ethical publishing in biomedical journals. 2015. Available at: http://icfjournal.org/index.php/icfj/ article/viewFile/4/86 (accessed on 6 November 2017).

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Pathophysiology

Whatever Happened to Measuring Ventricular Contractility in Heart Failure? Mark IM Noble Department of Medicine and Therapeutics, University of Aberdeen, Aberdeen, UK

Abstract Attempts have been made to assess and measure ventricular contractility in patients and whether it can be used to identify heart failure. Due to the assumption that if the contractility of all the muscle fibres in a heart were lower, could it be called heart failure? Early attempts involved the assumption of a model of muscle that had a contractile unit in series with an elastic element, but this was found to be incorrect. Further attempts applied the series elastic model but this model also proved challenging. However, one method has assessed changes in contractility in a given patient, in response to an intervention, but could not compare contractility in a patient with heart failure with a normal person. End-systolic pressure-volume (ESPV) is regarded as a more correct index of contractility and this method was used to confirm changes in contractility from beat to beat during AF, showing results that end-systolic volume varied and indicating a shift of ESPV from beat to beat. This review will discuss the difficulty in measurement, the complicated nature of myocardial fibre orientation and hypertrophy, and whether myocardial contractility failure precipitates increased global heart failure.

Keywords Heart failure, myocardial contractility, cardiac muscle, end-systolic volume, end-diastolic volume, ejection fraction Disclosure: The author has no disclosures to declare. Received: 20 September 2017 Accepted: 1 November 2017 Citation: Cardiac Failure Review 2017;3(2):79–82. DOI: 10.15420/cfr.2017:17:1. Correspondence: Mark IM Noble, Department of Medicine and Therapeutics, University of Aberdeen, King’s College, Foresterhill, Aberdeen AB24 3FX, UK. E: mimnoble@mac.com

The simplicity of the concept of contractility is illustrated in Figure 1, which is the case for a strip of muscle connected to a force transducer. If one records the force of a contraction, and then measures it again at a longer length, the force is found to be higher. If by making other interventions, such as increasing the calcium ion (Ca2+) concentration of the extracellular fluid, and the force is higher, it is called increased contractility, or lower contractility if the force is lower if the contractility is lower in all the fibres in a heart, could that be called heart failure? Therefore, can you measure contractility in heart failure?

Cardiac Muscle Mechanics The fundamental mechanism of cardiac muscle was identified in strips of heart muscle from which the cell membrane had been removed in a solution where intracellular Ca2+ was simulated, so the length-force curve could be measured at different Ca2+ concentrations, i.e. complete lengthforce curves at different levels of contractility via Ca2+.1 The purpose of muscle contraction is to create movement, so one also needed to characterise the way cardiac muscle shortened. Early attempts involved the assumption of a model of muscle that had a contractile unit in series with an elastic element, but this was found to be incorrect2 and a complete characterisation had to wait for the development of the laser diffraction technique for measuring sarcomere length (SL).3 Sarcomere shortening velocity has an inverse hyperbolic relationship to force (load). Increasing SL increased velocity at any given load except in the absence of any load (maximum velocity V0). With an increased contractility (increased Ca2+), velocity increased at all levels of load.3 An attempt was made to apply the series elastic model to assessing contractility in patients by measuring Vmax as dP/dt/P, where dP/dt is the rate of rise of left ventricular pressure (LVP) and P is the LVP. Not only was the model wrong, but which P do you take? If one takes the

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developed pressure above diastolic, Vmax starts at zero and dP/dt/P is infinity, which is absurd. If you take the total pressure, dP/dt/P can be anything you like (according to your zero pressure reference value) and Vmax is not of use.4 A simpler and quite useful index is dP/dtmax, the maximum rate of rise of LVP, which, by chance, is not affected much by end-diastolic volume (EDV) in the intact human, but does reflect changes in contractility.5 However, one can use this method to assess changes in contractility in a given patient, in response to an intervention, but not compare contractility in a patient with heart failure with a normal person because the rate of pressure rise also depends on synchronicity of contraction of the whole left ventricle.

Frank and Starling Otto Frank (1865–1944)6 and Ernest Henry Starling (1866–1927)7 were contemporary physiologists who both studied the mechanical function of the heart. They both correctly realised that a strip of heart muscle, as with skeletal muscle, developed more force if you increased its length. Starling expressed this as the energy of contraction being dependent on initial length (Starling’s Law),7 but energy is not the same as force. They also both tried to elicit this property in the whole heart, something that is still a difficult exercise today. Starling did not actually perform the necessary experiment to justify his law, because he showed that stroke volume (SV) increased when one increased the EDV. This is a different property that can be demonstrated in the whole heart, which is nothing to do with force depending on length but shows the heart contracts down to the same end-systolic volume (ESV). Frank first showed that isovolumic pressure increased with increase of EDV, and recently I have been involved in helping a German team who are hoping to publish all Frank’s papers in English. It is already clear from these papers that Frank also measured pressure-volume loops, which were later popularised by Sagawa and Suga.8

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Pathophysiology Figure 1: The Difference between the Muscular Force of a Contraction Due to Increase of Length of the Contractile Apparatus (Left) and a Difference in Force Due to a Change in Contractility (Right)

Force

Length

Frank-Starling

Contractility

Figure 2: Pressure and Volume at the End of Ejection Are the Same as the Isovolumic Pressure That Can Be Developed at That Volume

pressure-volume (ESPV) curve that is straight. The slope of the line is called Emax for maximum elastance. The concept of muscular elastance is correct; it expresses the change in force for a given change in length (for the heart the change in pressure for a given change in volume) and is the reciprocal of compliance. Essentially the stiffness (elastance) of the muscle rises and wanes with the time course of the contraction and is proportional to the force. However, I do not think it is linear, so the elastance is the tangent of the pressure-volume curves, which become less steep as the volume increases (Figure 2). Nevertheless, the tangent at any one volume becomes steeper with time during contraction and less steep with time during relaxation. Pressure-volume loops can be recorded in patients using a catheter in the left ventricle that transduces pressure conventionally but also carries electrodes to measure capacitance as an index of volume. By varying the venous return with a vena caval balloon one can record a series of pressure-volume loops and plot the ESPV. One can also introduce an intervention that increases contractility and record a leftward shift of ESPV. ESPV is regarded as a more correct index of contractility than LVdP/dtmax (or maximum acceleration of blood from the left ventricle).10 A reduction in contractility could be induced by getting the subject to breathe carbon dioxide,11 but I am not aware that this has been done while measuring human PV loops. This PV loop method was used to confirm changes in contractility from beat to beat during AF. The difficulty is that ejection pressure is virtually clamped by the aortic pressure, and thus I was not prepared to change LVP with a balloon. Nevertheless, results showed ESV varied, i.e. on strong beats the ventricle emptied more and on weak beats it emptied less, indicating a shift of ESPV from beat to beat.12

Ejection Fraction Isovolumic pressure and end-systolic pressure

Left ventricular pressure

Ejection

Systolic pressure development A

Systolic pressure development B

Filling Left ventricular volume While, in theory, one can conceive all pressure-volume loops starting from the same enddiastolic volume (EDV) and varying the pressure at which ejection starts (A), in experimental practice, one has to accept varying EDVs when varying ejection pressure (B). The maximum stiffness of the muscle (elastance) is the tangent to the end-systolic pressure-volume curve (pressure change divided by volume change) and gets less steep with increasing volume.

Measurement of peak isovolumic pressure (Frank curve)6 was unknown to Starling and is the nearest one can get, in anaesthetised animal experiments, to the force-length relationship of the isolated muscle strip. When the heart is allowed to fill, develop pressure and then eject a stroke volume, one finds the ejection ends at a pressure the same as obtained isovolumically at the same volume.9

Let us imagine the result of a global fall in contractility of the heart, which might be a way of defining heart failure. In Figure 3, this is depicted as a fall to the right of the ESPV. Assuming that fluid retention, if present, has been removed, the heart compensates to keep cardiac output normal by dilating to a new EDV, and thus a reduction in ejection fraction (EF). According to the British Heart Foundation, heart failure is denoted by an EF less than 55 %. Solaro considers that heart failure is a failure of contractility and states, â&#x20AC;&#x153;Disorders of the heart, whatever the cause, remain epidemic in world heath, and detailed understanding of control of cardiac contractility is essential in the quest for measures to prevent, diagnose, and treat these disordersâ&#x20AC;?.13 There is vast literature testing various theories about the metabolic substrate that underlies reduced contractility in heart failure. However, the above example of global fall in contractility of the heart is not realistic. I suppose it might apply to some dilated cardiomyopathies, but what about the common causes, such a coronary disease and the end-stage of untreated hypertension; and in heart failure of hypertrophic cardiomyopathy, I can imagine that contractility is increased. After MI, a scar may form and become a sort of series elastic element for the rest of the heart; therefore, the contractility of the scar is obviously nil, but if you look at the contracting muscle, the contractility may be normal or even increased, maybe due to increased sympatho-adrenergic stimulation. Can one regard heart failure, caused by AF, a failure of contractility, when actually, contractility is changing all the time from beat to beat?12

The Confusion of Understanding Heart Failure I have omitted from this discussion the many papers of Suga and Sagawa because their laboratory always came out with an end-systolic

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A consequence of the adoption of many theories with similar views to Solaro13 is that treatments are devised for heart failure that â&#x20AC;&#x153;manipulate

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Measuring Contractility in Heart Failure cardiac contractility”.14 A favourite drug to treat heart failure used to be stimulation with dobutamine. The resulting problems actually led to the opposite treatment, namely beta-adrenergic blockade15 or to adrenergic stimulation on top of beta-blockade.

Figure 3: The Principle of Ejection Fraction (EF), Which Is Stroke Volume (SV) Divided by End-Diastolic Volume (EDV), and Its Relation to the End-Systolic Pressure-Volume Curve (ESPV) That Defines Contractility

• I s depressed myocyte contractility centrally involved in heart failure? • What is the role of beta-adrenergic signalling in heart failure? • What causes sudden death in heart failure? • Is abnormal cell growth and hypertrophy the cause of heart failure? • Does energy starvation cause heart failure? • What mechanisms underlie diastolic dysfunction in heart failure?

Left ventricular pressure

In 2003 the journal Circulation Research published a series of review articles entitled, ‘Unanswered Questions in Heart Failure’. The unanswered questions were:

EDV 2 EDV 1 SV 2 SV 1

The first of these questions is relevant to the title of the present paper and remained unanswered at the end of the review article.16 The second paper,17 in trying to find some understanding of the apparently illogical success of beta-blockade therapy, got into a tangle of various beta receptor subtypes that did not reveal an answer. The last three questions postulate causes of heart failure other than contractility failure.

What Does Heart Failure Do To Cardiac Cells One can induce heart failure in dogs by pacing the ventricle at 250 BPM for 6 weeks. It has been claimed that myocytes obtained from such animals were longer and thinner than those from controls, and shortening in response to electrical stimulation was reduced.18 Unfortunately, this cannot be accepted as evidence for reduced contractility in heart failure because: myocytes get damaged in the process of isolation and those from heart-failure animals may be more susceptible to such damage; and shortening is not contractility but load dependent, and the load in these experiments is not known. While in the animal, the load would have been abnormally high due to increased wall force in the dilated heart according to the LaPlace relationship. Therefore, studying isolated myocytes has its limitations.

Therapeutic Implications of the ‘Heart Failure Equals Contractility Failure’ Theory It was logical to counteract supposed contractility failure with a drug that increases contractility (‘positively inotropic’). Such a drug should improve contraction whether or not there is contractility failure. Adrenergic stimulation also increases heart rate and both effects increase the demand of the heart for oxygen, so these drugs should only be used for testing, e.g. dobutamine during echocardiography if one is looking for poorly contracting segments that might respond. However, one can also increase contractility with digoxin and with the phosphodiesterase 3 inhibitors, of which the best known is milrinone. Digoxin (which Frank found to be positively inotropic)19 is very useful in controlling heart rate in patients with AF and has proponents for its use in patients with heart failure in sinus rhythm, although this is controversial.20 Milrinone has the advantage that it is effective in the presence of betablockade with no effect on heart rate.21 Digoxin and milrinone should

entish JC, ter Keurs HE, Ricciardi L, et al. Comparison K between the sarcomere length-force relations of intact and skinned trabeculae from rat right ventricle. Influence of calcium concentrations on these relations. Circ Res 1986;58:755–68. DOI: 10.1161/01.RES.58.6.755; PMID: 3719928

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Left ventricular volume A shift of the latter to the right, indicating a fall of ESPV, would reduce the SV but the circulation compensates for this by moving to a higher EDV to keep SV the same. Because of the increase in EDV, EF is reduced. For clarity, the double arrowed horizontal lines are separated but should be superimposed.

probably only be used in acute heart failure as a temporary measure, because they work by increasing intracellular Ca2+. This, in turn, increases the risk of arrhythmias and when given chronically increases mortality, giving milrinone the nickname ‘killrinone’. There are also reports of poor outcomes with digoxin given during sinus rhythm.20 However, improved mortality rate is not everything and in a disease that is fatal, a drug like this might improve the quality of a shorter life. Another approach is to keep intracellular Ca2+ constant but increase the sensitivity of the contractile proteins to Ca2+, thus predicting a lesser risk of arrhythmias.22 A similar drug produced a benefit in dogs with tachycardia-induced heart failure,23 but I am not aware of its clinical development, possibly because it induced arrhythmias in rat hearts.24

Conclusion Possibly, heart failure in dilated cardiomyopathies is due to a global reduction in ventricular contractility. In other types of heart failure, the presence or absence of reduced contractility is unknown. In particular, if the failing heart is subjected to coronary disease in some parts but not others, it is very difficult to determine the contractile state of the unaffected parts. In the case of anatomical abnormalities, e.g. valvular disease and congenital heart disease, the heart may be failing globally but myocardial contractility may be normal. Maybe myocardial contractility failure precipitates increased global failure? With the difficulty in measurement, the complicated nature of myocardial fibre orientation and hypertrophy, it is impossible for me to give a firm opinion. Another paper could be written reviewing all that has been written on these aspects, and are still being written. n

oble MI, Else W. Re-examination of the applicability of the N Hill model of muscle to cat myocardium. Circ Res 1972;31:580– 9. DOI: 10.1161/01.RES.31.4.580; PMID: 5075377 Daniels M, Noble MI, ter Keurs HE, Wohlfart B. Velocity of sarcomere shortening in rat cardiac muscle: relationship of force, sarcomere length, calcium and time. J Physiol 1984;355:

367–81. DOI: 10.1113/jphysiol.1984.sp015424; PMID: 6491996 Noble MI. Problems concerning the application of concepts of muscle mechanics to the determination of the contractile state of the heart. Circulation 1972;45:252–5. DOI: 10.1161/01. CIR.45.2.252; PMID: 5009471 Drake-Holland AJ, Mills CJ, Noble MI, Pugh S. Response

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Pathophysiology

6. 7. 8.

10.

11.

12.

to changes in filling and contractility of indices of human left ventricular mechanical performance. J Physiol 1990;422:29–39. DOI: 10.1113/jphysiol.1990.sp017970; PMID: 1972191 Frank O. [On the dynamics of cardiac muscle.] Zeitschift fur Biologie 1895;32:370–447 [in German]. Starling EH. Linacre Lecture on the Law of the Heart. London: Longmans, Green and Co, 1918. Sagawa K, Suga H, Shoukas AA, Bakalar KM. End-systolic pressure/volume ratio: a new index of ventricular contractility. Am J Cardiol 1977;40(5):748–53. Weber KT, Janicki JS, Hefner LL. Left ventricular force-length relations of isovolumic and ejecting contractions. Am J Physiol 1976;231:337–43. PMID: 961884. Bennett ED, Else W, Miller GA, et al. Maximum acceleration of blood from the left ventricle of patients with ischaemic heart disease. Clin Sci Mol Med 1974;46:49–59. DOI: 10.1042/ cs0460049; PMID: 4811873 van den Bos GC, Drake AJ, Noble MI. The effect of carbon dioxide upon myocardial contractile performance blood flow and oxygen consumption. J Physiol 1979;287:149–61. DOI: 10.1113/jphysiol.1979.sp012651; PMID: 430387 Brookes CI, White PA, Staples M, et al. Myocardial contractility

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13.

14.

15.

16.

17.

18.

is not constant during spontaneous atrial fibrillation in patients. Circulation 1998;98:1762–8. DOI: 10.1161/01. CIR.98.17.1762; PMID: 9788831 Solaro RJ: Heart failure as a failure of contractility. In: Solaro RJ. Regulation of Cardiac Contractility. San Rafael, CA: Morgan & Claypool Life Sciences, 2011; Dorn GW, Molkentin JD. Manipulating cardiac contractility in heart failure: data from mice to men. Circulation 2004;109:150–8. DOI: 10.1161/01.CIR.0000111581.15521.F5; PMID: 14734503 Hall SA, Cigarroa CG, Marcoux L, et al. Time course of improvement in left ventricular function, mass and geometry in patients with congestive heart failure treated with betaadrenergic blockade. J Am Coll Cardiol 1995;25:1154–61. DOI: 10.1016/0735-1097(94)00543-Y; PMID: 7897129 Houser SR, Margulies KB. Is depressed myocyte contractility centrally involved in heart failure? Circ Res 2003;92:350–8. DOI: 10.1161/01.RES.0000060027.40275.A6; PMID: 12623873 Lohse MJ, Engelhardt S, Eschenhagen T. What is the role of beta-adrenergic signalling in heart failure? Circ Res 2003;93:896–906. DOI: 10.1161/01.RES.0000102042.83024.CA; PMID: 14615493 Ravens U, Davia K, Davies CH, et al. Tachycardia-induced failure alters contractile properties of canine ventricular

19. 20.

21.

22.

23.

24.

myocytes. Cardiovasc Res 1996;32:613–21. DOI: 10.1016/S00086363(96)00121-6; PMID: 8881522 Frank O. Die Wirkung von Digitalis (Helleborein) auf das Herz Sitzungsber. Gesell Morph Physiol 1898;14:14–43. van Veldhuisen DJ, de Graeff PA, Remme WJ, Lie KI. Value of digoxin in heart failure and sinus rhythm: new features of an old drug? J Am Coll Cardiol 1996;28:813–9. DOI: 10.1016/S07351097(96)00247-1; PMID: 8837553 Pugh SE, Travill C, Hynd J, Noble MI. Preliminary results of a study of the effects of milrinone infusion in the presence of beta-adrenergic blockade. Royal Society of Medicine International Congress & Symposium Series 1991;172:31–34. Lee JA, Allen DG. EMD 53998 sensitizes the contractile proteins to calcium in intact ferret ventricular muscle. Circ Res 1991;69:927–36. DOI: 10.1161/01.RES.69.4.927; PMID: 1934345 Drake-Holland AJ, Lee JA, Hynd J, et al. Beneficial effect of the calcium-sensitizing drug EMD 57033 in a canine model of dilated heart failure. Clin Sci (Lond) 1997;93:213–18. DOI: 10.1042/cs0930213; PMID: 9337635 Evans SJ, Levi AJ, Lee JA, Jones JV. EMD 57033 enhances arrhythmias associated with increased wall stress in the working rat heart. Clin Sci (Lond) 1995;89:59–67. DOI: 10.1042/ cs0890059; PMID: 7671569.

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Diagnosis

Natriuretic Peptide-based Screening and Prevention of Heart Failure Joe Gallagher, 1 Chris Watson, 2 Patricia Campbell, 1 Mark Ledwidge 1 and Kenneth McDonald 1 1. School of Medicine and Medical Sciences, University College Dublin, Ireland; 2. Centre for Experimental Medicine, Queens University, Belfast, Northern Ireland

Abstract There is increasing interest in the concept of personalised medicine, whereby conditions with common pathophysiologies are targeted together, and also using biomarkers to identify patients who will most benefit from certain interventions. Several data sets indicate that natriuretic peptides are effective in refining risk prediction for heart failure and cardiovascular disease and add predictive power to conventional risk factors. To date two trials have tested the approach of using natriuretic peptides as part of a strategy to identify those at highest risk of cardiovascular events: St. Vincent’s Screening to Prevent Heart Failure (STOP-HF) and N-terminal Pro-brain Natriuretic Peptide Guided Primary Prevention of Cardiovascular Events in Diabetic Patients (PONTIAC). These have shown natriuretic peptide-based screening and targeted prevention can reduce heart failure and left ventricular dysfunction and other major cardiovascular events. This approach is now part of North American guidelines.

Keywords Natriuretic peptides, heart failure, cardiovascular prevention, diabetes Disclosure: The authors have no conflicts of interest to declare. Received: 20 October 2017 Accepted: 24 October 2017 Citation: Cardiac Failure Review 2017;3(2):83–5. DOI: 10.15420/cfr.2017:20:1 Correspondence: Dr Joe Gallagher, STOP HF Unit, St Michaels Hospital, Dun Laoghaire, Co Dublin, Ireland. E: jgallagher@ucd.ie

Increasingly biomarkers are of interest in cardiovascular disease (CVD) for risk stratification. In particular, natriuretic peptides (NPs), which were originally used for the diagnosis of heart failure, are now finding a role in identifying those most at risk of heart failure and other cardiovascular (CV) disorders. Their ability to be measured rapidly through blood tests makes their widespread use more practical. They may also aid in the detection of disease at an earlier stage before structural and functional changes become apparent on imaging (see Figure 1).

Natriuretic Peptides Several data sets1–3 indicate that NPs are effective in refining risk prediction for CVD and add predictive power to conventional risk factors. Conventional risk indicators (e.g. lipids or hypertension) reflect potential for CV damage, whereas early elevations of NP are an endogenous response to often preclinical CV damage, which allows time for intervention. In addition to standard signals for NP release, such as volume overload, other work4 has demonstrated this peptide responds to fibro-inflammation, a fundamental pathophysiological signal present from the outset of many CVDs and indeed comorbidities such as cognitive impairment and ischaemia.5,6 Increases in plasma brain natriuretic peptide (BNP) or N-terminal prohormone BNP (NT-proBNP) concentration have diagnostic and prognostic implications in selected populations, as demonstrated initially in heart failure, and subsequently in early-stage and asymptomatic CVD. Recent reports have suggested that NP provides prognostic information for a wide variety of CVDs beyond that obtained from routine risk factors. A recent study7 showed NT-proBNP is predictive of future CHD and stroke in individuals without known CVD at the time of measurement. The individual participant data meta-analysis included 40 prospective cohorts comprising over 95,000

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individuals. It also suggested the risk prediction with NT-proBNP is greater in older compared with younger individuals.7 The estimation of personal CVD risk in older individuals is difficult using current population-based models, due to the higher incidence of CVD in this group. Levels of B-type NP and N-terminal pro-atrial NP strongly predicted the risk of heart failure, with an increase in the adjusted risk of 77 % and 94 %, respectively, per one standard deviation increment in log peptide values.

Studies on Natriuretic Peptide-based Screening and Prevention Two trials have tested the approach of using NPs as part of a strategy to identify those at highest risk of CV events and targeting treatment to these groups in order to prevent heart failure and other CV disorders. Both these studies – St. Vincent’s Screening to Prevent Heart Failure (STOP-HF) and NT-proBNP Selected PreventiOn of cardiac eveNts in a populaTion of dIabetic patients without A history of Cardiac disease (PONTIAC) – had favourable results. The STOP-HF trial was a pragmatic randomised controlled trial involving one specialist centre and 39 general practices with 1,374 participants. Those included were asymptomatic individuals >40 years old with a history of one or more of the following: hypertension, hyperlipidaemia, obesity, vascular disease (coronary artery disease, cerebrovascular disease and peripheral vascular disease), diabetes mellitus, arrhythmia requiring therapy or moderate to severe valvular disease. Participants were randomised to a control group (receiving routine general practitioners [GP] management and specialist care as required) or BNP-driven collaborative care between the GP and specialist CV centre. In the intervention group, BNP results were made available to GPs, with protocol-driven referral to the specialist CV service,

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Diagnosis

Biomarker based detection

Baseline risk

Earliest molecular detection

Clinical event Structural changes

Current intervention

Cost

Disease burden

Figure 1: Diagrammatic Representation of the Concept of Biomarkers as a Component of Stage B Heart Failure

of all patients with a value of ≥50 pg/ml. Those with BNP values <50 pg/ml received the same care as provided in the control group but with disclosure of BNP values to patients and GPs. Participants with a BNP level of ≥50 pg/ml underwent echocardiography and review by a cardiologist at the study centre, who decided on further investigation and management. The focus of the specialist intervention for those with elevated BNP was multidimensional and included optimal risk factor management and complete investigation and treatment of abnormalities defined on examination or on echocardiography. In addition, all patients received further coaching by a specialist nurse who emphasised individual risk status and the importance of adherence to medication and healthy lifestyle behaviours. A total of 263 patients (41.6 %) in the intervention group had at least one BNP reading of ≥50 pg/ml. The intervention group underwent more CV investigations (control: 496 per 1,000 patient-years versus intervention: 850 per 1,000 patient-years; incidence rate ratio 1.71; 95 % CI [1.61–1.83]; p<0.001) and received more renin–angiotensin– aldosterone system (RAAS)-based therapy at follow up (control: 49.6 %; intervention: 56.5 %; p=0.01). The primary endpoint of left ventricle (LV) dysfunction with or without heart failure was met in 59 (8.7 %) of 677 in the control group and 37 (5.3 %) of 697 in the intervention group (OR 0.55; 95 % CI [0.37–0.82]; p=0.003). Asymptomatic LV dysfunction was found in 45 (6.6 %) of 677 control-group patients and 30 (4.3 %) of 697 intervention-group patients (OR 0.57; 95 % CI [0.37–0.88]; p=0.01). Heart failure occurred in 14 (2.1 %) of 677 control-group patients and 7 (1.0 %) of 697 intervention-group patients (OR 0.48; 95 % CI [0.20–1.20]; p=0.12). The incidence rates of emergency hospitalisation for major CV events were 40.4 per 1,000 patient-years in the control group versus 22.3 per 1,000 patient-years in the intervention group (incidence rate ratio 0.60; 95 % CI [0.45–0.81]; p=0.002). Interestingly there were no statistically significant differences in risk factor control between the control and intervention group at the end of the study. Similar effects have been seen in other positive studies on CVD.8 A subsequent analysis of the cost-effectiveness of this approach was undertaken. The cost per quality-adjusted life year gain was €1,104 and the intervention has an 88 % probability of being cost-effective at a willingness to pay threshold of €30,000.9 In the STOP-HF study it was noted the benefits observed in the intervention group were likely multifactorial. They include facilitating targeted therapy changes, in particular increased used of angiotensin receptor blockers and increased use of diagnostic tests. Although

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blood pressure reduction in both groups was similar, the targeted use of RAAS-modifying therapy may have contributed to the reduction in endpoints through mechanisms other than blood pressure reduction. Patient adherence to therapy and lifestyle advice may have been encouraged by communicating risk status to patients. In the PONTIAC trial, 300 patients with type 2 diabetes, increased level of NT-proBNP (>125 pg/ml) but free from CVD were randomised to either standard treatment at diabetes care units or an ‘intensified’ strategy in which patients were additionally treated at a cardiac outpatient clinic for the up-titration of RAAS inhibitors and beta-blockers. Cardiac disease-based exclusion criteria were one or more of the following: history of cardiac disease; signs of cardiac disease on the ECG such as AF; ST-T-wave abnormalities or a bundle branch block; abnormal echocardiography (with the exception of diastolic dysfunction) defined as low ejection fraction; wall motion abnormalities; significant valve dysfunction or other significant alteration. The primary endpoint of hospitalisation/death due to cardiac disease at 2 years was significantly reduced with use of the intensified strategy (hazard ratio 0.35; 95 % CI [0.13–0.98]; p=0.044). After 12 months there was a significant difference between the control and intensified groups in both the number of patients treated with renin–angiotensin system (RAS) antagonists and beta-blockers and in the dosage reached (p<0.0001 for all). RAS antagonists were up-titrated to 100 % of the recommended dosage in 79 % of cases in the intensified group compared with 42 % in the control group (p<0.0001). Beta-blockers were up-titrated to 100 % of the recommended dosage in 51 % of cases in the intensified group and in only 10 % of cases in the control group (p<0.0001). A combination of 100 % of the RAS antagonist and 100 % of the beta-blocker recommended dosage was achieved in 46 % of cases in the intensified group and in 5 % of cases in the control group (p<0.0001). Similar to STOP-HF, blood pressure was significantly and similarly reduced in both groups after 12 months (p=0.003 control group; p=0.002 intensified group). Heart rate was reduced only in the intensified group (p=0.004) and there was a trend towards this also in the STOP-HF study (p=0.06). Similar to the STOP-HF study there was no significant changes in NP concentrations.

Inclusion in the Guidelines Both the STOP-HF and PONTIAC randomised clinical trials are included in the 2014 Canadian Cardiovascular Society Heart Failure Management Guidelines10 as a recommendation for the use of NPs in at-risk individuals. The guideline suggests for individuals with risk factors for the development of heart failure, NP levels be used to implement strategies to prevent heart failure. In the guideline it is recommended an increased level of NP of BNP >100 pg/ml and NT-proBNP >300 pg/ml be used to avoid overscreening. However, this does not appear to be based on any analysis and is not supported by the cost-effectiveness analysis carried out by the STOP-HF team.9 In more recent guidelines from the US11 it is recommended for patients at risk of developing heart failure, NP biomarker-based screening followed by team-based care, including a CV specialist optimising guideline-directed medical therapy, can be useful to prevent the development of left ventricular dysfunction (systolic or diastolic) or new-onset heart failure. It highlights the need for further studies to determine cost-effectiveness and risk of such screening, as well as its impact on quality of life and mortality rate.

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Natriuretic Peptide Screening and Heart Failure Future Directions The data above highlights the emerging role of NP intervention in international health systems and the need to evaluate the intervention further by comparing to current interventions for heart failure prevention. There is increasing interest in the concept of personalised medicine, whereby conditions with common pathophysiologies are targeted together, and also using biomarkers to identify patients who will most benefit from certain interventions. Approaches such as those used in STOP-HF12 and PONTIAC13 involve personalised medicine using NPs to identify those patients most at risk of CV events from a broad group of conditions, such as hypertension, diabetes, ischaemic heart disease, AF and cerebrovascular disease, and targeting care to the group defined as high risk from within this cohort. In particular, PONTIAC also intensified use of RAAS and beta-blocker therapies in a predetermined fashion. STOP-HF did not have a predefined strategy but did have intensification of RAAS therapy in the intervention group noted at study end. By using molecular markers of disease these approaches identify abnormalities earlier allowing for detection and intervention at an early stage. The ARNI in Asymptomatic Patients With Elevated Natriuretic Peptide and Elevated Left Atrial Volume Index eLEvation (PARABLE) study14 is investigating the use of the angiotensin receptor-neprilysin inhibitor sacubitril/valsartan in those with hypertension and/or diabetes and an elevated NP level. This is based on the concept of using a biomolecular signal to initiate a particular therapy targeted at that biomolecular pathway and represents a study based on the personalised medicine concept. Its primary outcome is left atrial volume index measured by cardiac MRI.

CVD, the continuation of directing resources in a non-discriminatory manner poses a major threat to the sustainability of healthcare systems and heightens inequality. Ongoing difficulty in preventing cardiometabolic diseases and improving outcomes may in part be explained by the uniform direction of resources to a population containing predominantly lower-risk patients. Currently, prevalent approaches to heart failure prevention devote the same level of resources to all individuals in a broad population (typically, a population exhibiting risk factors such as obesity, smoking, or conditions such as diabetes or hypertension). NP-based screening and targeted prevention are a possible approach to this issue.

New Research The emergence of this strategy of NP screening and targeted intervention requires further study of a number of factors, such as: determining optimal levels of NPs for risk stratification, determining the role of other biomarkers (troponin, ST2 and galectin-3 or multiplex panels in refining risk prediction), the development of clinical prediction rules integrating clinical features and biomarkers to improve risk prediction, the evaluation of alternative systems of care using this approach (increasing the role of generalist physicians or using alternative systems to obtain specialist advice, such as web conferencing),16 determining the optimal use of medications (RAAS inhibitors and beta-blockers) and refining the need for cardiac imaging in this cohort.

Conclusion Common Pathophysiological Processes and Personalised Medicine CVD causes 1.9 million deaths (40 % of all deaths) annually in the EU, and is estimated to cost the EU economy almost €196 billion a year.15 In the setting of the continuing burgeoning rise in both risk factors and

ang TJ, Larson MG, Levy D, et al. Plasma natriuretic peptide W levels and the risk of cardiovascular events and death. N Engl J Med 2004;350:655–63. DOI: 10.1056/NEJMoa031994; PMID: 14960742 Onodera M, Nakamura M, Tanaka F, et al. Plasma B-type natriuretic peptide is useful for cardiovascular risk assessment in community-based diabetes subjects: comparison with albuminuria. Int Heart J 2012;53:176–81. DOI: 10.1536/ihj.53.176; PMID: 22790686 Welsh P, Hart C, Papacosta O, et al. Prediction of cardiovascular disease risk by cardiac biomarkers in 2 United Kingdom cohort studies: does utility depend on risk thresholds for treatment? Hypertension 2016;67:309–15. DOI: 10.1161/HYPERTENSIONAHA.115.06501; PMID: 26667414 Jan A, Dawkins I, Murphy N, et al. Associates of an elevated natriuretic peptide level in stable heart failure patients: implications for targeted management. ScientificWorldJournal 2013;2013:562763. DOI: 10.1155/2013/562763; PMID: 24453873 Schmidt R, Schmidt H, Curb JD, et al. Early inflammation and dementia: a 25-year follow-up of the Honolulu-Asia aging study. Ann Neurol 2002;52:168–74. DOI: 10.1002/ana.10265; PMID: 12210786 Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med 2005;352:1685–95. DOI: 10.1056/ NEJMra043430; PMID: 15843671

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The use of NPs to identify individuals who would most benefit from heart failure prevention strategies holds great promise. By targeting care to those most at risk it provides a strategy for sustainable heart failure prevention models. This approach is now part of international guidelines and further studies to refine this model of care are ongoing. n

illeit P, Kaptoge S, Welsh P, et al. Natriuretic peptides and W integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis. Lancet Diabetes Endocrinol 2016;4:840–9. DOI: 10.1016/S2213-8587(16)30196-6; PMID: 27599814 8. Murphy AW, Cupples ME, Smith SM, et al. Effect of tailored practice and patient care plans on secondary prevention of heart disease in general practice: cluster randomised controlled trial. BMJ 2009;339:b4220. DOI: 10.1136/bmj.b4220; PMID: 19875426 9. Ledwidge MT, O’Connell E, Gallagher J, et al. Costeffectiveness of natriuretic peptide-based screening and collaborative care: a report from the STOP-HF (St Vincent’s screening to prevent heart failure) study. Eur J Heart Fail 2015;17:672–9. DOI: 10.1002/ejhf.286; PMID: 26139583 10. Moe, GW., Ezekowitz JA, O’Meara E, et al. The 2014 Canadian Cardiovascular Society Heart Failure Management Guidelines Focus Update: Anemia, Biomarkers, and Recent Therapeutic Trial Implications. Can J Cardiol;31:3–16 11. Yancy CW, Jessup M, Bozkurt B, et al. 2017 ACC/AHA/ HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure

12.

13.

14.

15.

16.

Society of America. J Card Fail 2017;23:628–51. DOI: 10.1016/ j.cardfail.2017.04.014; PMID: 28461259 Ledwidge M, Gallagher J, Conlon C, et al. Natriuretic peptidebased screening and collaborative care for heart failure: the STOP-HF randomized trial. JAMA 2013;310:66–74. DOI: 10.1001/ jama.2013.7588; PMID: 23821090 Huelsmann M, Neuhold S, Resl M, et al. PONTIAC (NT-proBNP selected prevention of cardiac events in a population of diabetic patients without a history of cardiac disease): a prospective randomized controlled trial. J Am Coll Cardiol 2013;62:1365–72. DOI: 10.1016/j.jacc.2013.05.069; PMID: 23810874 ARNI in asymptomatic patients with elevated natriuretic peptide and elevated left atrial volume index elevation (PARABLE). 2016. Available at https:// clinicaltrials.gov/ct2/show/NCT02682719. (Accessed: 20th October 2017) Nichols M, Townsend N, Luengo-Fernandez R, et al. European Cardiovascular Disease Statistics, 2012. European Heart Network: Brussels, and European Society of Cardiology: Sophia Antipolis. Gallagher J, James S, Keane C, et al. Heart failure virtual consultation: bridging the gap of heart failure care in the community – a mixed-methods evaluation. ESC Heart Fail 2017;4:252–8. DOI: 10.1002/ehf2.12163; PMID: 28772044

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Diagnosis

The Prognostic Role of Tissue Characterisation using Cardiovascular Magnetic Resonance in Heart Failure Robert D Adam, James Shambrook and Andrew S Flett Department of Cardiology, University Hospital Southampton, Southampton, UK

Abstract Despite significant advances in heart failure diagnostics and therapy, the prognosis remains poor, with one in three dying within a year of hospital admission. This is at least in part due to the difficulties in risk stratification and personalisation of therapy. The use of left ventricular systolic function as the main arbiter for entrance into clinical trials for drugs and advanced therapy, such as implantable defibrillators, grossly simplifies the complex heterogeneous nature of the syndrome. Cardiovascular magnetic resonance offers a wealth of data to aid in diagnosis and prognostication. The advent of novel cardiovascular magnetic resonance mapping techniques allows us to glimpse some of the pathophysiological mechanisms underpinning heart failure. We review the growing prognostic evidence base using these techniques.

Keywords Cardiac magnetic resonance, heart failure, late gadolinium enhancement, T1 mapping, extracellular volume, T2 techniques, prognosis, response to therapy, cardiac resynchronisation therapy, implantable cardioverter-defibrillator Disclosure: The authors have no conflicts of interest to declare. Acknowledgments: The authors would like to acknowledge Dr Rohin Francis for granting permission to use his ‘Pop heArt’ image from the 2017 Society for Cardiovascular Magnetic Resonance paper as the accompanying image for the electronic version of the article. Received: 3 October 2017 Accepted 31 October 2017 Citation: Cardiac Failure Review 2017;3(2):86–96. DOI: 10.15420/cfr.2017:19:1 Correspondence: Dr Andrew Flett, University Hospital Southampton, Tremona Road, Southampton SO16 6YD, UK. E: Andrew.Flett@uhs.nhs.uk

The current lifetime risk of heart failure is approximately one in three and the prognosis remains poor.1 The latest UK heart failure audit data reveal inpatient and 1-year mortality rates of 10 % and 27 %, respectively.2 The aetiology of heart failure is diverse, and many patients have several cardiac and non-cardiac pathologies that conspire to cause the syndrome. Currently the most common cause is ischaemic heart disease, but many patients have comorbidities such as hypertension, diabetes mellitus, atrial fibrillation and renal insufficiency. 3–7 The identification of heart failure aetiology is crucial as this may guide subsequent decisions regarding treatment strategies such as suitability for specific evidence-based pharmacological and device therapies. Current National Institute for Health and Care Excellence and international guidelines recommend utilising blood tests (B-type natriuretic peptide), ECG and various cardiac imaging modalities to confirm clinical suspicions of a heart failure diagnosis and investigate the underlying cause of the condition.8–10 Cardiovascular magnetic resonance (CMR) has become a gold standard non-invasive test in heart failure due to its unparalleled capability to not only accurately assess cardiac anatomy and function with excellent reproducibility, but also its unique ability to identify specific pathological tissue characteristics that may be diagnostic of the underlying disease process.11 In the past 2 decades there has been a rapid evolution of contrast-enhanced techniques and subsequent increases in their utilisation in clinical practice and heart failure research. This has led to a large prognostic evidence base. CMR is now recommended as the first-line imaging investigation in the

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current National Institute for Health and Care Excellence,8 European9 and North American10 heart failure guidelines when transthoracic echocardiographic windows are poor or there is diagnostic uncertainly regarding aetiology. In this article, we will review these prognostic studies, concentrating on how CMR tissue characterisation can guide prognosis in heart failure.

Late Gadolinium Enhancement Normal myocardium, blood and other tissues have specific T1 times (the time taken for longitudinal recovery of magnetisation after a radiofrequency pulse inverts the net polarisation of atoms in the body). T1-weighted images detect differences in these times, giving an intrinsic contrast between the myocardium and surrounding tissues. Late gadolinium enhancement (LGE) CMR is based upon the principle that gadolinium (Gd) – bound to diethylenetriaminepentaacetic acid or DTPA – is excluded from the intracellular compartment but has free distribution in the extracellular space. It dramatically shortens T1 time and gives extrinsic contrast to the tissue in which it resides. In normal myocardium, where the extracellular space is small, Gd rapidly washes in and out of the tissue and so the T1 is relatively preserved. When healthy myocardium is replaced by fibrosis or “scar” tissue, the extracellular space increases yielding a larger volume of distribution for Gd and a delayed wash out. These scar areas appear brighter on T1-weighted imaging compared to healthy myocardium and the scar burden identified using this technique is well validated against histological assessment.12,13 The wealth of data around LGE and its critical prognostic importance in the whole spectrum of cardiac disease is extensively reviewed elsewhere.14–22 Here, we highlight a few issues that are of specific importance to the heart failure population.

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The Prognostic Role of CMR Tissue Characterisation in HF Heart Failure Aetiology Pathologists have known for decades that disease processes affect the myocardium in specific patterns. LGE CMR allows us to visualise these processes in vivo and identify differentiating and recognisable patterns of scarring,23,24 making LGE CMR a very powerful tool for assessing the aetiology of heart disease, see Figure 1. This is particularly true for ischaemic cardiomyopathy (ICM) versus nonischaemic cardiomyopathy (NICM).

Figure 1: Late Gadolinium Enhancement Patterns in Ischemic and Non-ischaemic Cardiomyopathies Ischaemic

Non-ischaemic

Early studies26 found that >10 % of heart failure patients labelled with non-ischaemic dilated cardiomyopathy had LGE patterns consistent with previous myocardial infarction. This has since been confirmed in other studies and long-term follow-up highlights the prognostic importance of making this distinction, as patients with ischaemic scarring have been shown to be almost twice as likely to have a cardiac death or heart failure hospitalisation compared to those with non-ischaemic scarring.27 The presence of scarring in any condition seems to confer an adverse prognosis.28 This is reiterated in a recent study of 670 patients with myocarditis followed for up to 7 years. The presence of LGE was associated with double the risk of major adverse cardiac events, with patchy LGE conferring the worst outcome (HR 2.9; CI 1.8–4.8) Septal and midwall patterns were also more strongly predictive of major adverse cardiac events (HR 2.6; CI 1.8–3.8).29 Conversely, the absence of LGE conferred an excellent prognosis, with an annual risk of death of just 0.3 %.

C Mid-wall HE

A Subendocardial Infarct

• Idiopathic Dilated • Hypertrophic • Sarcoidosis, Cardiomyopathy Cardiomyopathy myocarditis, • Right ventricular Anderson–Fabry, • Myocarditis pressure overload Chagas disease (e.g.congenital heart disease, pulmonary hypertension)

D Epicardial HE B Transmural Infarct

• Sarcoidosis, myocarditis, Anderson–Fabry, Chagas disease

E Global Endocardial HE

Response to Medical Therapy

Response to Cardiac Resynchronisation Therapy Guideline-directed cardiac resynchronisation therapy (CRT) leads to a significant improvement in quality of life33,34 and a reduction in heart failure hospitalisation35,36 and mortality.37 Despite this, approximately 30 % of these patients gain no prognostic advantage and many do not experience these improvements in symptoms and might be considered non-responders.38 Studies using CMR to investigate the potential role of LV scar tissue in this phenomenon not only found a significant correlation between increased LGE burden and non-response to CRT, but also suggested a dose–response-type relationship that predicted this outcome.39,40 The greater the scar burden, the less likely patients are to respond. The quality of the scar is also important: patients with partial-thickness and, to a greater extent, full-thickness posterolateral LGE, have a lesser response to CRT and are at significantly greater risk of cardiovascular death and heart failure rehospitalisation than those without scarring in this region, see Figure 3.41,42 The potential to utilise CMR to guide optimal response to CRT was subsequently utilised in a study (n=559) where patients who had their LV lead deployed over

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• Amyloidosis, systemic sclerosis, post-cardiac transplantation

(A–B) Ischaemic cardiomyopathy always involves the subendocardium in a coronary artery distribution. (C–E) Late gadolinium enhancement in non-ischaemic cardiomyopathies tends to spare the subendocardium, typically affecting the midwall or epicardium across multiple coronary artery territories. HE = hyper enhancement. Reproduced with permission from Mahrholdt et al, 2005.25

Figure 2: Influence of Late Gadolinium Enhancement (LGE) on Left Ventricular Remodelling in Patients Treated with Optimal Medical Therapy Left ventricular ejection fraction (%)

Although various pharmaceutical agents have been shown to improve the prognosis of heart failure, how an individual patient will respond to these medications can vary dramatically and there remains no accurate method of predicting who will benefit from these agents. LGE has shown potential as a CMR biomarker for non-response to first-line medical therapies in heart failure. Patients with both ICM and NICM who have severely impaired systolic function are significantly less likely to have improved left ventricular (LV) function following 6 months of beta-blocker therapy if scarring is identified on CMR.30 Similar observations have been seen in NICM patients with LGE who have received optimal medical therapy, see Figure 2.31,32 Patients with scarring do not respond to medical therapy to the same extent as those without scar tissue.

60 p<0.001

40 p=0.9 30

20 Baseline Baseline LGE negative

Follow-up Baseline LGE positive

Reproduced with permission from Leong et al, 2010.32

areas of myocardium containing scar tissue had significantly higher risk death or hospitalisation than those who were paced away from scar tissue (HR 5.57; CI 3.40–9.14; p≤0.0001), see Figure 4.43

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Diagnosis Figure 3: Kaplan–Meier Estimates of the Time to Combined Endpoint (Cardiovascular Death and Heart Failure Hospitalisation) in Cardiac Resynchronisation Therapy Patients Related to the Presence and Thickness of Posterolateral (PL) scar 1 Non-PL scar

Survival

0.8 Non-transmural PL scar

0.6 0.4 0.2

Failure on Mortality (DANISH), which included 1,116 participants.48 In this very well conducted study, patients with a NICM and EF <35 % were randomised to ICD or no ICD and followed up for a mean period of 68 months. There was no difference in the primary outcome of death from any cause, although there was a reduction in sudden death. There was no CMR in this study and critics have argued that this may have shed light on those patients who could benefit from ICD. This does more to highlight the inadequacies of EF as a gatekeeper to ICDs and has been the driving force behind efforts to identify novel biomarkers, including CMR indices that accurately identify patients at increased risk of SCD. As scar tissue is a focus for the development of malignant ventricular arrhythmias in both ICM49 and NICM50, its detection and quantification by LGE has been strongly associated with arrythmogenic events in patients who fall both within and, more interestingly, outside the current ICD implantation guidelines.51

Transmural PL scar 0.0

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400

600

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Figure 4: Clinical Outcome of Cardiac Resynchronisation Therapy According to Implantation Strategy

Klem et al. performed CMR in 137 individuals (half with reduced EF, half preserved EF and split approximately half ICM versus NICM) prior to implantation of an ICD. They found that, in contrast to the gradual increase in adverse incidents that was observed with worsening LVEF, there was a sharp step-up in death or ICD discharge with even a small amount of scar tissue (>5 % of the LV mass; HR 5.2; CI 2.0–13.3; p=0.0006).53 Other studies have confirmed this finding of a threshold

0.8

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Time (days) +CMR-S = group with cardiovascular magnetic resonance (CMR) showing no scar at the left ventricular pacing position; +CMR+S = group with CMR showing scarring at the left ventricular pacing position; -CMR = non-CMR-guided group. Reproduced from Leyva et al, 2011.43

Advances in post processing techniques now allow tissue characterisation images to be fused with 3D whole-heart images that accurately depict the coronary sinus anatomy in relation to areas of scar tissue using data from the same CMR scan.44 This technology has the potential to allow pre-emptive prediction of whether patients will benefit from CRT implantation based on their individual anatomy.

Implantable Cardioverter-Defibrillator Implantation Reduced left ventricular systolic function (LVEF ≤35 %) remains the sole arbiter in international guidelines for implantable cardioverterdefibrillator (ICD) therapy in heart failure patients.9,10 Paradoxically, 70–80 % of those who suffer sudden cardiac death (SCD) have an ejection fraction (EF) of >35 %45,46 and up to half of patients who currently receive an ICD never use it.47 The field has recently become even more controversial following the recent Danish Study to Assess the Efficacy of ICDs in Patients with Non-ischemic Systolic Heart

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Following the results of DANISH, a recent study including 253 individuals set out to determine whether CRT with a defibrillator is superior to CRT with a pacemaker in NICM patients with or without focal LV midwall fibrosis (MWF) detected by LGE.52 As expected, MWF conferred an adverse prognosis, with a 3.75-fold higher risk of SCD. Only patients with MWF gained a benefit from a defibrillator over a pacemaker in terms of outcome, suggesting that scar detection on CMR could be an invaluable tool in guiding appropriate device therapy.

effect of scar burden conferring a strong adverse prognostic effect.54–59 Patients with preserved EF and scarring on CMR have a similar event rate to those with EF <30 %; while patients with EF <30 % but no scar have a similar event rate to those with preserved EF, see Figure 5. This observation eloquently demonstrates that an EF threshold used to arbitrate ICD prescription will inevitably lead to unused implants in many patients with low EF and overlooked arrhythmic deaths in many patients with preserved EF. A more recent study including 399 participants with a median LVEF of 50 % followed up for a median of 54 months found a similar phenomenon in patients with NICM and preserved LV function. Midwall LGE was a significant independent predictor of SCD (HR 4.8; CI 1.7–13.8; p=0.003) and aborted SCD (HR 35.9; CI 4.8–271.4; p<0.001), independent of LVEF, see Figure 6.60 Scar heterogeneity has also been shown to have a significant influence on the development of ventricular arrhythmias. Border zone (BZ) scar, defined as areas with a LGE signal intensity of <50 % of the infarct core zone (CZ), is well known to confer a more significant risk of developing arrythmogenic endpoints than total scar burden alone.61–63 More recently Acosta et al. used 3D colour-coded LGE signal-intensity maps to accurately quantify total scar mass, CZ mass, BZ mass and BZ channel mass (defining a corridor of BZ connecting two areas of normal myocardium flowing between two CZs or between a CZ and a valve annulus) in 219 patients undergoing CRT implantation (defibrillator 71 %).64 Patients had a mean LVEF of 26 % and were followed up for a median of 36 months. Although all scar parameters

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The Prognostic Role of CMR Tissue Characterisation in HF Figure 5: Implantable Cardioverter-Defibrillator Discharge and Sudden Cardiac Death Rates According to Ejection Fraction and Scar Burden 50

Event rate (%)

Scar >5 %, LVEF ≤30 % (3-yr event rate 35 %) 30

Entire group, LVEF >30 % (3-yr event rate 17 %)

Scar >5 %, LVEF >30 % (3-yr event rate 27 %)

Entire group, LVEF ≤30 % (3-yr event rate 27 %)

Scar ≤5 %, LVEF >30 % (3-yr event rate 6 %)

Scar ≤5 %, LVEF ≤30 % (3-yr event rate 11 %)

0 0

Months

LVEF = left ventricular ejection fraction. Adapted with permission from Klem et al(53)

It therefore seems that quantification and characterisation of scar tissue using LGE has a significant influence on adverse outcomes related to arrhythmias over and above that yielded from EF. This suggests that these scar parameters may be a better way to identify patients who would benefit from implantation of an ICD rather than the single parameter of EF. These fascinating observations have provided the impetus for eagerly anticipated randomised control trials such as Defibrillators To Reduce Risk by Magnetic ResoNance Imaging Evaluation (DETERMINE, NCT00487279) and Cardiac Magnetic Resonance GUIDEd Management of Mild-moderate Left Ventricular Systolic Dysfunction (CMR-Guide, NCT01918215), which aim to establish whether ICD therapy improves the prognosis of patients with mild-to-moderate LV systolic dysfunction (LVEF >35–50 %) and LGE with ICM and NICM, respectively. The LGE technique has provided new pathophysiological insights across the spectrum of cardiovascular disease and is probably the single most important development since CMR became a clinical tool in the last millennium. It has certainly been the driving force behind the huge expansion in the utilisation of the technique over the past 2 decades. However, LGE does have some important limitations. Despite LGE being highly accurate at identifying focal fibrosis, its reliance on relative signal intensity differences and the intentional nulling of “normal” myocardium means it cannot be utilised to identify diffuse myocardial fibrosis (DMF). A heart could be 50 % scar tissue, but if it is equally distributed throughout the myocardium the LGE technique would belie this fundamental pathology and the clinical report may simply read “no scar”. This problem also means that you cannot use the signal intensity from one scan as a comparator against another – either within the same patient over time or across different patients. In addition, one can only see the “tip of the iceberg”, as a threshold

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Figure 6: Five-year Risk Estimates for Primary Sudden Cardiac Death and Aborted Sudden Cardiac Death Based on Left Ventricular Ejection Fraction Alone and Midwall Late Gadolinium Enhancement Status in Addition to Left Ventricular Ejection Fraction 0.5

Observed

Model predicted Overall LGE

0.4

5 Year risk

correlated with a significantly increased risk of ICD therapy or SCD, BZ mass (HR 1.06; CI 1.04–1.08) and BZ channel mass (HR 1.2; CI 1.10– 1.32) were the strongest predictors of these endpoints. Interestingly, regardless of aetiology, patients with scar tissue but without BZ channels did not receive any ICD therapy or experience SCD (100 % negative predictive value). LVEF was not identified as an independent predictor of the primary endpoint.

No LGE

0.3

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LVEF (%) Green line = left ventricular ejection fraction (LVEF) alone; purple line = midwall late gadolinium enhancement status in addition to LVEF in the presence of late gadolinium enhancement; blue line = midwall late gadolinium enhancement status in addition to LVEF in the absence of late gadolinium enhancement. Reproduced with permission from Halliday et al, 2017.60

of fibrosis (perhaps as much as 15 %) needs to be crossed before it is detectable.65 Finally, within an area of “scarring” there are islands of intact myocytes and within areas of “normal” myocardium there may be microscopic pockets of fibrosis. These limitations, together with a lack of consensus regarding the optimal method for LGE quantification,66,67 have led to the development of T1 mapping sequences that have the potential to overcome many of these limitations.

T1 Mapping Essentially, the method uses a robust imaging sequence to quantify the T1 time; the readout of this being a colour image. The colour of each pixel represents the T1 time in ms on a scale. The T1 of

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Diagnosis Figure 7: Diagnostic Utility of Tissue Characterisation using Native T1 and Extracellular Volume Fraction (Based on MOLLI at 1.5T)

Figure 8: Multiparametric Cardiovascular Magnetic Resonance Tissue Characterisation

>1200 msec

(1) Native T1

(2) LGE

(3) ECV

Acute MI

NativeT1 values (milliseconds)

ATTR amyloidosis

HCM DCM RA

Acute myocarditis TC Syst. Sclerosis

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A. Acute MI

Normal

B. Chronic MI Fabry

<900 msec

Iron Lipomatous metaplasia Fat

C. NICM

100 %

tissues is mostly influenced by their water and fat content. Fatty tissues have a very low T1 and are represented in one colour; tissues containing water, which is found in scars containing collagen for example, have a long T1 represented in another colour. The technique is so sensitive that it has been able to detect a difference between healthy males and healthy females of around 20 ms. 68 This is a completely novel and largely unexplained phenomenon. With this sensitivity, images taken in one patient can be used to directly track disease progression/regression over time (provided that the disease trajectory affects the water/fat content of the myocardium in one direction or the other). T1 maps acquired before and after Gd enable us to non-invasively derive the extracellular volume (ECV) in a highly reproducible manner that has been well validated against histological analysis. 69–71 Colour maps can be generated that display each pixel as the ECV expressed as a percentage (normal being approximately 20 %). In most diseases, elevations in ECV represent collagen/scar (and correspondingly DMF). T1-mapping techniques allow this to be seen within areas of infarction/non-ischaemic scar and also in remote/”normal” myocardium. In cardiac amyloid the ECV represents the protein burden in the tissue, and although this is an incredibly important parameter in prognostication72,73 it is beyond the scope of this article and is reviewed elsewhere.74 There are several excellent and very recent review articles that very eloquently describe the physics and the strengths of this new method as well as some of the ongoing controversies and difficulties it faces.75–78 T1-mapping techniques are still an area of avid research but we highlight here how they are already finding utility in diagnostics and prognostication with reference to heart failure.

Heart Failure Aetiology The ability of T1-mapping techniques to determine DMF in vivo has greatly augmented the diagnostic capabilities of CMR and provided new insight into the pathophysiological processes that ultimately culminate in the development of heart failure in a wide range of diseases, see Figures 7 and 8.79,80 Automated post processing software packages81

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Non-Ischaemic

Extra cellular volume (ECV) AL = amyloid light-chain; ATTR = transthyretin-related; DCM = dilated cardiomyopathy; HCM = hypertrophic cardiomyopathy; MI = myocardial infarction; RA = rheumatoid arthritis; Syst. Sclerosis = systemic sclerosis; TC = Takotsubo cardiomyopathy. Reproduced from Haff et al, 2016.

D. HFpEF

E. Myocarditis

F. Amyloidosis 200 ms

1600 ms

100 %

(A) Area of microvascular obstruction (yellow arrow, A2 and A3). (B) Lipomatous metaplasia transformation in previous anteroseptal infarct (green arrow, B1). (C) Non-ischaemic cardiomyopathy with no LGE (C2) but raised native T1 in the septum (C1) and raised ECV (C3). (D) Native T1 values were significantly raised throughout (>1,000 ms), with no LGE (D2). (D3) ECV maps demonstrated patchy rise in extra-cellular space. (E) Higher native T1 values in inferolateral wall (E1), consistent with LGE in the same region (E2, yellow arrow). (E3) ECV map demonstrates diffusely increased extracellular space. (F1) Diffuse rise in native T1. (F3) Diffuse rise in extracellular space in the whole myocardium. ECV = extracellular volume; HFpEF = heart failure with preserved ejection fraction; LGE = late gadolinium enhancement; MI = myocardial infarction. Adapted from Haff et al, 2016.80

and synthetic ECV calculation methods82 have swiftly transitioned these imaging sequences from theoretical research tools to standardised protocols used in some centres in everyday clinical practice. Some caution is advised in their routine application: the absolute values for native T1 depend greatly on field strength, pulse sequence, scanner manufacturer and the rules of measurement.83 Before clinical implementation, local reference ranges should be established.

Adverse Outcomes The past 5 years have seen a number of large studies investigating the prognostic role of T1 mapping in both all-comers referred for CMR, see Table 1, and in patients with heart failure, see Table 2. Although the majority of this research (which includes over 6,000 patients and nearly 9,000 patient years of follow-up) is prospective in design, potentially leading to selection bias in the patient cohort,

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The Prognostic Role of CMR Tissue Characterisation in HF Figure 9: Association of Cardiovascular Magnetic Resonance Parameters with All-cause Mortality 1.00

1.00

0.95

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Survival

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≥2SD (abnormal)

LVEF >35 % ≤35 %)

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Chi-squared 19.0 (p<0.001), HR 5.2 (2.4–14.6)

Chi-squared 2.7 (p=0.14), HR 3.1 (0.6–12.1) 0.85

0.80 0

Time (Months)

LVEF = left ventricular ejection fraction; SD = standard deviation. Adapted with permission from Puntman et al, 2016.92

Figure 10: Association between Extracellular Volume and Clinical Outcome per Left Ventricular Ejection Fraction Ejection fraction <45 % 0.8

40 % ≤ECV 35 % ≤ECV<40 % 30 % ≤ECV<35 % 25 % ≤ECV<30 %

0.6

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Probability of HHF or Death (n=52)

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CMR = cardiovascular magnetic resonance; ECV = extracellular volume; HHF = hospitalisation for heart failure. Adapted from Schelbert et al, 2015.86

the fascinating and consistent finding is that T1 and ECV are independently associated with poor outcome in a dose-dependent fashion, independently of EF, focal scar burden (LGE) or aetiology. The ability to accurately quantify DMF using these techniques allows the relative risk of an adverse event to be estimated per unit change in these parameters. Schelbert et al. found that a 3 % rise in ECV in heart failure with preserved EF patients with both ICM and NICM (n=160, median LVEF of 62 % and mean follow-up of 1.9 years) correlated with a three-fold increase in risk of death or heart failure hospitalisation.84 Puntman et al., who followed 637 patients with a median LVEF of 47 % for an average of 1.8 years, found that a 10-ms increase in native T1 time is associated with a 10 % increased risk of the same endpoints in patients with NICM.85 Consider how tiny this difference is, as 10 ms is half the difference identified between healthy males and females. Yet

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this parameter is a more sensitive biomarker of adverse events than LVEF (HR 5.2 versus 3.1), see Figure 9. Indeed, the prognostic predictive value of these methods, over and above LVEF – the current benchmark in heart failure – was elegantly demonstrated by Schelbert et al.86 The authors studied 1,172 individuals for a mean 1.7 years and found that ECV was significantly associated with an increased risk of heart failure hospitalisation and death in a univariate Cox regression model (p<0.05 for all) whether LVEF was reduced (<45 %) or preserved (>45 %), see Figure 10.86 The graph shows a dose–response relationship, hinting at an aetiological link between ECV expansion and adverse outcome. The study by Wong et al.95 including 1,176 participants followed up for a median of 1.3 years found that ECV was significantly higher in patients

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Diagnosis Table 1: T1 Mapping Studies in All-comers for Cardiovascular Magnetic Resonance (Extracellular Volume is used as the Parameter for All) Follow-up

LVEF (%)

Study

Number

Exclusions

(Years) Mean

Mean ± SD or

Endpoints

Findings

Wong et al, 793 Hypertrophic 0.8 58 (46–64) 201284 cardiomyopathy Amyloid

or Median

Median (IQR) 1˚ death 2˚ death, cardiac transplant, left ventricular assist device

1˚ HR 1.55; CI 1.27–1.88 2˚ HR 1.48; CI 1.23–1.78 (per 3 % increase in ECV)

Ghassan Ghosn 1247 Nil 1.4 58±18 et al, 201585

Death and/or cardiovascular hospitalisation

HR 1.10; p≤0.010

Schelbert 1172 Hypertrophic 1.7 * 1˚ death cardiomyopathy 2˚ HFH et al, 201586 Amyloid 3˚ death and HFH Adult congenital heart disease Takotsubo

1˚ HR 1.86; CI 1.45–2.40 2˚ HR 1.77; CI 1.32–2.36 3˚ HR 1.85; CI 1.50–2.27 (per 5 % increase in ECV)

Kammerlander 473 Hypertrophic 1.1 62±12 cardiomyopathy et al, 201687 Amyloid Anderson– Fabry disease

HR 1.11; CI 1.05–1.17

Cardiovascular death or cardiovascular hospitalisation

*Left ventricular ejection fraction was not reported for the entire cohort. IQR = interquartile range; LVEF = left ventricular ejection fraction; SD = standard deviation.

Table 2: T1 Mapping Studies in Heart Failure Populations Study

Aetiology

Follow-up

LVEF (%)

(Number)

(Years) Mean

Mean ± SD or

or Median

Median (IQR)

Parameter

Mascherbauer et al, NICM¶ (61) 1.9 64±11 Post-contrast T1 201488 time (ms)

Endpoints

Findings

CV death or HFH

HR 0.99; CI 0.98–0.99

NICM (89) 2.0 41±13 ECV CV death, HFH, sustained Barison et al, 201589 ventricular tachycardia/ fibrillation Duca et al, 201690 NICMß (117)

2.0

63±10

ECV

CV death or HFH

Bivariate Cox analysis: p≤0.05 HR 1.13; CI 1.05–1.22

0.75 NR ECV CV death and/or CV Duca et al, 201691 NICM§ (73) hospitalisation

HR 1.04; CI 1.02–1.07

Puntmann et al, NICM (637) 1.8 47 (29–50) Pre-contrast T1 1˚ death 2˚ heart failure death 201692 time (ms) and HFH

1˚ HR 1.1; CI 1.06–1.15 2˚ HR 1.1; CI 1.01–1.10 (per 10 ms increase in T1)

NICM (117) 0.9 25±8 ECV CV death, HFH, cardiac HR 1.80; CI 1.48–2.20 Youn et al, 201793 transplant (per 3 % increase in ECV) Schelbert et al, 201794

Ischaemic 1.9 62 (56–67) ECV Death or HFH cardiomyopathy/ NICM† (21/139)

HR 3.19; CI 1.55–6.54 (per 5 % increase in ECV)

¶Patients

with cardiac amyloid and sarcoidosis were excluded; ßpatients with cardiac amyloid were excluded; §30.1 % of patients had a diagnosis of cardiac amyloid; †patients with hypertrophic cardiomyopathy, Anderson–Fabry disease, Takotsubo cardiomyopathy and hemochromatosis were excluded. Study also included 745 patients without heart failure. CV = cardiovascular; ECV = extracellular volume; HFH = heart failure hospitalisation; LVEF = left ventricular ejection fraction; NICM = non-ischaemic cardiomyopathy; NR = not reported; SD = standard deviation.

with diabetes than in those without (30.2 % [interquartile range 26.9– 32.7 %] versus 28.1 % [interquartile range 25.9–31.0 %], respectively; p≤0.001). This again highlights the technique’s exceptional sensitivity. Moreover, within the population, a 3 % rise in ECV correlated with a 52 % increased risk of death and/or heart failure hospitalisation (HR 1.52; 95 % CI 1.21–1.89).95

some patients with apparent severe heart failure go on to have a good prognosis and recovery of LV function. Indeed, an early study investigating the impact of treating post myocardial infarction patients with Omega-3 fatty acids (n=358) successfully used T1 mapping to accurately quantify and monitor DMF,100 highlighting this as a potentially viable method of establishing patients on optimal medical therapy based on their histological parameters.

Response to Medical Therapy Invasive myocardial tissue sampling has shown that DMF can be reversed by a number of first-line heart failure medications (lisinopril,96 perindopril,97 losartan98 and spironolactone99). Since T1 mapping is sensitive to these changes, it has the potential to be the first accurate non-invasive method of monitoring reduction in DMF as a response to medical therapy. If this is observed, it may help to explain why

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One in 10 patients treated with anthracycline chemotherapy develop cardiotoxicity, 101 which has been shown to have a significant prognostic implication.102 As early diagnosis and treatment of this complication with routine pharmaceutical therapy for heart failure reverses adverse remodelling and reduces subsequent cardiovascular adverse events,101,103 the identification of individuals who are at

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The Prognostic Role of CMR Tissue Characterisation in HF Figure 11: Number of Deaths of Patients with Thalassaemia Major in the UK

Table 3: Clinical Utility of Cardiovascular Magnetic Resonance Techniques Ordered by Pathophysiological Mechanism and Tissue Characteristics

50 Pathological Process/

Aetiology

Number of deaths

40 35 30 25 20 15 10 5 0 1950– 1955– 1960– 1965– 1970– 1975– 1980– 1985– 1990– 1995– 2000– 1954 1959 1964 1969 1974 1979 1984 1989 1994 1999 2003

Unknown Inflection

Other

Malignancy

BMT complication

Anaemia

increased risk of this complication is of critical importance. A recent study104 identified patients previously treated with anthracycline chemotherapy as having significantly increased ECV compared with controls, suggesting that T1-mapping techniques may have a role to play as novel risk stratification biomarkers prior to and during treatment with anthracycline agents. Indeed, ongoing clinical trials in this area (NCT01719562) aim to provide the evidence required to increase access to CMR screening before chemotherapy, which has been identified as one of the major factors limiting its use as a diagnostic imaging tool in this cohort.105

Response to Cardiac Resynchronisation Therapy The only study to date that has investigated the potential correlation between contrast-enhanced CMR-derived myocardial depressant factor and response to CRT showed that ECV calculated from a single region of interest in the septum of 48 patients (28 with ICM and 21 with NIDCM) correlated with response to biventricular pacing, but this effect did not hold up on multivariate analysis.106 As studies using multi-segmental analysis of the whole myocardium have identified that regional variation in ECV is significantly related to intra-ventricular dyssynchrony irrespective of EF,107 further clinical trials utilising wholeheart T1 maps to further explore this relationship are warranted.

Implantable Cardioverter-Defibrillator Implantations DMF has been shown to be pro-arrythmogenic due to its disruptive effect on electrical propagation between myocytes. 108,109 DMF identification and quantification using T1 mapping therefore has potential as a risk biomarker for malignant arrhythmias and subsequent ICD implantation in heart failure patients. One single-centre study followed-up 138 patients (71 with ICM and 59 with NICM) who underwent CMR prior to ICD implantation for a mean of 1.2 years. It was found that native T1 times were an independent predictor of appropriate ICD therapy and ventricular arrhythmias according to univariate (HR 1.06; CI 1.01–1.11; p=0.021 per 10 ms

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Tissue Characterisation Technique

LGE

Native T1

ECV

Infiltrative Iron Amyloid Anderson– Fabry disease

- + +

+ ++ ++

? + ++ ++ ? - + -

T2*

Acute Oedema + ++ Myocardial Necrosis ++ ++ Injury Haemorrhage ++ +

+ ++ ? ++ + ++ ? + ++

Fibrosis

++ ? ++ - -

Diffuse/global* - Focal/regional* ++

+ +

++ = Useful; + = potentially useful; ? = unknown; − = not useful. *Diffuse/global refers to findings affecting most the myocardium. Focal/regional refers to localised findings, including patchy abnormalities. ECV = extracellular volume; LGE = late gadolinium enhancement. Adapted from Messroghli et al, 2017.78

Iron overload

Iron overload replaced anaemia as the most common cause of death after 1970, when adequate transfusion schemes became commonplace. Iron chelation therapy by subcutaneous infusion of deferoxamine became standard practice after 1980. T2* cardiovascular magnetic resonance was introduced in the UK in 1999. BMT = bone marrow transplantation. Reproduced from Modell et al, 2008.124

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Cardiovascular Magnetic Resonance

increase in T1 time) and multivariate (HR 1.10; CI 1.04–1.16; p=0.01 per 10 ms increase in T1 time) analysis.110 Further research in this area is clearly needed.

T2 Techniques T2-weighted imaging allows the visualisation of myocardial oedema due to the accumulation of water prolonging the T2 relaxation time; it enables the decay of transverse magnetisation to be measured. While “conventional” T2 sequences such as short tau inversion recovery (T2 STIR) can generate images that enable qualitative assessment of myocardial oedema in a variety of acute cardiomyopathies,111 T2-mapping techniques allow the extent of these inflammatory changes to be accuracy quantified in a similar way to T1 maps.112,113 Indeed, there is a growing body of evidence highlighting the diagnostic role of T2 mapping in acute cardiac conditions such as myocardial infarction,114 myocarditis,115,116 stress-induced (Takotsubo) cardiomyopathy117,118 and acute transplant rejection.119,120 However, to date no prognostic evidence derived from these techniques has been reported in the heart failure population and further research in this area of CMR is warranted. T2-Star (T2*) is a relaxation parameter that inversely relates to the iron stores in any tissue and allows accurate, reproducible quantification of myocardial iron deposition.121,122 Since its introduction into clinical practice in 1999, this CMR technique has been successfully utilised as a screening tool in transfusion-dependent beta thalassemia patients to identify those with increased cardiac iron deposition. These patients are at significantly increased risk of cardiac complications such as heart failure and malignant arrhythmias.123 Early initiation of chelation therapy in these individuals has had an extraordinary impact on the long-term prognosis of this condition, with a 71 % reduction in the annual death rate from iron overload since 2000, see Figure 11.124 Interestingly, T1 mapping has potential in this field, since myocardial iron also shortens intrinsic T1 times. In early studies, T1 mapping appears to be more sensitive than T2 measurement,125,126 although its role in guiding therapy is yet to be established.

Conclusion CMR is firmly established as the gold standard diagnostic imaging investigation in heart failure due to its unparalleled capability to accurately assess cardiac function and anatomy with excellent

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Diagnosis reproducibility and its novel ability to non-invasively identify specific aetiological processes, see Table 3. There is a growing body of prognostic evidence using tissue characterisation techniques that suggests real potential as a viable risk-stratification biomarker and a precise means of tracking response to therapy. However, the techniques are continuing to evolve and undergo further refinement as summarised by the Society for Cardiovascular Magnetic Resonance in their recentlypublished second consensus statement regarding CMR parametric mapping.78 We currently lack the wealth of data required for them to become routine clinical tools. We need a clearer understanding of the interrelation between native T1 and ECV. How do disease processes exert differential influence and what is the impact of

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that? The ongoing issue of variation in quantification methods must be solved using the standardisation of normalised reference ranges between vendors. Efforts are already underway to achieve this.127 With randomised trials, such as DETERMINE and CMR-GUIDE underway and a further 37 T1-mapping studies currently registered on www.clinicaltrials.gov, the evidence required to address these and other issues are in the pipeline. The reliance on EF as the sole arbiter for advanced therapies such as defibrillator implantation must end. As we move into the next decade, precision medicine, which is already finding application in several specialities, such as oncology, endocrinology and microbiology, will become more commonplace. CMR tissue characterisation techniques continue to mature and it seems likely they will play a leading role in the development of precision heart failure care. n

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JACC Cardiovasc Imaging 2013;6:944–54. DOI: 10.1016/j.jcmg.2013.05.013; PMID: 23932642. Halliday BP, Gulati A, Ali A, et al. Association between midwall late gadolinium enhancement and sudden cardiac death in patients with dilated cardiomyopathy and mild and moderate left ventricular systolic dysfunction. Circulation 2017;135:2106– 15. DOI: 10.1161/CIRCULATIONAHA.116.026910; PMID: 28351901. Schmidt A, Azevedo CF, Cheng A, et al. Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction. Circulation 2007;115:2006–14. DOI: 10.1161/CIRCULATIONAHA.106.653568; PMID: 17389270. Roes SD, Borleffs CJ, van der Geest RJ, et al. Infarct tissue heterogeneity assessed with contrast-enhanced MRI predicts spontaneous ventricular arrhythmia in patients with ischemic cardiomyopathy and implantable cardioverterdefibrillator. Circ Cardiovasc Imaging 2009;2:183–90. DOI: 10.1161/ CIRCIMAGING.108.826529; PMID: 19808591. Heidary S, Patel H, Chung J, et al. Quantitative tissue characterization of infarct core and border zone in patients with ischemic cardiomyopathy by magnetic resonance is associated with future cardiovascular events. J Am Coll Cardiol 2010;55:2762–8. DOI: 10.1016/j.jacc.2010.01.052; PMID: 20538171. Acosta J, Fernández-Armenta J, Borràs R, et al. Scar characterization to predict life-threatening arrhythmic events and sudden cardiac death in patients with cardiac resynchronization therapy: The GAUDI-CRT Study. JACC Cardiovasc Imaging 2017. DOI: 10.1016/j.jcmg.2017.04.021; PMID: 28780194; epub ahead of print. Moon JC, Reed E, Sheppard MN, et al. The histologic basis of late gadolinium enhancement cardiovascular magnetic resonance in hypertrophic cardiomyopathy. J Am Coll Cardiol 2004;43:2260–4. DOI: 10.1016/j.jacc.2004.03.035; PMID: 15193690. Flett AS, Hasleton J, Cook C, et al. Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance. JACC Cardiovasc Imaging 2011;4:150–6. DOI: 10.1016/j.jcmg.2010.11.015; PMID: 21329899. Schulz-Menger J, Bluemke DA, Bremerich J, et al. Standardized image interpretation and post processing in cardiovascular

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Histological validation of cardiovascular magnetic resonance T1 mapping markers of myocardial fibrosis in paediatric heart transplant recipients. J Cardiovasc Magn Reson 2017;19:10. DOI: 10.1186/ s12968–017–0326–x; PMID: 28143545. Banypersad SM, Fontana M, Maestrini V, et al. T1 mapping and survival in systemic light-chain amyloidosis. Eur Heart J 2015;36:244–51. DOI: 10.1093/eurheartj/ehu444; PMID: 25411195. Martinez-Naharro A, Treibel TA, Abdel-Gadir A, et al. Magnetic resonance in transthyretin cardiac amyloidosis. J Am Coll Cardiol 2017;70:466–77. DOI: 10.1016/j.jacc.2017.05.053; PMID: 28728692. Fontana M, Chung R, Hawkins PN, Moon JC. Cardiovascular magnetic resonance for amyloidosis. Heart Fail Rev 2015;20:133–44. DOI: 10.1007/s10741–014–9470–7; PMID: 25549885. Piechnik SK, Jerosch-Herold M. Myocardial T1 mapping and extracellular volume quantification: an overview of technical and biological confounders. Int J Cardiovasc Imaging 2017. DOI: 10.1007/s10554–017–1235–7; PMID: 28849419; epub ahead of print. Radenkovic D, Weingartner S, Ricketts L, et al. T1 mapping in cardiac MRI. Heart Fail Rev 2017;22:415–30. DOI: 10.1007/ s10741–017–9627–2; PMID: 28623475. Schelbert EB, Sabbah HN, Butler J, Gheorghiade M. Employing extracellular volume cardiovascular magnetic resonance measures of myocardial fibrosis to foster novel therapeutics. Circ Cardiovasc Imaging 2017;10:pii:e005619. DOI: 10.1161/ CIRCIMAGING.116.005619; PMID: 28512159. Messroghli DR, Moon JC, Ferreira VM, et al. Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: A consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imaging (EACVI). J Cardiovasc Magn Reson 2017;19:75. DOI: 10.1186/s12968–017–0389–8; PMID: 28992817. Bulluck H, Maestrini V, Rosmini S, et al. Myocardial T1 mapping. Circ J 2015;79:487–94. DOI: 10.1253/circj.CJ–15–0054; PMID: 25746524. Haaf P, Garg P, Messroghli DR, et al. Cardiac T1 Mapping and Extracellular Volume (ECV) in clinical practice: a comprehensive review. J Cardiovasc Magn Reson 2016;18:89. DOI: 10.1186/s12968–016–0308–4; PMID: 27899132. Spottiswoode B, Ugander M, Kellman P. Automated inline extracellular volume (ECV) mapping. Journal of Cardiovascular Magnetic Resonance 2015;17(Suppl 1):W6. DOI: 10.1186/1532– 429X–17–S1–W6. Treibel TA, Fontana M, Maestrini V, et al. Automatic measurement of the myocardial interstitium: synthetic extracellular volume quantification without hematocrit sampling. JACC Cardiovasc Imaging 2016;9:54–63. DOI: 10.1016/j. jcmg.2015.11.008; PMID: 26762875. Moon JC, Messroghli DR, Kellman P, et al. Myocardial T1 mapping and extracellular volume quantification: a Society for Cardiovascular Magnetic Resonance (SCMR) and CMR Working Group of the European Society of Cardiology consensus statement. J Cardiovasc Magn Reson 2013;15:92. DOI: 10.1186/1532–429X–15–92; PMID: 24124732. Wong TC, Piehler K, Meier CG, et al. Association between extracellular matrix expansion quantified by cardiovascular magnetic resonance and short-term mortality. Circulation 2012;126:1206–16. DOI: 10.1161/ CIRCULATIONAHA.111.089409; PMID: 22851543. Ghassan Ghosn M, Pickett S, Brunner G, et al. Association of myocardial extracellular volume and clinical outcome: a cardiac magnetic resonance study. J Am Coll Cardiol 2015;65(Issue 10S). DOI: 10.1016/S0735–1097(15)61077–4. Schelbert EB, Piehler KM, Zareba KM, et al. Myocardial fibrosis quantified by extracellular volume is associated with subsequent hospitalization for heart failure, death, or both across the spectrum of ejection fraction and heart failure stage. J Am Heart Assoc 2015;4:pii:e002613. DOI: 10.1161/ JAHA.115.002613; PMID: 26683218. Kammerlander AA, Marzluf BA, Zotter-Tufaro C, et al. T1 mapping by CMR imaging: from histological validation to clinical implication. JACC Cardiovasc Imaging 2016;9:14–23. DOI: 10.1016/j.jcmg.2015.11.002; PMID: 26684970. Mascherbauer J, Marzluf BA, Tufaro C, et al. Cardiac magnetic resonance postcontrast T1 time is associated with outcome in patients with heart failure and preserved ejection

fraction. Circ Cardiovasc Imaging 2013;6:1056–65. DOI: 10.1161/ CIRCIMAGING.113.000633; PMID: 24036385. 89. Barison A, Del Torto A, Chiappino S, et al. Prognostic significance of myocardial extracellular volume fraction in nonischaemic dilated cardiomyopathy. J Cardiovasc Med (Hagerstown) 2015;16:681–7. DOI: 10.2459/ JCM.0000000000000275; PMID: 26090916. 90. Duca F, Zotter-Tufaro C, Kammerlander AA, et al. Cardiac extracellular matrix is associated with adverse outcome in patients with chronic heart failure. Eur J Heart Fail 2017;19:502– 11. DOI: 10.1002/ejhf.680; PMID: 27891745. 91. Duca F, Kammerlander AA, Zotter-Tufaro C, et al. Interstitial fibrosis, functional status, and outcomes in heart failure with preserved ejection fraction: insights from a prospective cardiac magnetic resonance imaging study. Circ Cardiovasc Imaging 2016;9:pii:e005277. DOI: 10.1161/ CIRCIMAGING.116.005277; PMID: 27974408. 92. Puntmann VO, Carr-White G, Jabbour A, et al. T1-mapping and outcome in nonischemic cardiomyopathy: all-cause mortality and heart failure. JACC Cardiovasc Imaging 2016;9:40–50. DOI: 10.1016/j.jcmg.2015.12.001; PMID: 26762873. 93. Youn JC, Hong YJ, Lee HJ, et al. Contrast-enhanced T1 mapping-based extracellular volume fraction independently predicts clinical outcome in patients with non-ischemic dilated cardiomyopathy: a prospective cohort study. Eur Radiol 2017;27:3924–33. DOI: 10.1007/s00330–017–4817–9; PMID: 28439651. 94. Schelbert EB, Fridman Y, Wong TC, et al. Temporal relation between myocardial fibrosis and heart failure with preserved ejection fraction: association with baseline disease severity and subsequent outcome. JAMA Cardiol 2017;2:95–1006. DOI: 10.1001/jamacardio.2017.2511; PMID: 28768311. 95. Wong TC, Piehler KM, Kang IA, et al. Myocardial extracellular volume fraction quantified by cardiovascular magnetic resonance is increased in diabetes and associated with mortality and incident heart failure admission. Eur Heart J 2014;35:657–64. DOI: 10.1093/eurheartj/eht193; PMID: 23756336. 96. Brilla CG, Funck RC, Rupp H. Lisinopril-mediated regression of myocardial fibrosis in patients with hypertensive heart disease. Circulation 2000;102:1388–93. DOI: 10.1161/01. CIR.102.12.1388; PMID: 10993857. 97. Schwartzkopff B, Brehm M, Mundhenke M, Strauer BE. Repair of coronary arterioles after treatment with perindopril in hypertensive heart disease. Hypertension 2000;36:220–5. DOI: 10.1161/01.HYP.36.2.220; PMID: 10948081. 98. Diez J, Querejeta R, Lopez B, et al. Losartan-dependent regression of myocardial fibrosis is associated with reduction of left ventricular chamber stiffness in hypertensive patients. Circulation 2002;105:2512–7. DOI: 10.1161/01. CIR.0000017264.66561.3D; PMID: 12034658. 99. Izawa H, Murohara T, Nagata K, et al. Mineralocorticoid receptor antagonism ameliorates left ventricular diastolic dysfunction and myocardial fibrosis in mildly symptomatic patients with idiopathic dilated cardiomyopathy: a pilot study. Circulation 2005;112:2940–5. PMID: 16275882 100. Heydari B, Abdullah S, Pottala JV, et al. Effect of Omega-3 Acid Ethyl Esters on Left Ventricular Remodeling After Acute Myocardial Infarction: The OMEGA–REMODEL Randomized Clinical Trial. Circulation 2016;134:378–91. DOI: 10.1161/ CIRCULATIONAHA.115.019949; PMID: 27482002. 101. Cardinale D, Colombo A, Bacchiani G, et al. Early detection of anthracycline cardiotoxicity and improvement with heart failure therapy. Circulation 2015;131:1981–8. DOI: 10.1161/ CIRCULATIONAHA.114.013777; PMID: 25948538. 102. Felker GM, Thompson RE, Hare JM, et al. Underlying causes and long-term survival in patients with initially unexplained cardiomyopathy. N Engl J Med 2000;342:1077–84. DOI: 10.1056/ NEJM200004133421502; PMID: 10760308. 103. Cardinale D, Colombo A, Lamantia G, et al. Anthracyclineinduced cardiomyopathy: clinical relevance and response to pharmacologic therapy. J Am Coll Cardiol 2010;55:213–20. DOI: 10.1016/j.jacc.2009.03.095; PMID: 20117401. 104. Jordan JH, Vasu S, Morgan TM, et al. Anthracycline-associated T1 mapping characteristics are elevated independent of the presence of cardiovascular comorbidities in cancer survivors. Circ Cardiovasc Imaging 2016;9:pii:e004325. DOI: 10.1161/ CIRCIMAGING.115.004325; PMID: 27502058. 105. Zamorano JL, Lancellotti P, Rodriguez Munoz D, et al; Authors/Task Force Members; ESC Committee for Practice Guidelines. 2016 ESC Position Paper on cancer treatments and cardiovascular toxicity developed under the auspices of the ESC Committee for Practice Guidelines: The Task Force for cancer treatments and cardiovascular toxicity of the European Society of Cardiology (ESC). Eur Heart J 2016;37: 2768–801. DOI: 10.1093/eurheartj/ehw211; PMID: 27567406. 106. Chen Z, Sohal M, Sammut E, et al. Focal but not diffuse myocardial fibrosis burden quantification using cardiac magnetic resonance imaging predicts left ventricular reverse modeling following cardiac resynchronization therapy. J Cardiovasc Electrophysiol 2016;27:203–9. DOI: 10.1111/jce.12855; PMID: 26463874. 107. Lin LY, Wu CK, Juang JM, et al. Myocardial regional interstitial fibrosis is associated with left intra-ventricular dyssynchrony in patients with heart failure: a cardiovascular magnetic resonance study. Sci Rep 2016;6:20711. DOI: 10.1038/ srep20711; PMID: 26846306. 108. Spach MS, Boineau JP. Microfibrosis produces electrical load variations due to loss of side-to-side cell connections: a

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Diagnosis major mechanism of structural heart disease arrhythmias. Pacing Clin Electrophysiol 1997;20:397–413. PMID: 9058844. 109. Massare J, Berry JM, Luo X, et al. Diminished cardiac fibrosis in heart failure is associated with altered ventricular arrhythmia phenotype. J Cardiovasc Electrophysiol 2010;21:1031– 7. DOI: 10.1111/j.1540-8167.2010.01736.x. 110. Chen Z, Sohal M, Voigt T, et al. Myocardial tissue characterization by cardiac magnetic resonance imaging using T1 mapping predicts ventricular arrhythmia in ischemic and non-ischemic cardiomyopathy patients with implantable cardioverter-defibrillators. Heart Rhythm 2015;12:792–801. DOI: 10.1016/j.hrthm.2014.12.020; PMID: 25533585. 111. Francone M, Carbone I, Agati L, et al. Utility of T2-weighted short-tau inversion recovery (STIR) sequences in cardiac MRI: an overview of clinical applications in ischaemic and non-ischaemic heart disease. Radiol Med 2011;116:32–46. DOI: 10.1007/s11547-010-0594-0; PMID: 20927650. 112. Huang TY, Liu YJ, Stemmer A, Poncelet BP. T2 measurement of the human myocardium using a T2-prepared transientstate TrueFISP sequence. Magn Reson Med 2007;57:960–6. DOI: 10.1002/mrm.21208; PMID: 17457877. 113. Giri S, Chung YC, Merchant A, et al. T2 quantification for improved detection of myocardial edema. J Cardiovasc Magn Reson 2009;11:56. DOI: 20.2286/1532-429X-11-56. 114. Verhaert D, Thavendiranathan P, Giri S, et al. Direct T2 quantification of myocardial edema in acute ischemic injury. JACC Cardiovasc Imaging 2011;4:269–78. DOI: 10.1016/j. jcmg.2010.09.023; PMID: 21414575.

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115. Bohnen S, Radunski UK, Lund GK, et al. Performance of t1 and t2 mapping cardiovascular magnetic resonance to detect active myocarditis in patients with recent-onset heart failure. Circ Cardiovasc Imaging 2015;8:pii:e003073. DOI: 10.1161/ CIRCIMAGING.114.003073; PMID: 26015267. 116. Lurz P, Luecke C, Eitel I, et al. Comprehensive cardiac magnetic resonance imaging in patients with suspected myocarditis: The MyoRacer-Trial. J Am Coll Cardiol 2016;67:1800– 11. DOI: 10.1016/j.jacc.2016.02.013; PMID: 27081020. 117. Vermes E, Pucheux L, Pucheux J, et al. T2-mapping and T1-mapping detect myocardial involvement in Tako-Tsubo cardiomyopathy: a preliminary experience. J Cardiovasc Magn Reson 2015;17(Suppl 1):P354. DOI: 10.1186/1532-429X-17S1-P354. 118. Thavendiranathan P, Walls M, Giri S, et al. Improved detection of myocardial involvement in acute inflammatory cardiomyopathies using T2 mapping. Circ Cardiovasc Imaging 2012;5:102–10. DOI: 10.1161/CIRCIMAGING.111.967836; PMID: 22038988. 119. Butler CR, Savu A, Bakal JA, et al. Correlation of cardiovascular magnetic resonance imaging findings and endomyocardial biopsy results in patients undergoing screening for heart transplant rejection. J Heart Lung Transplant 2015;34:643–50. DOI: 10.1016/j.healun.2014.12.020; PMID: 25934478. 120. Usman AA, Taimen K, Wasielewski M, et al. Cardiac magnetic resonance T2 mapping in the monitoring and follow-up of acute cardiac transplant rejection: a pilot study. Circ Cardiovasc Imaging 2012;5:782–90. DOI: 10.1161/CIRCIMAGING.111.967836.

121. Anderson LJ, Holden S, Davis B, et al. Cardiovascular T2-star (T2*) magnetic resonance for the early diagnosis of myocardial iron overload. Eur Heart J 2001;22:2171–9. PMID: 11913479. 122. Carpenter JP, He T, Kirk P, et al. On T2* magnetic resonance and cardiac iron. Circulation 2011;123:1519–28. DOI: 10.1161/ CIRCULATIONAHA.110.007641; PMID: 21444881. 123. Kirk P, Roughton M, Porter JB, et al. Cardiac T2* magnetic resonance for prediction of cardiac complications in thalassemia major. Circulation 2009;120:1961–8. DOI: 10.1161/ CIRCULATIONAHA.109.874487; PMID: 19801505. 124. Modell B, Khan M, Darlison M, et al. Improved survival of thalassaemia major in the UK and relation to T2* cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2008;10:42. DOI: 10.1186/1532-429X-10-42; PMID: 18817553. 125. Alam MH, Auger D, Smith GC, et al. T1 at 1.5T and 3T compared with conventional T2* at 1.5T for cardiac siderosis. J Cardiovasc Magn Reson 2015;17:102. DOI: 10.1186/s12968-0150207-0; PMID: 26602203. 126. Sado DM, Maestrini V, Piechnik SK, et al. Noncontrast myocardial T1 mapping using cardiovascular magnetic resonance for iron overload. J Magn Reson Imaging 2015;41: 1505–11. DOI: 10.1002/jmri.24727; PMID: 25104503. 127. Captur G, Gatehouse P, Keenan KE, et al. A medical devicegrade T1 and ECV phantom for global T1 mapping quality assurance – the T1 Mapping and ECV Standardization in cardiovascular magnetic resonance (T1MES) program. J Cardiovasc Magn Reson 2016;18:58. DOI: 10.1186/s12968016-0280-z; PMID: 27660042.

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Diagnosis

The Role of Automated 3D Echocardiography for Left Ventricular Ejection Fraction Assessment Ernest Spitzer, 1,2 Ben Ren, 1,2 Felix Zijlstra, 1 Nicolas M Van Mieghem 1 and Marcel L Geleijnse 1 1. Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands; 2. Cardialysis, Clinical Trial Management & Core Laboratories, Rotterdam, the Netherlands

Abstract Ejection fraction is one of the most powerful determinants of prognosis and is a crucial parameter for the determination of cardiovascular therapies in conditions such as heart failure, valvular conditions and ischaemic heart disease. Among echocardiographic methods, 3D echocardiography has been attributed as the preferred one for its assessment, given an increased accuracy and reproducibility. Full-volume multi-beat acquisitions are prone to stitching artefacts due to arrhythmias and require prolonged breath holds. Single-beat acquisitions exhibit a lower temporal resolution, but address the limitations of multi-beat acquisitions. If not fully automated, 3D echocardiography remains time-consuming and resource-intensive, with suboptimal observer variability, preventing its implementation in routine practice. Further developments in hardware and software, including fully automated knowledge-based algorithms for left ventricular quantification, may bring 3D echocardiography to a definite turning point.

Keywords 3D echocardiography, non-invasive imaging, left ventricular ejection fraction, single-beat acquisitions, automated analysis, adaptive analytics Disclosure: The authors have no conflicts of interest to declare. Received: 12 September 2017 Accepted: 24 October 2017 Citation: Cardiac Failure Review 2017;3(2):97–102. DOI: 10.15420/cfr.2017:14.1 Correspondence: Ernest Spitzer, Thoraxcenter, Erasmus University Medical Center, ‘s-Gravendijkwal 230, 3015 CE Rotterdam, the Netherlands; E: ernest.spitzer@gmail.com

Left ventricular ejection fraction (LVEF) is the most widely used parameter of left ventricular (LV) systolic function, and its deterioration is associated with reduced survival rates.1 LVEF is expressed as a percent value, and calculated by dividing the stroke volume (enddiastolic volume minus end-systolic volume) by the end-diastolic volume and multiplying by 100; however, volume measurements entail a much higher complexity.2 Despite these limitations, noninvasive imaging has become the mainstay for the assessment of LV volumes and ejection fraction (EF), and echocardiography (echo) is the workhorse modality in routine clinical practice.3 Echo itself provides different methods to assess LV volumes and these have evolved historically (see Figure 1). The first non-invasive assessment of EF was performed using mono-dimensional (M-mode) echocardiographic images of the heart in the late 1960s,4 which within a lustrum transitioned to real-time 2D and 2D-derived 3D echocardiographic assessments.5 Accuracy (closer to the gold standard) and precision (better reproducibility) – unequivocal determinants of clinical utility of echo – also progressed; yet, the position of echo as potentially the best solution for EF assessment was envisioned with the introduction of the matrix-array transducer in the late 1980s together with the progressive validation of real-time 3D echo.6 Other imaging modalities for assessment of EF exist and may be preferred under specific scenarios, but are associated with an increased cost and complexity (cardiac magnetic resonance; CMR), radiation (CT) or both (nuclear imaging).7 Notwithstanding, among all imaging modalities, CMR is generally referred to as the gold standard for the assessment of LV volumes. Image quality obtained with CMR

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is superior to echo despite its technically lower spatial and temporal resolution, since it is much less limited by the acoustic window (see Figure 2).8,9 The use of short-axis slices for contouring and the possibility of long-axis corrections for the basal and apical limits of the LV may also contribute to this attribute.10,11 Different echo methods for the assessment of EF have been compared with CMR and currently there is no doubt that 3D echo, with good image quality, offers the closest approximation to CMR-derived volumes.12 M-mode has been retracted as a valid method for volume derivations and 2D-derived biplane or triplane volumes may be limited by potential foreshortening and out-of-plane wall motion abnormalities.2

Current Status of 3D Echocardiography Comparisons with Cardiac Magnetic Resonance When comparing with CMR, 3D echo shows closer agreement for LV volumes quantification than 2D echo. In a meta-analysis including 28 studies comparing 3D echo with CMR in 1,198 individuals, 3D echo underestimated the LV end-diastolic volume (LVEDV) by −14 ± 5 ml and the LV end-systolic volume (LVESV) by −7 ± 3 ml, while LVEF was virtually the same among both modalities.13 2D-derived volumes showed a larger bias, being −33 ± 10 ml for LVEDV and −16 ± 5 ml for LVESV. In practice, this entails that LVEF, but not LV volumes, can be used interchangeably among these modalities. Concordantly, current guidelines recommend the use of biplane 2D method of disks summation (biplane Simpson’s method) and, in laboratories with experience in 3D, 3D echo is recommended when image quality is adequate for analysis. 2 Interestingly, larger biases between 3D echo and CMR have been reported in women

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Diagnosis Figure 1: Historical Perspective of Ejection Fraction Assessment with Echocardiography Technological advances

Clinical Implementation

Left ventricle Assessment

Discovery of ultrasound (Spallanzani) 18th century Conception of SONAR system (Langevin) 1917

Challenges of 3D Echocardiography

Ultrasound use in Medicine (Dussik) 1941

Pulse-echo ultrasonic detector 1950’s

First M-mode Echo (Edler & Hertz) 1953

Fiber-optic recorder 1960’s

Intracardiac Echo (Ciezynski) 1960’s

First electronic phase-array scanner (Somer) 1968

Contrast Echo (Gramiak & Shah) 1968

Pulse-wave Doppler (Wells & Peronneau) 1969

Stress Echo (Kraunz) 1970

Real-time 2D scanner (Bom) 1971

Intraoperative Echo (Johnson) 1972

2D Echo (Gramiak) 1973

Dupplex scanner (Barber)

First 3D Echo (Dekker) 1974

Mechanical 2D sector-scanner (Griffith & Henry) Electronic phasearray 2D scanner (Thurstone) 1974 Digital Multigate Doppler (Brandestini) 1978 First matrix-array transducer (3D) (Snyder) 1986

M-mode EF (Feigenbaum & Dodge) 1968

1974

Transoesophageal Echo (Frazin) 1976 Colour Doppler flow imaging (Kasai) 1982

2D-based Simpson’s rule 1980’s

Myocardial velocity imaging (McDicken) 1992

3D Echo validation 1990’s

Pre-Echo

Fully automated 3D LV analysis Starting

Machine learning and automation Starting

Echo Era

LV = left ventricular; EF = ejection fraction; Echo = echocardiography.

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Despite the initial enthusiasm, 3D echo has not been able to replace 2D echo in routine clinical use, and has never been successfully applied in clinical trials.16–18 Favourable characteristics are its potentially high accuracy and precision, but these are counterbalanced by several factors. Firstly, quality of the 3D datasets is determined by patient factors (e.g. acoustic window, regular cardiac rhythm and adequate breath hold), and suffers from relatively low temporal and spatial resolutions. Temporal resolution (i.e. volume rate) refers to the ability of localising an anatomic structure in a point in time and is limited by the speed of sound, but can be improved by reducing the sector size (width and depth). In addition, spatial resolution – defined as the ability to differentiate two points in space – is dependent on the number of scan lines per volume (scan line density). However, the more scan lines, the longer the acquisition time, the lower the volume rate. Thus, smaller scanned sectors offer a better overall resolution.16 Secondly, 3D echo requires even more in-depth knowledge of the echo settings during acquisition in order to obtain the best image possible, as has been recently outlined by the European Associations of Echocardiography and the American Society of Echocardiography.16 Moreover, as with 2D images, the analysis of 3D images has an inherent learning curve before obtaining reliable results.17 Thirdly, if not fully automated, 3D echo for the assessment of LVEF is still a time-consuming process, representing a logistical and economic challenge for the clinical work flow.

Single-beat 3D Echocardiography

2D & 3D Speckle tracking Echo 2000’s

Single-beat highresolution 3D Starting

and in patients with congenital or acquired cardiac disease.14 Limited comparisons have been performed in patients with ventricular aneurysms or hypertrophic cardiomyopathy, generally excluded from validation studies. Noteworthy, underestimation of volumes is more pronounced in patients with depressed LVEF, since the true boundary (i.e. endocardial border) between compacted myocardium and the LV cavity (blood and trabeculations) is less well defined (see Figure 2).15 This phenomenon is also common in patients with noncompacted cardiomyopathy, and accentuated when image quality is suboptimal.

Breakthroughs

Full-volume multi-beat acquisitions offer the best available 3D resolution and provide the recommended datasets for the assessment of LV volumes.16 Wide-angle single-beat acquisitions, however, overcome stitching artefacts common in irregular cardiac rhythms, and avoid the need for prolonged breath holds.19 Initial experience with singlebeat analysis showed similar findings to those described for manual 3D when compared with CMR in patients with sinus rhythm, with a bias of −18 ± 27 ml for LVEDV, −10 ± 18 ml for LVESV, and −0 ± 3 % for LVEF.15 Furthermore, results in patients with AF were comparable with those obtained with the biplane Simpson’s method.15 Notwithstanding, there is a trade-off between temporal and spatial resolutions when considering single-beat 3D.19 Whereas optimally recorded multi-beat acquisitions show a temporal resolution of 33 ± 8 volumes per second (vps), superior to CMR (24 heart phases per cardiac cycle), singlebeat acquisitions may be as low as 7 ± 2 vps, which are insufficient to capture the LVESV.12,20 In addition, the imaging modes during acquisition influence the spatial and temporal resolutions, where the harmonic and space modes provide better spatial resolution than the fundamental and time modes with varying spatial resolution from 14 ± 2 vps up to 49 ± 7 vps. Datasets with the highest temporal resolution are those with the lowest spatial resolution.19

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Automated 3D Ejection Fraction Figure 2: Image Quality Obtained with Routing Echocardiography and Cardiac Magnetic Resonance

Automated 3D Echocardiography

3D Echo

2D Echo

CMR

Among modalities, CMR offers the best differentiation between compacted myocardium and the LV cavity as it is much less limited by the acoustic window. CMR = cardiac magnetic resonance; echo = echocardiography; LA = left atrium; LV = left ventricular.

Figure 3: Stairway of Echocardiographic Methods for the Assessment of Left Ventricular Ejection Fraction

3D fully auto

3D semi-auto

3D manual

2D triplane

Clinical trials

Quantitative (>5 min); requires vast experience; provides a 16-segment model

Limited clinical use

Clinical research

Validation

Innovation

Cost-efficient; user-friendly; time-efficient; low complexity Standard acquisition, analysis and interpretation Cost-effectiveness; accuracy; prognosis; imaging endpoints Costly; advanced training; timeconsuming; high complexity

Clinical validation; feasibility; reliability; normal subjects; pathologic conditions Phantom testing; gold standard; reproducibility; multi-modality and inter-software cross-validation

High-resolution 3D transducers; automated algorithms; adaptive analytics; machine learning; computational capacity

Current fastest method (<1 min); reliability may be questionable; requires vast experience; overused in routine practice

Routine clinical use

Limited clinical use

Mainly research

No longer recommended

EF = ejection fraction; M-mode = mono-dimensional; WMA = wall motion abnormalities.

3D Ejection Fraction Assessment in the Clinical Work Flow The current status of ejection fraction assessment in echo laboratories is summarised in Figure 3. Although not endorsed by the guidelines, echo reports with visual estimates of LVEF are frequently observed. Visual EF is a fast method and has shown adequate accuracy with expert readers.21 Wall motion scoring and a derived LVEF has also been implemented as a semi-quantitative alternative. 22,23 Conversely, the biplane Simpson’s method is not systematically used in a significant proportion of echo laboratories, which points towards a practical mismatch with the guidelines. 2 3D echo is currently confined to limited clinical use and research use (see Figure 4). The need for advanced training, its critical

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Guidelines

Quantitative (7–10 min); requires vast experience; most accurate

Semi-quantitative method (1-2 min); reliability may be questionable; requires vast experience

WMA scoring

Routine practice

Quantitative (5–7 min); requires vast experience; most accurate

Quantitative (<1 min); geometrical assumptions; not reliable

M-mode EF

Visual EF

Fastest method (<10 sec); increased precision

Quantitative (<5 min); requires vast experience; guideline-recommended

2D biplane

Figure 4: Stairway from Innovation to Routine Clinical Practice for 3D Echocardiography

dependency on image quality and its persistent complexity may continue to limit the generalizability of 3D echo. Conversely, a cost- and time-efficient, user-friendly approach is needed. This may only be possible with the advent of improved echocardiographic probes, higher computational capacity and machine learning technologies. Effort should be focused on the development of more potent probes aiming mainly to improving spatial resolution, as single-beat acquisitions with 3D echo are currently close to CMR in terms of temporal resolution.19 This, however, is a clinical demand that has been standing for a decade.24 With the current limitations, 3D echo for assessment of LVEF can be implemented in routine clinical flow if sufficient resources are assigned,25 including: training and certification of personnel performing 3D acquisitions (including a standard protocol); a dedicated (specialised) team for off-line 3D LVEF assessment to assure reliable measurements (accurate and

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Diagnosis Table 1: Comparisons Among Fully Automated 3D Methods and Either Cardiac Magnetic Resonance or Manual Echocardiography Software

Feasibility

LVEDV bias (ml)

LVESV bias (ml)

LVEF bias (%)

Thavendiranathan, CMR et al., 201215 (I)

Authors

Reference

eSie LVATM (Siemans Healthcare)

101

66 %

−18 ± 54

−10 ± 36

−0 ± 6

Thavendiranathan, et al., 201215 (II)*

2D Simpson

eSie LVA

89 %

2 ± 16

4 ± 13

−2 ± 4

Ren, et al., 201419

Manual 3D

eSie LVA

85 %

−3 ± 23

−2 ± 14

−0 ± 9

2D Simpson Otani, et al., 201631,*

HeartModel (Philips Healthcare)

100 %

−3 ± 26

−1 ± 17

−0 ± 10

Tsang, et al., 201630 (I)

CMR

HeartModel

94 %

2 ± 40

10 ± 40

−6 ± 16

Tsang, et al., 201630 (II)

Manual 3D

HeartModel

104

90 %

−24 ± 50

−13 ± 58

−2 ± 18

Spitzer, et al., 201732

Manual 3D

HeartModel

93 %

−6 ± 39

−2 ± 39

−1 ± 15

Levy, et al., 201733

CMR

HeartModel

86 %

−22 ± 34

−13 ± 33

−1 ± 7

Medvedofsky, et al., 201734,†

Manual 3D

HeartModel

180

100 %

−14 ± 20

−6 ± 16

−2 ± 7

Medvedofsky, et al., 201734,†,‡

Manual 3D

HeartModel

300

66 %

−3 ± 22

1 ± 16

0 ± 10

*Atrial fibrillation; †Including patients with arrhythmias; ‡Consecutive patients. I and II describe two reference modalities used in a single report. CMR = cardiac magnetic resonance; LVA = left ventricle analysis; LVEDV = left ventricle end-diastolic volume; LVEF = left ventricle ejection fraction; LVESV = left ventricle end-systolic volume.

reproducible); and assigning the appropriate time for acquisition, analysis, approval and interpretation of 3D EF measurements. Consequently, the establishment of training programmes to enable widespread learning of each of the steps mentioned above has the potential to extend this technique. Importantly, in order to use 3D echo in routine clinical practice, robust normal reference values are required. Recent guidelines on chamber quantification provide limited data on no more than 1,780 subjects, with different ethnic backgrounds.2 These data seem insufficient to reliably provide guidance based on gender, age and ethnicity. A recent meta-analysis including 2,806 subjects highlighted that significant heterogeneity and inconsistency exist among studies, which calls for standardisations and a collaborative prospective collection of data.26

Fully Automated 3D Ejection Fraction Assessment Fully automated 3D EF analysis refers to obtaining quantitative results without any user interaction (e.g. views selection, markers positioning and contours drawing or modification). Several scientific groups as well as vendors have developed algorithms for 3D endocardial border detection.27,28 However, most of them remain semi-automatic where the user input is initially needed to manually annotate important landmarks (e.g. mitral plane, apex), including TomTec 4D LV-Analysis© software (TomTec Imaging Systems), Philips QLab 3DQ-Advanced software (Philips Healthcare) and GE 4D LVQ tool in the EchoPAC software (GE Vingmed Ultrasound). Notwithstanding, multiple investigations on these semi-automated methods have reported promising accuracy and reproducibility results, as well as reduced analysis time when compared with manual 3D echo.27 Published data in which commercially-available software were used in a fully automated manner is limited to two vendors, in which knowledge-based probabilistic contouring algorithms29 or adaptive analytics algorithms are used.30 Initially, it was Siemens ultrasound that integrated the left ventricle analysis (LVA) tool in the ACUSON SC2000 PRIME (Siemens Healthcare) workplace, which uses an expert knowledge database for border detection. Subsequently, Philips

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Healthcare incorporated the HeartModel algorithm in the Philips EPIQ 7 machine. The algorithms start by automatically detecting the enddiastolic and end-systolic phases, generating preliminary endocardial surfaces that are then compared with an existing database of 3D datasets. Then, the software matches volumes and shapes and generates a model adapted to the patients’ LV.30 Table 1 summarises the available data on comparisons between fully automated 3D algorithms and either CMR or manual echo (2D or 3D).15,17,19,30–34 Except for one,17 all studies investigated selected cohorts. Feasibility remains low (one-third not feasible) due to contouring algorithm failures in presence of suboptimal image quality or false data acquisition triggering.15,17,19 However, AF does not preclude the use of fully automated algorithms, as has been demonstrated in limited number of studies.15,31 The ease of use and the high reproducibility of these algorithms make this strategy a candidate for bringing 3D EF into a widespread clinical use; however, there remain some challenges. Firstly, image quality plays a pivotal role, and results obtained with poor but analysable image quality (evidenced in up to one-fourth of an unselected population) provide inaccurate results.17 Secondly, existing databases of 3D datasets within the algorithms seem not to properly address subjects with large aneurysms, complex congenital heart disease or even dilated ventricles, where larger underestimation of volumes has been reported.15,30 A reasonable approach would be to extend such database to specific conditions that can be selected during acquisition (i.e. an adaptive acquisition protocol). Thirdly, under an expert eye 80 % of fully automated contours would still need some degree of correction.17 These include small changes that marginally affect the volumes and EF but also larger changes that could significantly affect the decision-making process for a specific patient. Thus, until results in larger cohorts show differently, proper training in LVEF assessment and supervision of automated contours is strongly encouraged. Finally, both fully automated algorithms are vendor-dependent, and this technology cannot be applied to acquisitions performed with other machines. Further development and validation of a vendor-independent software, such as the TomTec 4D LV-Analysis software, may further expand the use of fully automated analysis.35

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Automated 3D Ejection Fraction Conclusion Within half a century echo has matured into the preferred non-invasive modality for the assessment of LVEF and volumes. 3D echo offers the best accuracy and reproducibility within echocardiographic methods;

10.

11.

12.

13.

hite HD, Norris RM, Brown MA, et al. Left ventricular endW systolic volume as the major determinant of survival after recovery from myocardial infarction. Circulation 1987;76:44–51. DOI: 10.1161/01.CIR.76.1.44; PMID: 3594774 Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging 2015;16:233–70. DOI: 10.1093/ehjci/jev014; PMID: 25712077 Picard MH, Popp RL, Weyman AE. Assessment of left ventricular function by echocardiography: a technique in evolution. J Am Soc Echocardiogr 2008;21:14–21. DOI: 10.1016/j. echo.2007.11.007; PMID: 18165124 Edler I, Lindström K. The history of echocardiography. Ultrasound Med Biol 2004;30:1565–644. DOI: 10.1016/S03015629(99)00056-3; PMID: 15617829 Gowda RM, Khan IA, Vasavada BC, et al. History of the evolution of echocardiography. Int J Cardiol 2004;97:1–6. DOI: 10.1016/j.ijcard.2003.07.018; PMID: 15336798 Krenning BJ, Voormolen MM, Roelandt JR. Assessment of left ventricular function by three-dimensional echocardiography. Cardiovasc Ultrasound 2003;1:12. DOI: 10.1186/1476-7120-1-12; PMID: 14514356 Asferg C, Usinger L, Kristensen TS, Abdulla J. Accuracy of multi-slice computed tomography for measurement of left ventricular ejection fraction compared with cardiac magnetic resonance imaging and two-dimensional transthoracic echocardiography: a systematic review and meta-analysis. Eur J Radiol 2012;81:e757–62. DOI: 10.1016/j.ejrad.2012.02.002; PMID: 22381439 Dorosz JL, Lezotte DC, Weitzenkamp DA, et al. Performance of 3-dimensional echocardiography in measuring left ventricular volumes and ejection fraction: a systematic review and metaanalysis. J Am Coll Cardiol 2012;59:1799–808. DOI: 10.1016/j. jacc.2012.01.037; PMID: 22575319 Alkema M, Spitzer E, Soliman OI, Loewe C. Multimodality imaging for left ventricular hypertrophy severity grading: a methodological review. J Cardiovasc Ultrasound 2016;24:257–67. DOI: 10.4250/jcu.2016.24.4.257; PMID: 28090249 Wood PW, Gibson PH, Becher H. Three-dimensional echocardiography in a dynamic heart phantom: comparison of five different methods to measure chamber volume using a commercially available software. Echo Res Pract 2014;1:51–60. DOI: 10.1530/ERP-14-0051; PMID: 26693301 Kirschbaum SW, Baks T, Gronenschild EH, et al. Addition of the long-axis information to short-axis contours reduces interstudy variability of left-ventricular analysis in cardiac magnetic resonance studies. Invest Radiol 2008;43:1–6. DOI: 10.1097/RLI.0b013e318154b1dc; PMID: 18097271 Soliman OI, Kirschbaum SW, van Dalen BM, et al. Accuracy and reproducibility of quantitation of left ventricular function by real-time three-dimensional echocardiography versus cardiac magnetic resonance. Am J Cardiol 2008;102:778–83. DOI: 10.1016/j.amjcard.2008.04.062; PMID: 18774006 Rigolli M, Anandabaskaran S, Christiansen JP, Whalley GA. Bias associated with left ventricular quantification by multimodality imaging: a systematic review and meta-

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however, it is still time-consuming and requires significant expertise. The advent of fully automated 3D analysis software may represent an opportunity to further promote and investigative the widespread use of 3D echo. n

analysis. Open Heart 2016;3:e000388. DOI: 10.1136/ openhrt-2015-000388; PMID: 27158524 Shimada YJ, Shiota T. A meta-analysis and investigation for the source of bias of left ventricular volumes and function by three-dimensional echocardiography in comparison with magnetic resonance imaging. Am J Cardiol 2011;107:126–38. DOI: 10.1016/j.amjcard.2010.08.058; PMID: 21146700 Thavendiranathan P, Liu S, Verhaert D, et al. Feasibility, accuracy, and reproducibility of real-time full-volume 3D transthoracic echocardiography to measure LV volumes and systolic function: a fully automated endocardial contouring algorithm in sinus rhythm and atrial fibrillation. JACC Cardiovasc Imaging 2012;5:239–51. DOI: 10.1016/j.jcmg.2011.12.012; PMID: 22421168 Lang RM, Badano LP, Tsang W, et al. EAE/ASE recommendations for image acquisition and display using three-dimensional echocardiography. Eur Heart J Cardiovasc Imaging 2012;13:1–46. DOI: 10.1093/ehjci/jer316; PMID: 22275509 Medvedofsky D, Mor-Avi V, Byku I, et al. Three-dimensional echocardiographic automated quantification of left heart chamber volumes using an adaptive analytics algorithm: feasibility and impact of image quality in nonselected patients. J Am Soc Echocardiogr 2017;30:879–85. DOI: 10.1016/j. echo.2017.05.018; PMID: 28688857 Yu CM, Abraham WT, Bax J, et al. Predictors of response to cardiac resynchronization therapy (PROSPECT) study design. Am Heart J 2005;149:600–5. DOI: 10.1016/j.ahj.2004.12.013; PMID: 15990740 Ren B, Vletter WB, McGhie J, et al. Single-beat real-time threedimensional echocardiographic automated contour detection for quantification of left ventricular volumes and systolic function. Int J Cardiovasc Imaging 2014;30:287–94. DOI: 10.1007/ s10554-013-0327-2; PMID: 24221906 Macron L, Lim P, Bensaid A, et al. Single-beat versus multibeat real-time 3D echocardiography for assessing left ventricular volumes and ejection fraction: a comparison study with cardiac magnetic resonance. Circ Cardiovasc Imaging 2010;3:450–5. DOI: 10.1161/CIRCIMAGING.109.925966; PMID: 20435854 Thavendiranathan P, Popovic ZB, Flamm SD, et al. Improved interobserver variability and accuracy of echocardiographic visual left ventricular ejection fraction assessment through a self-directed learning program using cardiac magnetic resonance images. J Am Soc Echocardiogr 2013;26:1267–73. DOI: 10.1016/j.echo.2013.07.017; PMID: 23993695 Lavine SJ, Salacata A. Visual quantitative estimation: semiquantitative wall motion scoring and determination of ejection fraction. Echocardiography 2003;20:401–10. DOI: 10.1046/j.1540-8175.2003.03079.x; PMID: 12848859 Galema TW, van de Ven AR, Soliman OI, et al. Contrast echocardiography improves interobserver agreement for wall motion score index and correlation with ejection fraction. Echocardiography 2011;28:575–81. DOI: 10.1111/j.15408175.2010.01379.x; PMID: 21535116 van der Heide JA, Kleijn SA, Aly MF, et al. Three-dimensional echocardiography for left ventricular quantification: fundamental validation and clinical applications. Neth Heart J 2011;19:423–31. DOI: 10.1007/s12471-011-0160-y; PMID: 21584798

25. v an Dalen BM, van Miltenburg AJ, Zuetenhorst HJ, Dragoiescu C. Implementation of left ventricular ejection fraction assessment by three-dimensional echocardiography in daily clinical practice. J Am Soc Echocardiogr 2017;30:932. DOI: 10.1016/j.echo.2017.05.011; PMID: 28688856 26. Buccheri S, Costanzo L, Tamburino C, Monte I. Reference values for real time three-dimensional echocardiographyderived left ventricular volumes and ejection fraction: review and meta-analysis of currently available studies. Echocardiography 2015;32:1841–50. DOI: 10.1111/echo.12972; PMID: 26053260 27. Leung KY, Bosch JG. Automated border detection in threedimensional echocardiography: principles and promises. Eur J Echocardiogr 2010;11:97–108. DOI: 10.1093/ejechocard/jeq005; PMID: 20139440 28. Bernard O, Bosch JG, Heyde B, et al. Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography. IEEE Trans Med Imaging 2016;35:967–77. DOI: 10.1109/TMI.2015.2503890; PMID: 26625409 29. Yang L, Georgescu B, Zheng Y, et al. A fast and accurate tracking algorithm of left ventricles in 3D echocardiography. Proc IEEE Int Symp Biomed Imaging 2008;5:221–4. DOI: 10.1109/ ISBI.2008.4540972; PMID: 19936301 30. Tsang W, Salgo IS, Medvedofsky D, et al. Transthoracic 3D echocardiographic left heart chamber quantification using an automated adaptive analytics algorithm. JACC Cardiovasc Imaging 2016;9:769–82. DOI: 10.1016/j.jcmg.2015.12.020; PMID: 27318718 31. Otani K, Nakazono A, Salgo IS, et al. Three-dimensional echocardiographic assessment of left heart chamber size and function with fully automated quantification software in patients with atrial fibrillation. J Am Soc Echocardiogr 2016;29:955–65. DOI: 10.1016/j.echo.2016.06.010; PMID: 27477865 32. Spitzer E, Ren B, Soliman OI, et al. Accuracy of an automated transthoracic echocardiographic tool for 3D assessment of left heart chamber volumes. Echocardiography 2017;34:199–209. DOI: 10.1111/echo.13436; PMID: 28240430 33. Levy F, Dan Schouver E, Iacuzio L, et al. Performance of new automated transthoracic three-dimensional echocardiographic software for left ventricular volumes and function assessment in routine clinical practice: Comparison with 3 Tesla cardiac magnetic resonance. Arch Cardiovasc Dis 2017; DOI: 10.1016/j.acvd.2016.12.015; PMID: 28566200; epub ahead of press. 34. Medvedofsky D, Mor-Avi V, Amzulescu M, et al. Threedimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study. Eur Heart J Cardiovasc Imaging 2017; DOI: 10.1093/ehjci/jew328; PMID: 28159984; epub ahead of press. 35. Eskofier J, Wefstaedt P, Beyerbach M, et al. Quantification of left ventricular volumes and function in anesthetized beagles using real-time three-dimensional echocardiography: 4D-TomTec™ analysis versus 4D-AutLVQ™ analysis in comparison with cardiac magnetic resonance imaging. BMC Vet Res 2015;11:260. DOI: 10.1186/s12917-015-0568-5; PMID: 26459280

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Co-Morbidities

Hypertension and Frailty Syndrome in Old Age: Current Perspectives Izabella Uchmanowicz, 1 Anna Chudiak, 1 Beata Jankowska-Polańska 1 and Robbert Gobbens 2 1. Division of Nursing in Internal Medicine Procedures, Department of Clinical Nursing, The Faculty of Health Sciences, Wroclaw Medical University, Poland; 2. The Faculty of Health, Sports and Social Work, Inholland University of Applied Sciences, Amsterdam, the Netherlands

Abstract Hypertension is both a health problem and a financial one globally. It affects nearly 30 % of the general population. Elderly people, aged ≥65 years, are a special group of hypertensive patients. In this group, the overall prevalence of the disease reaches 60 %, rising to 70 % in those aged ≥80 years. In the elderly population, isolated systolic hypertension is quite common. High systolic blood pressure is associated with an increased risk of cardiovascular disease, cerebrovascular disease, peripheral artery disease, cognitive impairment and kidney disease. Considering the physiological changes resulting from ageing alongside multiple comorbidities, treatment of hypertension in elderly patients poses a significant challenge to treatment teams. Progressive disability with regard to the activities of daily life, more frequent hospitalisations and low quality of life are often seen in elderly patients. There is discussion in the literature regarding frailty syndrome associated with old age. Frailty is understood to involve decreased resistance to stressors, depleted adaptive and physiological reserves of a number of organs, endocrine dysregulation and immune dysfunction. The primary dilemma concerning frailty is whether it should only be defined on the basis of physical factors, or whether psychological and social factors should also be included. Proper nutrition and motor rehabilitation should be prioritised in care for frail patients. The risk of orthostatic hypotension is a significant issue in elderly patients. It results from an autonomic nervous system dysfunction and involves maladjustment of the cardiovascular system to sudden changes in the position of the body. Other significant issues in elderly patients include polypharmacy, increased risk of falls and cognitive impairment. Chronic diseases, including hypertension, deteriorate baroreceptor function and result in irreversible changes in cerebral and coronary circulation. Concurrent frailty or other components of geriatric syndrome in elderly patients are associated with a worse perception of health, an increased number of comorbidities and social isolation of the patient. It may also interfere with treatment adherence. Identifying causes of non-adherence to pharmaceutical treatment is a key factor in planning therapeutic interventions aimed at increasing control, preventing complications, and improving long-term outcomes and any adverse effects of treatment. Diagnosis of frailty and awareness of the associated difficulties in adhering to treatment may allow targeting of those elderly patients who have a poorer prognosis or may be at risk of complications from untreated or undertreated hypertension, and for the planning of interventions to improve hypertension control.

Keywords Frailty syndrome, hypertension, polypharmacy, orthostatic hypotension, falls, cognitive impairment, old age Disclosure: The authors have no conflicts of interest to declare. Received: 21 July 2017 Accepted: 12 September 2017 Citation: Cardiac Failure Review 2017;2017;3(2):102–7. DOI: 10.15420/cfr.2017:9:2 Correspondence: Professor Izabella Uchmanowicz, Division of Nursing in Internal Medicine Procedures, Department of Clinical Nursing, The Faculty of Health Sciences, Wroclaw Medical University, 5 Bartla Street, 51-618 Wroclaw, Poland. E: izabella.uchmanowicz@umed.wroc.pl

Hypertension is considered the most common disorder in the general population. It is both a health and a financial problem worldwide. The Writing Group of the American Society of Hypertension defines hypertension as a cardiovascular syndrome resulting from a number of interconnected factors.1 Therefore, a comprehensive evaluation is required, involving both the cardiovascular system and other risk factors. In the global population, the prevalence of hypertension is 26.4 %, which is expected to increase to one-third of the population by 2025.2 The elderly comprise a special group of hypertensive patients because of ageing-related processes. In accordance with data from the Framingham study, 58.9 % of people aged ≥65 and 70 % of those aged ≥80 years are hypertensive.3 Most patients in the group have isolated systolic hypertension, with normal diastolic blood pressure (DBP) values – below 90 mmHg. In the elderly population, systolic blood pressure (SBP) is a more significant risk factor for cardiovascular complications than DBP. Chronic high SBP leads to left ventricular hypertrophy, as

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decreased elasticity of large blood vessels – particularly the aorta – puts extra strain on the heart.4 The ageing process involves a number of changes contributing to the development of hypertension. Besides decreased blood vessel elasticity, these include increased collagen content in the extracellular matrix, decreased numbers of elastic fibres, increased vessel wall thickness, and decreased vessel lumen. In the elderly, the lower number of elastic fibres in the vessel wall contributes to increased SBP. Chronic high blood pressure (BP) leads to irreversible vascular changes and increases the risk of cardiovascular complications.5 Treatment of elderly patients in line with the ESC/ESH (European Society of Cardiology/European Society of Hypertension) guidelines largely decreases the risk of stroke and mortality. Regardless of age, target BP values should be below 140/90 mmHg.6 In elderly patients, anti-hypertensive treatment should be administered with caution because of the presence of atherosclerotic lesions, which lead to myocardial ischaemia and cerebrovascular incidents. An excessively

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Hypertension and Frailty Syndrome in Old Age: Current Perspectives rapid BP reduction can result in decreased perfusion of vital organs and even enhance ischaemic lesions.7

Anti-hypertensive Treatment in Elderly Patients Considering the physiological alterations resulting from ageing and the presence of multiple comorbidities, treatment of hypertension in elderly patients poses a significant challenge to treatment teams. Age can affect the pharmacokinetics of the medication used and decrease the patient’s capability to comply with treatment.8 Age also has a significant impact on the function of all body systems, particularly the cardiovascular system. Processes occurring in the arteries contribute to increased arterial stiffness and calcium accumulation, as well as quantitative and qualitative alterations in vascular wall collagen. Consequences include atherosclerosis and decreased vascular elasticity, impaired sino-atrial node function, and decreased heart rate. All this leads to increased SBP, decreased left ventricular ejection fraction, and impaired response to orthostatic changes, seen in elderly patients. There is a risk of isolated systolic hypertension or diastolic heart failure.9,10 Ageing also significantly affects the central nervous system (CNS). Cerebral perfusion decreases by 15–20 % in the elderly. The number of neurons in the grey matter, cerebellum, and hippocampus also declines. In consequence, elderly individuals may experience impaired memory or other cognitive functions, which restricts their activity and mobility in daily living. Ageing is also apparent in the kidneys, with structural and functional changes taking place. Both kidney weight and glomerular filtration are reduced. Between the ages of 40 and 90, kidney performance can decrease by up to 50 %.11 Age also affects the regulation of urine density and pH by the kidneys.12 In the digestive tract, ageing manifests itself in decreased oesophageal motor activity, referred to as presbyoesophagus.13 Another ageing-related process in the digestive system is the decrease in digestive juice acidity because of gastric mucosal atrophy.14 The respiratory system is also affected by ageing. Its impact mainly involves the gradual decrease in chest mobility because of costal cartilage ossification, and reduced muscle power. These processes increase susceptibility to bronchial infection.15 Changes in the immune system are also significant in terms of the risk of infection, as they weaken the body’s immunity. Systemic changes from ageing mainly include the decreases in muscle mass and lean body mass, and water content drops, primarily in the cartilage. Moreover, ageing involves a loss of bone mass, with degenerative processes and an increased risk of osteoporosis. This in turn increases the risk of falls, fractures, and other injuries. Compliance with treatment is adversely affected by the patients’ impaired senses of vision, hearing, taste, and smell.16 Ageing processes have a significant impact on the course of treatment. This includes changes in pharmacokinetics, including absorption (decreased active transport reduces the bioavailability of medication), distribution (extended half-life of fat-soluble drugs, increased serum concentration of water-soluble drugs), metabolism (slower oxidative metabolism results in higher concentrations of some drugs), and elimination (which is decreased because of lower kidney and liver perfusion; elimination of drugs and their metabolites may also be insufficient because of impaired kidney function).17 Anti-hypertensive treatment of elderly patients in line with the ESH/ ESC guidelines18 largely reduces the risk of stroke and mortality from cardiovascular incidents. The basic drug groups used in elderly patients, if no special indications exist, are diuretics, calcium channel blockers (CCBs), angiotensin-converting enzyme (ACE) inhibitors, and angiotensin receptor blockers. Because of the increased risk of adverse events in the initial stage of treatment, a low initial dosage

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should be used, and later increased gradually with caution (‘titrated’) until the desired effect is achieved. Anti-hypertensive treatment in elderly patients should be started if the SBP is ≥140 mmHg, and the target SBP values should be <140 mmHg. In patients aged ≥80 years, pharmaceutical anti-hypertensive treatment is commenced if SBP is ≥160 mmHg and the comorbidity burden is low. In this patient group, SBP should be reduced more slowly and cautiously, with the target SBP value of <150 mmHg, or <140 mmHg in patients with isolated systolic hypertension. In people aged ≥90 years, continuation of antihypertensive treatment is recommended, provided that the treatment has been well tolerated in previous years (between the ages of 80 and 90 years) and produced satisfactory results. Reasonable antihypertensive treatment in elderly patients involves a non-negligible risk of DBP reduction below 65 mmHg.18 While there is no optimum or target DBP value in the hypertension treatment process, a number of studies have demonstrated very low DBP values to be associated with increased risk of adverse events and cardiovascular incidents.19,20 This risk increases with age.21 DBP is also significantly affected by impaired compensation mechanisms in elderly patients, and by comorbidities such as ischaemic heart disease, kidney disease, or completed stroke, which affect the blood vessels. Undoubtedly, patients over the age of 85 are at increased risk of excessive DBP reduction, which may result in potentially serious health consequences or death.22

Frailty Definitions and Measures Multiple studies have shown that frail older people are at high risk of developing adverse outcomes such as disability,23,24 hospitalisation,23 institutionalisation,25 lower quality of life26,27 and premature death.28 However, there is still no consensus regarding the conceptual and operational definition of frailty.29–31 Fundamentally, frailty is a medical concept, and as a result, it is often defined in the context of problems in physical functioning. An example of such a definition is the one produced by Fried et al.23 These researchers define frailty as a “biologic syndrome of decreased reserve and resistance to stressors, resulting from cumulative declines across multiple physiologic systems, causing vulnerability to adverse outcomes.”23 Their operationalisation of frailty, the phenotype of frailty, is extensively used in both research and practice. The phenotype assesses frailty based on five criteria: physical inactivity, low walking speed, weight loss, exhaustion and low grip strength.23 The debate on frailty is mainly focused on whether frailty should be defined only in terms of physical factors or whether psychological and social factors should be included as well.32 According to Bergman et al.,30 frailty provides a conceptual basis for moving away from organ- and disease-based approaches towards a health-based, integrative approach. An integrative approach is important because it starts from a holistic point of view and thus regards how humans function as a whole organism; a partial view could lead to fragmentation of care33,34 and consequently to reduced quality of care being provided to frail older people. A definition of frailty that expresses this integrative approach is as follows: “Frailty is a dynamic state affecting an individual who experiences losses in one or more domains of human functioning (physical, psychological, social), which is caused by the influence of a range of variables and which increases the risk of adverse outcomes.”33 Recently, Sutton et al.29 identified 38 multi-component frailty measures. One of these measures is the frequently used and cited Frailty Index developed by Mitnitski et al.35 The Frailty Index is based on the cumulative deficit approach and proposes that frailty can be assessed by evaluating a large number of non-specified age-associated health deficits, usually at least 30.35,36 Because both the phenotype of frailty23 and the Frailty Index35 are

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Co-Morbidities difficult to put into practice in clinical or large epidemiological settings as they require objective measures implemented by trained staff and a clinical database with information regarding signs, symptoms and health problems,37 alternative frailty measures – mostly self-reported – have been recommended for use in clinical practice.38 Examples of self-reported questionnaires assessing frailty are: the FRAIL scale,39 the Groningen Frailty Indicator40 and the Tilburg Frailty Indicator (TFI)41. According to Sutton et al.,29 the TFI has been the most extensively examined in terms of psychometric properties and also has the most robust evidence of reliability and validity. Nevertheless, the definition of frailty and adverse outcomes that best suits the unique needs of researchers, healthcare professionals (such as clinicians, nurses, physiotherapists and occupational therapists) and policymakers conducting the assessment of frailty determines the choice of the appropriate measure of frailty.42

Care for Frail Patients Decreased exercise tolerance and progressive disability are manifestations of frailty. These signs largely affect the patient’s wellbeing and quality of life. Comorbidities, including hypertension, are also of significance. The way these patients function on a daily basis is difficult because of limitations in basic activities, decreased independence and progressive disability. Proper nutrition and motor rehabilitation should be prioritised in care for frail patients. Motor exercises can improve quality of life, physical fitness and prognosis with regard to comorbidities. The most effective solution is resistance training, which increases muscle strength and endurance. Proper nutrition should include high-energy meals rich in ingredients counteracting the progressive weakening of the body. Vitamin supplements can also be included, and protein deficiency must be compensated for.43 Elderly patients can benefit from an interdisciplinary approach to care. Important factors include early identification of polypharmacy by the treatment team, implementing appropriate treatment for comorbidities, identifying cognitive impairment and low mood, providing psychological support and preventing falls.44 In recent years, a number of prevention programs have been launched that promote screening tests enabling quick identification of patients who are frail or at risk of frailty such as the FRAIL scale39 and the TFI.41 Consistent prevention activity programs have become a priority for many health care facilities and teams. In accordance with the guidelines, screening for frailty should include all patients older than 70 years who have one or more of the following symptoms: significant weight loss in the preceding year, fatigability, overall weakness, and a decrease in physical activity interfering with normal activity.45 Frailty syndrome is undoubtedly a challenge for multidisciplinary teams providing health care for geriatric patients. In clinical practice, special attention should be paid to frail elderly patients, who should receive tailored treatment. Future activities in the field of frailty prevention and identification should include the development of screening tests and minimising the health impact of frailty, with particular attention paid to at-risk groups of patients. A key issue related to frailty is social awareness, as the consequences of the syndrome are both health-related and social. The latter include increased morbidity, more frequent hospitalisations, loss of one’s social position and roles, and the risk of social isolation. Social acceptance may play a significant role in adapting to changes imposed by frailty syndrome.46 Although no multi-centre studies exist that demonstrate differences in care for frail patients, the group certainly deserves more attention. As these patients are at a higher risk of complications following invasive procedures and of adverse effects from medication, they should be managed with extra caution.

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Orthostatic Hypotension Orthostatic hypotension (OH) results from an autonomic nervous system dysfunction, and involves maladjustment of the cardiovascular system to sudden changes in body position. The primary symptom of initial orthostatic hypotension (IOH) is a sudden drop of SBP by ≥20 mmHg, or of DBP by ≥10 mmHg, occurring within 3 minutes of standing. Another form of OH is delayed orthostatic hypotension (DOH). In this case, BP measurement should be performed 30 minutes after verticalisation. Differential diagnosis of DOH against vasovagal syndrome (VVS) is important. The syndrome accounts for approximately 40 % of BP drop incidents with syncope, while OH accounts for 10 %.47 In VVS, hypotension and/or bradycardia occur in response to an exaggerated autonomic reflex. This produces a loss of consciousness lasting up to 20 seconds. VVS diagnosis involves a tilt test during extended verticalisation (up to 45 minutes), thereby reproducing the syncope in the diagnostic laboratory. First-line treatment for patients at risk of VVS includes alpha-sympathomimetics (midodrine, etilefrine).48 Both VVS and OH are associated with patient age. The risk of OH increases in patients aged ≥65, and is found in approximately 30 % of the population.49 Elderly hypertensive patients are a special risk group for OH. Impaired cerebral circulation in this group is also a factor.50 Causes of OH in hypertensive patients include use of some anti-hypertensive drugs, mainly including diuretics, ACE inhibitors, CCBs, and alpha blockers.51 Symptoms preceding an OH incident include visual disorders, vertigo, dysarthria, and falling after verticalisation. In addition to antihypertensive treatment, factors contributing to OH include long-term immobility, exertion unadjusted to the patient’s age, and excessively large meals. Recurrent OH incidents increase the risk of CNS lesions and stroke. Because of the high risk of OH in elderly patients, bedside BP measurements are routinely performed. The procedure involves BP measurement 10 minutes after lying down, and 3 minutes (IOH) or 30 minutes (DOH) after verticalisation – this is called a verticalisation test. It should be performed in the morning. The time of day is significant because of the decreased blood volume because of nocturia, which also increases OH risk. The head-up tilt table test is also used in OH diagnosis. The patient lies on the bed for 20 minutes, after which the bed is gradually verticalised up to an 80-degree angle over a 3-minute period. During this simulated verticalisation, BP is measured continuously.52 Regularly recurring episodes of OH are a strict indication for treatment.53 Depending on symptom intensity, pharmaceutical and non-pharmaceutical treatment can be used. Non-pharmaceutical methods include recommendations for head elevation when lying down (approximately 10–12 cm) and slow and gradual verticalisation, with the patient maintaining a sitting position for approximately 30 seconds before standing up fully. Other recommendations include simple exercises enhancing circulation in the lower extremities, and avoiding leaning. Consumption of alcohol and large, heavy meals is inadvisable. The patient should increase their fluid intake up to approximately 2.5 litres per day. If non-pharmaceutical treatment is ineffective, oral pharmaceutical treatment should be implemented. First-line treatment is dihydroergotamine, increasing vascular tone. The starting dose is 5–10 mg twice daily. Other agents used in OH treatment include sympathomimetics, such as etilefrine (Effortil®), midodrine (Gutron®), and norfenefrine (Novadral®), which increase BP by stimulating alpha- and beta-adrenergic receptors. Another class of drugs used is mineralocorticoids, for example, fludrocortisone (Cortineff®). These drugs increase BP by increasing sodium retention.54 OH treatment is planned individually for each patient, taking into

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specialised motor rehabilitation aimed at increasing muscle strength and improving balance during daily activities.62

Cognitive Impairment Polypharmacy Polypharmacy is one of the 21st century’s great challenges in geriatrics. It involves the inappropriate and unnecessary administration of a large number of medications, some of which are potentially harmful and not medically indicated.56 This problem is closely associated with the elderly population. Societal ageing and the increasing number of patients aged ≥65 increases its prevalence. According to estimates, approximately 50 % of patients aged 65 take five or more oral medications daily, and 12 % take 10 or more.57 Polypharmacy contributes to adverse events and drug interactions. The risk of adverse events with two concurrent medications is approximately 5 %, but increases to 50 % with five concurrent medications. The risk of polypharmacy is increased by age-related factors, including the structural and functional changes in the body that contribute to slower metabolism and thus slower medication absorption. Because of the numerous comorbidities, treatment by more than one physician is often required. When pharmaceutical treatment is not appropriately supervised by the treatment team, multiple medications of the same type can be used, increasing the risk of adverse effects. Patient-related factors increasing the risk of polypharmacy include taking medication in a way other than prescribed, disregarding contraindications and adverse effects, as well as easy access to over-the-counter drugs and their abuse. Poor pharmaceutical management can also result from insufficient knowledge on the consequences of polypharmacy, both among patients and physicians. Drug interactions may result in enhanced or decreased effects, extended or shortened effect duration, or toxicity, resulting in abnormal heart rhythms, kidney and liver damage and CNS reactions.58

Falls Falls among elderly patients are strictly associated with ageing processes in the body, affecting the nervous system, the musculoskeletal system, vision and hearing, and blood vessels. Chronic diseases, including hypertension, deteriorate baroreceptor function and result in irreversible changes in cerebral and coronary circulation. The risk of sudden drops in BP also rises. Organ complications of chronic hypertension, ischaemic heart disease and heart failure increase the risk of syncope, which often results in falls.59 Anti-hypertensive treatment and medication side effects are also significant.60 Comorbidities and decreased mobility also contribute to falls in elderly patients. Besides multimorbidity associated with cerebral and coronary atherosclerosis, age itself – with the resulting decrease in posture stability – can contribute to falls resulting in injury that limits the patient’s independence and increases both their dependence on others and the risk of subsequent incidents.61 Each fall is a traumatic experience for the patient, producing what is called ‘post-fall syndrome’. This syndrome comprises psychological trauma and the fear of subsequent falls, resulting in further limitation of physical activity. The events can be triggered by sudden decreases in BP, which is why appropriate treatment choice is essential. Some drug groups are associated with a higher risk of adverse events. For example, diuretics often cause electrolyte disorders leading to arrhythmias, CNS disorders, impaired neuromuscular conductivity and decreased mobility. Treatment with sympatholytics can lead to cognitive impairment, which also contributes to the risk of falls. Fall risk can be limited with the appropriate anti-hypertensive treatment and

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Cognitive functions include a number of intellectual processes, such as short- and long-term memory, language processes (writing, reading, speaking), visual and spatial processes, abstract thinking and perceiving external stimuli. Normal cognitive function allows one to learn, remember and reproduce information, as well as to communicate it verbally or non-verbally. It also allows one to solve tasks, plan actions and make decisions. Overall, full cognitive function enables normal everyday bio-psycho-social functioning. Physiologically, ageing processes involve age-associated memory impairment or age-related cognitive decline.63 Currently, symptoms of dementia are found in 2–10 out of 1000 patients aged >70, and 20–40 out of 1000 patients aged ≥80. It becomes significantly more common with age, though it will not occur in 50 % of 85-year-olds, and therefore cannot be exclusively attributed to ageing.64 Approximately 50 % of patients are affected by Alzheimer’s disease, and approximately 10–15 % by vascular pathology. The risk of vascular pathology is increased by alcoholism, tobacco use, diabetes, hypercholesterolaemia, AF and hypertension. Initial reports on the impact of hypertension on vascular pathology were different. The association between high BP values and the development of vascular pathology was only confirmed in a long-term observation of BP before the development of cognitive impairment.65 In the literature, particular attention is paid to transient falls in BP that may contribute to CNS hypoperfusion, leading to the development of ischaemic lesions in elderly patients.66 At present, the association between hypertension and cognitive impairment in the elderly population is considered evidence-based.67

Adherence to Treatment in the Elderly Population High SBP or, to a lesser extent, high DBP, is common in the elderly population and is associated with an increased risk of cardiovascular disease, cerebrovascular disease, peripheral artery disease, cognitive impairment, and kidney disease. Moreover, elderly patients are at increased risk of hypertension-related abnormalities. Based on research, the European Societies recommend anti-hypertensive treatment in elderly patients (>80 years), if the treatment is well tolerated. However, studies on treatment effectiveness and adherence mainly involve younger patients, and the representation of elderly patients (aged ≥75 years) is insufficient. Moreover, the available publications offer no guidelines for the treatment of elderly patients diagnosed with geriatric syndrome or its components (frailty, cognitive impairment).68 Concurrent frailty and/or cognitive impairment in elderly patients is associated with a worse perception of health, increased number of comorbidities, and social isolation of the patient.23 It can also be suspected to interfere with treatment adherence.69 Few studies are available on the association between the components of geriatric syndrome and adherence to treatment. Those papers that discuss associations between frailty and adherence are based on populations with diseases other than hypertension.70,71 There is a discussion in the available literature regarding the impact of frailty syndrome on adherence.70,72 In a study by Jankowska et al., frailty was found in 63.9 % of hypertensive patients and was associated with worse adherence to anti-hypertensive treatment. Among factors negatively correlated with adherence, the authors identified being alone and some determinants

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Co-Morbidities of frailty in accordance with the TFI (such as being alone, death of a loved one, serious illness, serious illness of the partner, and divorce or ending a relationship in the preceding year).72 In a study by Koizumi et al.,73 frailty in hypertensive patients was associated with limited physical activity, lower body weight, difficulties in ingesting solid foods and performing daily activities, and limitations in performing complex activities of daily living, correlated with the prevalence, treatment and control of hypertension.73 In another study, Talegawkar et al.74 investigated associations between frailty and adherence to the Mediterranean diet, and found that non-frail patients were more compliant with dietary recommendations than frail patients.74 Contrary to the above, Chao et al. report better adherence in frail patients, though not in the hypertensive population.70 The authors link better adherence found in frail patients to their older age, stating that elderly patients pay more attention to their illness and symptoms, and are thus more compliant with the prescribed treatment plans. Other authors suggest that the differences in adherence between frail elderly patients and younger individuals can be associated with cognitive impairment and with the less accurate reporting of adherence by the elderly, which may be the cause of artificially high results.75 A similar discussion exists with regard to correlations between elderly age and adherence to anti-hypertensive treatment. Karakurt et al.76 and Jassim Al Khaja et al.77 report that patients above 70 years take their medication less consistently than younger patients. In research by Jankowska et al., younger age was associated with better reported health behaviours in the ‘health practices’ domain,78 but worse adherence to pharmaceutical treatment.79 Jackevicius et al.80 and Lam et al.81 report a correlation between younger age and better pharmaceutical adherence. On the other hand, some publications indicate older age as a predictor of better adherence to medication.82,83 In elderly patients, factors decreasing adherence can also include multimorbidity and polypharmacy, adverse effects of treatment, unfulfilled expectations regarding treatment outcomes, and adverse drug interactions. Treatment outcomes do not always match patient expectations, which can result in discontinuation of the prescribed medication. As to the better adherence to treatment found in elderly patients, it has been explained by the presence of comorbidities, which makes patients

nypl K. Definition and division of hypertension – stance of K The Writing Group of the American Society of Hypertension 2005. Guide for GPs 2005;5:101–5. 2. Zdrojewski T, Bandosz P, Rutkowski M, et al. Dissemination, detection and efficacy of treatment of hypertension in Poland: results of NATPOL study 2011. Arter Hypertens 2014;18:116–7. 3. Lloyd-Jones DM, Evans JC, Levy D. Epidemiology of hypertension in the old-old: data from the community in the 1990s. Am J Hypertens 2004;17:200. DOI: 10.1016/j.amjhyper.2004.03.531 4. Nichols WW, Nicolini FA, Pepine CJ. Determinants of isolated systolic hypertension in the elderly. J Hypertens 1992;10 Suppl 6:73–7. DOI: 10.1097/00004872-199208001-00020; PMID: 1432333 5. Zdrojewski T, Więcek A, Grodzicki T, et al. Dissemination, awareness and effectiveness of treatment of hypertension in people over 65 in Poland. In: medical, psychological, sociological and economic aspects of aging in Poland. Termedia Medical Publishers, Poznań, 2012:155–68. 6. Williams B. Recent hypertension trials. Implications and controversies. J Am Coll Cardiol 2005;45:813–27. DOI: 10.1016/ j.jacc.2004.10.069; PMID: 15766813 7. Messerli FH, Panjrath GS. The J-curve between blood pressure and coronary artery disease or essential hypertension: exactly how essential? J Am Coll Cardiol 2009;54:1827–34. DOI: 10.1016/j.jacc.2009.05.073; PMID: 19892233 8. Wieczorowska-Tobis K. Organ changes in the process of aging. Pol Arch Intern Med 2008;118 Suppl:63–8. 9. Yavuz BB, Yavuz B, Sener DD, et al. Advanced age is associated with endothelial dysfunction in healthy elderly subjects. Gerontol 2008;54:153–6. DOI: 10.1159/000129064; PMID: 18441522 10. Knapowski J, Wieczorowska–Tobis K, Witowski J. Pathophysiology of ageing. J Physiol Pharmacol 2002;53:135–46.

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perceive themselves as very ill and take the prescribed treatment seriously.84 Identifying the causes of non-adherence to pharmaceutical treatment is a key factor in planning therapeutic interventions aimed at increasing control, preventing complications, and improving long-term outcomes and any adverse effects of treatment. Precise identification of contributors to low medication adherence is crucial for improving treatment effectiveness and for distinguishing those patients in need of additional supervision in order to decrease the risk of complications from untreated hypertension. Diagnosis of frailty and of the associated difficulties in adhering to treatment allows for targeting the elderly patients with a poorer prognosis and at risk of complications from untreated or undertreated hypertension, and for planning interventions to improve hypertension control.

Conclusions The importance of high BP and the effect of lowering BP in older adults remain controversial because of the mixed evidence in this population. For frail elderly patients, consider starting treatment if the SBP is 160 mmHg or higher. If the patient is severely frail and has a short life expectancy, a SPB target of 160–190 mmHg may be reasonable. If the SBP is below 140 mmHg, anti-hypertensive medications can be reduced as long as they are not indicated for other conditions. In general, no more than two anti-hypertensive medications should be prescribed to avoid unnecessary administration of a large number of medications. There is little direct evidence to inform the risks and benefits of using anti-hypertensive medications to treat chronic health conditions when significant frailty is present. Since the frail elderly are vulnerable to poor health outcomes, it is important to assess the risk/benefit ratio of healthcare interventions, including drug therapy. Future clinical trials need to consider modifications to safely include frail older adults, and treatment recommendations for hypertension, specific to the frail elderly, should consider inclusion of evidence beyond randomised controlled trials. Management of hypertension in frail elderly people is a newly emerging problem, and it should be pointed out that work on frailty in this context will only be relevant if effective health promotion, prevention, treatment, rehabilitation, and care interventions can be identified. ■

PMID: 12120891 11. D avies DF, Shock NM. Age changes in glomerular filtration rate, effective renal plasma flow and tubular excretory capacity in adult males. J Clin Invest 1950;29:496–507. DOI: 10.1172/JCI102286; PMID: 15415454 12. Fliser D, Franek E, Joest M, et al. Renal function in the elderly: impact of hypertension and cardiac function. Kidney Int 1997;51:1196–204. DOI: 10.1038/ki.1997.163; PMID: 9083286 13. Tack J, Vantrappen G. The aging oesophagus. Gut 1997;41: 422–4. DOI: 10.1136/gut.41.4.422 14. Lovat LB. Age related changes in gut physiology and nutritional status. Gut 1996;38:306–9. DOI: 10.1136/ gut.38.3.306; PMID: 8675079 15. Grodzicki T, Gryglewska B, Tomasik T, et al. Principles of hypertension in elderly patients. Pol Gerontol 2012;20:130–34. 16. Budzińska K. The influence of aging on skeletal muscle biology. Pol Gerontol 2005;13:1–7. 17. Briggs AM, Greig AM, Wark JD, et al. A review of anatomical and mechanical factors affecting vertebral body integrity. Int J Med Sci 2004;1:170–80. DOI: 10.7150/ijms.1.170; PMID: 15912196 18. Working Group of the European Society of Hypertension (ESH) and the European Society of Cardiology (ESC) for the management of hypertension. ESH / ESC guidelines for management of hypertension in 2013. Pol Cardiol 2013;71 Suppl III: 43–51. 19. Somes GW, Pahor M, Shorr RI, et al. The role of diastolic blood pressure when treating isolated systolic hypertension. Arch Intern Med 1999;159:2004–9. DOI: 10.1001/archinte.159.17.2004; PMID: 10510985 20. Fagard RH, Van den Enden M, Leeman M, et al. Survey on treatment of hypertension and implementation of WHOISH risk stratification in primary care in Belgium. J Hypertens 2002;20:1297–302. DOI: 10.1097/00004872-200207000-00015; PMID: 12131525

21. B outitie F, Gueyffier F, Pocock S, et al. INDANA Project Steering Committee: Individual data analysis of antihypertensive intervention. J-shaped relationship between blood pressure and mortality in hypertensive patients: new insights from a meta-analysis of individual patient data. Ann Intern Med 2002;136:438–48. DOI: 10.7326/0003-4819-136-6200203190-00007; PMID: 11900496 22. Wąsowski M, Marcinowska-Suchowierska E. Nadciśnienie tętnicze – odrębności diagnostyczne i terapeutyczne w wieku podeszłym. Post Nauk Med 2011;XXIV (5):385. 23. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146–56. DOI: 10.1093/gerona/56.3.M146; PMID: 11253156 24. Boyd CM, Xue QL, Simpson CF, et al. Frailty, hospitalization, and progression of disability in a cohort of disabled older women. Am J Med 2005;118:1225–31. DOI: 10.1016/ j.amjmed.2005.01.062; PMID: 16271906 25. Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ 2005;173:489–95. DOI: 10.1503/cmaj.050051; PMID: 16129869 26. Gobbens RJ, van Assen MA. The prediction of quality of life by physical, psychological and social components of frailty in community-dwelling older people. Qual life Res 2014;23:2289–300. DOI: 10.1007/s11136-014-0672-1; PMID: 24671672 27. Bilotta C, Bowling A, Case A, et al. Dimensions and correlates of quality of life according to frailty status: a crosssectional study on community-dwelling older adults referred to an outpatient geriatric service in Italy. Health Qual Life Outcomes 2010;8:56. DOI: 10.1186/1477-7525-8-56; PMID: 20529325 28. Shamliyan T, Talley KM, Ramakrishnan R, et al. Association of frailty with survival: a systematic literature review. Ageing Res Rev 2013;12:719–36. DOI: 10.1016/j.arr.2012.03.001; PMID:

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update 2004. Europace 2004;6:467–537. DOI: 10.1016/ j.eupc.2004.08.008; PMID: 15519256 49. B anach M. Aktualny stan wiedzy na temat hipotonii. Med Rodz 2004;6:246–50. 50. Grześkowiak A, Rojek A, Szyndler A, et al. Częstość hipotonii ortostatycznej u leczonych chorych z nadciśnieniem tętniczym. Nadciśn Tętn 2005;9:452. 51. Poon IO, Braun U. High prevalence of orthostatic hypotension and its correlation potentially causative medications among elderly veterans. J Clin Pharm Ther 2005;30:173–8. DOI: 10.1111/j.1365-2710.2005.00629.x; PMID: 15811171 52. Brignole M, Alboni P, Benditt D, et al. Guidelines on management (diagnosis and treatment) of syncope. Task Force on Syncope, European Society of Cardiology. Eur Heart J 2001;22:1256–306. DOI: 10.1053/euhj.2001.2739; PMID: 11465961 53. Mansoor GA. Orthostatic hypotension due to autonomic disorders in the hypertension clinic. Am J Hypertens 2006; 19:319–26. DOI: 10.1016/j.amjhyper.2005.09.019; PMID: 16500521 54. Herold G. Medycyna Wewnętrzna - Przewlekłe niedociśnienie krwi tętniczej i hipotonia ortostatyczna. Wydawnictwo Lekarskie PZWL, Warszawa 2001;326–31. 55. Tykocki T, Guzek K, Nauman P. Hipotonia ortostatyczna i nadciśnienie tętnicze w pozycji leżącej w pierwotnych zaburzeniach autonomicznych. Patofizjologia, diagnostyka i leczenie. Kard Pol 2010;68:1057–63. 56. Nobili A, Licata G, Solerno F, et al. Polypharmacy, length of hospital stay, and in hospital mortality among elderly patients in internal medicine wards. The REPOSI study. Eur J Clin Pharmacol 2011;67:507–19. DOI: 10.1007/s00228-010-0977-0; PMID: 21221958 57. Kaufman DW, Kelly JP, Rosenberg L, et al. Recent patterns of medication use in the ambulatory adult population of the United States. JAMA 2002;287:337–44. DOI: 10.1001/ jama.287.3.337; PMID: 11790213 58. Fulton MM, Allen ER. Polypharmacy in the elderly: A literature review. J Am Acad Nurse Pract 2005;17:123–32. DOI: 10.1111/j.1041-2972.2005.0020.x; PMID: 15819637 59. Hausdorf JM, Herman T, Baltadjieva R, et al. Balance and gait in older adults with systemic hypertension. Am J Cardiol 2003;91:643–5. DOI: 10.1016/S0002-9149(02)03332-5; PMID: 12615286 60. Verhaeverbeke I, Mets T. Drug-induced orthostatic hypotension in the elderly. Drug Saf 1997;17:105–18. DOI: 10.2165/00002018-199717020-00003; PMID: 9285201 61. Fletcher PC, Hirdes JP. Restriction in activity associated with fear of falling among community-based seniors using home care services. Age Ageing 2004;33:273–9. DOI: 10.1093/ageing/ afh077; PMID: 15082433 62. Żak M, Gryglewska B. Upadki pacjentów geriatrycznych z nadciśnieniem tętniczym — ocena ryzyka dokonywana po roku od upadku. Nadciśn Tętn 2005;9:112–6. 63. Ishizaki T, Yoshida H, Suzuki T, et al. Effects of cognitive function on functional decline among community-dwelling non-disabled older Japanese. Arch Gerontol Ger 2006;42:47–58. DOI: 10.1016/j.archger.2005.06.001; PMID: 16081171 64. Johnston SV, O’Meara ES, Manolio TA, et al. Cognitive impairment and are associated with carotid artery disease in patients without clinically evident cerebrovascular disease. Ann Intern Med 2004;140:237–47. DOI: 10.7326/0003-4819-140-4200402170-00005; PMID: 14970146 65. Kilander L, Nyman H, Boberg M, et al. Hypertension is related to cognitive impairment: a 20-year follow-up study of 999 men. Hypertension 1998;31:780–6. DOI: 10.1161/01. HYP.31.3.780; PMID: 9495261 66. Eguchi K, Kario K, Hoshide S, et al. Greater change of orthostatic blood pressure is related to silent cerebral infarct and cardiac overload in hypertensive subjects. Hypertens Res 2004;27:235–41. DOI: 10.1291/hypres.27.235; PMID: 15127880 67. Elias ME, Wolf PA, D’Agostino RB. Untreated blood pressure level is inversely related to cognitive functioning: the Framingham Study. Am J Epidemiol 1993;138:353–64. DOI:

10.1093/oxfordjournals.aje.a116868; PMID: 8213741 68. F erri C, Ferri L, Desideri G. Management of hypertension in the elderly and frail elderly. High Blood Press Cardiovasc Prev 2017; 24:1–11. DOI: 10.1007/s40292-017-0185-4; PMID: 28181201 69. Corrao G, Rea F, Ghirardi A, et al. Adherence with antihypertensive drug therapy and the risk of heart failure in clinical practice. Hypertension 2015;66:742–9. DOI: 10.1161/ HYPERTENSIONAHA.115.05463; PMID: 26222709 70. Chao CT, Huang JW. COGENT (COhort of GEriatric Nephrology in NTUH) study group Geriatric syndromes are potential determinants of the medication adherence status in prevalent dialysis patients. Peer J 2016;14:e2122. DOI: 10.7717/peerj.2122; PMID: 27326380 71. Sheppard VB, Faul LA, Luta G, et al. Frailty and adherence to adjuvant hormonal therapy in older women with breast cancer: CALGB protocol 369901. J Clin Oncol 2014;32:2318–27. DOI: 10.1200/JCO.2013.51.7367; PMID: 24934786 72. Jankowska-Polańska B, Dudek K, Szymanska-Chabowska A, et al. The influence of frailty syndrome on medication adherence among elderly patients with hypertension. Clin Interv Aging 2016;11:1781–90. DOI: 10.2147/CIA.S113994; PMID: 27994444 73. Koizumi Y, Hamazaki Y, Okuro M, et al. Association between hypertension status and the screening test for frailty in elderly community-dwelling Japanese. Hypertens Res 2013;36:639–44. DOI: 10.1038/hr.2013.7; PMID: 23446774 74. Talegawkar SA, Bandinelli S, Bandeen-Roche K, et al. A higher adherence to a Mediterranean-style diet is inversely associated with the development of frailty in communitydwelling elderly men and women. J Nutr 2012;142:2161–6. DOI: 10.3945/jn.112.165498; PMID: 23096005 75. Wu YH, Liu LK, Chen WT, et al. Cognitive Function in Individuals With Physical Frailty but Without Dementia or Cognitive Complaints: Results From the I-Lan Longitudinal Aging Study. J Am Med Dir Assoc 2015;16:899.e9–16. DOI: 10.1016/j.jamda.2015.07.013; PMID: 26321467 76. Karakurt P, Kaşikçi M. Factors affecting medication adherence in patients with hypertension. J Vasc Nurs 2012;30:118–26. DOI: 10.1016/j.jvn.2012.04.002; PMID: 23127428 77. Jassim Al Khaja KA, Sequeira RP, Mathur VS. Rational pharmacotherapy of hypertension in the elderly: analysis of the choice and dosage of drugs. J Clin Pharm Ther 2001;26:33–42. DOI: 10.1111/j.1365-2710.2001.00324.x; PMID: 11286605 78. Jankowska-Polańska B, Blicharska K, Uchmanowicz I, et al. The influence of illness acceptance on the adherence to pharmacological and non-pharmacological therapy in patients with hypertension. Eur J Cardiovasc Nurs 2016;5:559–68. DOI: 10.1177/1474515115626878; PMID: 26743263 79. Jankowska-Polańska B, Chudiak A, Uchmanowicz I, et al. Selected factors affecting adherence in the pharmacological treatment of arterial hypertension. Patient Prefer Adherence 2017;11:363–71. DOI: 10.2147/PPA.S127407; PMID: 28280309 80. Jackevicius CA, Mamdani M, Tu JV. Adherence with statin therapy in elderly patients with and without acute coronary syndromes. JAMA 2002;288:462–7. DOI: 10.1001/ jama.288.4.462; PMID12132976 81. Lam PW, Lum CM, Leung MF. Drug non-adherence and associated risk factors among Chinese geriatric patients in Hong Kong. Hong Kong Med J 2007;13:284–92. PMID: 17664533 82. Krousel-Wood M, Thomas S, Muntner P, et al. Medication adherence: a key factor in achieving blood pressure control and good clinical outcomes in hypertensive patients. Curr Opin Cardiol 2004;19:357–362. DOI: 10.1097/ 01.hco.0000126978.03828.9e; PMID: 15218396 83. Carter BL, Foppe van Mil JW. Comparative effectiveness research: evaluating pharmacist interventions and strategies to improve medication adherence. Am J Hypertens 2010;23:949– 55. DOI: 10.1038/ajh.2010.136; PMID: 20651698 84. Burnier M. Medication adherence and persistence as the cornerstone of effective antihypertensive therapy. Am J Hypertens 2006;19:1190–96. DOI: 10.1016/ j.amjhyper.2006.04.006; PMID: 17070434

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Dilemmas in the Dosing of Heart Failure Drugs: Titrating Diuretics in Chronic Heart Failure David Pham and Justin L Grodin Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA

Abstract Despite advances in medical therapy over the past few decades, the incidence of heart failure hospitalisation continues to rise. Diuretics are the most common therapy used to treat heart failure as they relieve congestion. However, there is a lack of guidance on how to best use these medications. Guidelines support the use of diuretics at the lowest clinically effective dose but do not specify a diuretic strategy beyond that. Here we review the diuretics available for treatment, potential mechanisms of diuretic resistance and ways to address this in the ambulatory setting, and review tools that have been developed to help guide diuretic use in the treatment of chronic heart failure.

Keywords Loop diuretic, furosemide, torsemide, bumetanide, diuretic resistance, heart failure. Disclosure: The authors have no conflicts of interest to declare. Received: 30 July 2017 Accepted: 12 September 2017 Citation: Cardiac Failure Review 2017;3(2):108–12. DOI: 10.15420/cfr.2017:10:1 Correspondence: Justin L. Grodin, MD, MPH, 5323 Harry Hines Blvd, Dallas, TX 75390-9047, USA. E: justin.grodin@utsouthwestern.edu

Introduction Advances in heart failure medical therapy over the past few decades have improved the prognosis of patients with this condition. Despite this, heart failure remains a significant burden to the medical system as the incidence of heart failure hospitalisation continues to rise.1 Diuretics have been a mainstay of therapy in heart failure to relieve congestion and improve symptoms. Despite the widespread use of diuretics, there is a lack of guidance on how to best titrate these medications in chronic use. Guidelines support the use of diuretics at the lowest clinically effective dose, but do not specify a diuretic strategy beyond that.2 Here we review the diuretics available for use in heart failure, potential mechanisms of diuretic resistance and ways to address this in the ambulatory setting, and review tools that have been developed with the goal to help guide diuretic use to treat patients with chronic heart failure.

Loop Diuretics Loop diuretics remain the diuretic of choice for treating patients with heart failure.3 Furosemide, torsemide and bumetanide are the agents widely available for clinical use, with furosemide the predominant agent of the three. All three loop diuretics are available in oral formulation and are first absorbed in the gastrointestinal track. Once absorbed, the majority of the diuretic becomes protein bound in the vascular space, which in turn requires the drug to be transported into the nephron by organic anion transporters.4 Loop diuretics then travel to the ascending loop of Henle and inhibit the Na+/2Cl/K+ cotransporter to block reabsorption of sodium and chloride, resulting in natriuresis. Loop diuretics also induce renal prostaglandin synthesis, which results in renal and peripheral vascular smooth muscle relaxation and venodilation.5 The dose–response curve is sigmoidal, demonstrating that the drug concentration must reach a diuretic threshold to have an effect, and further diuresis above this threshold is achieved by increased frequency of administration rather than increased drug concentration.5

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There are key pharmacokinetic differences between the loop diuretics (Table 1). Torsemide and bumetanide have an oral bioavailability of 80–100 %, while furosemide has a wide variant bioavailability of 10–100 %.6 Ingestion of food also has an effect on pharmacokinetics as it can decrease the maximum concentration of loop diuretics by one-half and increase the time to peak serum concentration by 30–60 min.7–9 The effect of food intake on the impairment of diuretic absorption is greater with furosemide and bumetanide, whereas torsemide’s bioavailability is relatively unchanged by food intake. The overall rate of absorption is also negatively affected when the patient is congested.10,11 In patients with chronic renal insufficiency, furosemide has been shown to have a variable dose response compared with a more consistent dose effect with bumetadine due to altered metabolism of furosemide in patients with kidney disease.12 With the oral formulations, furosemide has a half-life of 2 h, bumetanide has a half-life of 1 h, and torsemide has the longest half-life at 3.5 h.13 Furosemide is the most common loop diuretic prescribed but has a bioavailability that can be quite variable between similar patients as well as within the same patient during different disease states. This may be due to pharmacological factors inherent to furosemide and genetic differences between individuals as well.14,15 Despite the variable bioavailability, furosemide is commonly the first loop diuretic prescribed to patients with heart failure. However, there are few adequately powered or designed studies assessing the comparative effectiveness of these loop diuretics. The Torsemide in Chronic Heart Failure study is the largest study to date comparing furosemide to torsemide.16 The study found that after an average follow up period of 9 months in 1,377 patients, those in the torsemide group had a risk reduction in overall mortality, reduction in cardiac mortality and improvement in functional status. Unfortunately, the study was a non-randomised prospective cohort as it was a postmarket surveillance of safety, with surprisingly low use of beta-

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Dilemmas in the Dosing of HFrEF Drugs: Titrating Diuretics in Chronic Heart Failure blockers and angiotensin converting enzyme inhibitors.16 Torsemide in Chronic Heart Failure was also an open label study, so the comparison between torsemide and furosemide was not without bias. Other smaller studies support the benefits of torsemide over furosemide, suggesting less heart failure-related hospital days but no differences in hospitalisations due to heart failure.17,18 Some of these plausible benefits might result from torsemide’s aldosterone antagonistic properties.19,20 Practically speaking, the use of torsemide is reserved in patients that have demonstrated some degree of diuretic resistance to furosemide. However, doubts remain regarding its impact on clinical outcomes. Bumatenide, on the other hand, is even less well studied and its impact on clinical outcomes in comparison to furosemide is still unclear.21

Loop Diuretic Resistance A diminished response to loop diuretics, or diuretic resistance, can lead to an adverse clinical course and prolong hospital stays. Diuretic resistance occurs frequently, with data from the Prospective Randomized Amlodipine Survival Evaluation study reporting that up to 25 % of participants displayed diuretic resistance, defined as absolute diuretic dose above the population median dose.3 This is a common clinical scenario that requires an increase in diuretic dose for decongestion. Another metric of diuretic resistance is diuretic efficiency, which is the net fluid loss per mg of diuretic. It may be a more precise measurement of diuretic resistance then absolute diuretic dose and low diuretic efficiency has a stronger association with mortality than does absolute diuretic dose.22 Although loop diuretics have not demonstrated a mortality benefit in heart failure, evidence of diuretic resistance carries a poor prognosis with a higher predicted mortality and increased risk of readmissions for heart failure.22–25 Mechanisms to explain diuretic resistance are multifactorial. In a congested state, aspects of gut wall oedema, decreased splanchnic blood flow and decreased intestinal motility due to an increased sympathetic state all lead to a delayed time to maximum peak and decreased peak concentration of the drug.10 In patients with renal insufficiency, other organic acids such as blood urea nitrogen can compete with loop diuretics for transport by the organic anion transporters, with less drug therefore reaching the site of action. This decrease in drug concentration results in a failure to reach the diuretic threshold concentration needed for the drug to be effective. Further changes in sodium handling in response to loop diuretics also contribute to diuretic resistance. During periods of decreased drug levels between diuretic doses there is a rebound in sodium reabsorption that has been termed a ‘post-diuretic effect’. 26 A ‘braking phenomenon’ has also been described after chronic diuretic use due to renal adaptation. Hypertrophy of cells in the distal convoluted tubules, away from the site of action of loop diuretics, leads to increased efficiency of sodium reabsorption and decreases the effect of loop diuretics.25 And lastly, other drugs can contribute to diuretic resistance. Non-steroidal anti-inflammatory drugs are common over-the-counter medications that clinicians should ask their patients about as patients commonly view them as benign due to their availability but they can contribute to diuretic resistance as well as increased risk of hospitalisations.27 Diuretic resistance and decreased response to loop diuretics is, unfortunately, not uncommon in clinical practice. Guidelines do not dictate which loop diuretic to use, only to use the lowest dose

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Table 1: Properties of Loop Diuretics

Furosemide Torsemide Bumetanide

Relative intravenous potency (mg)

Oral : intravenous dosing

1 : 2

1 : 1

Bioavailability (%)

10–100

80–100

Drug half-life (h)

1.5–2.0

3–4

1.0–1.5

6–8

6–16

4–6

Duration of effect (h)

Reproduced from Felker & Mentz,6 with permission from Elsevier.

possible to achieve the desired effect.2 As diuretic resistance becomes increasingly suspected due to a decreasing response to a stable dose of loop diuretic, the diuretic dose can be increased in an effort to achieve comparably efficacious natriuresis. Another option at this time can also be to change from one loop diuretic to another with the hope that better pharmacokinetics between the loop diuretics can achieve the desired effect. Changing from furosemide to torsemide is common due to more consistent bioavailability of torsemide and the longer half-life of torsemide can potentially counter the ‘post-diuretic’ effect seen with furosemide.

Ambulatory Intravenous Loop Diuretics Another growing practice used to counter resistance to increasing oral doses of loop diuretic is to administer intravenous loop diuretics in the ambulatory setting. This strategy has been utilised by several centres to help reduce hospitalisations, specifically targeting those patients that would only require one or two doses of intravenous diuretic to achieve euvolemia. Preliminary reported experiences so far have demonstrated this as a safe and effective way to decongest hemodynamically stable patients and potentially reduce hospitalisations for heart failure and overall healthcare costs.28–30 These outpatient heart failure units may therefore be useful to address those stable patients who appear to have diuretic resistance not surmountable by oral doses and just need some decongestion in order to respond to oral doses again. Furthermore, these centers also present another opportunity to involve a multidisciplinary care team as these patients are being monitored for a few hours while being given intravenous diuretics.

Thiazide Diuretics When higher doses of a loop diuretic or changing loop diuretics are not achieving adequate decongestion, adding non-loop diuretics is an additional strategy – commonly referred to as ‘sequential nephron blockade’. Thiazide diuretics and metolazone are frequently utilised with loop diuretics to achieve this sequential blockage as thiazide diuretics inhibit the Na+Cl– cotransporter in the distal convoluted tubule. This can counter sodium reabsorption that occurs with the increased sodium load being delivered to the distal convoluted tubule after loop diuretic administration, as well as counter the increased sodium transport capacity or ‘braking phenomenon’ that occurs with chronic loop diuretic use. Although the use of loop and thiazide diuretics is common clinical practice, the majority of the data on this combination diuretic use is limited to small observational trials or case studies.31 Despite this, the evidence in these studies supports the use of combination diuretic therapy in those on high dose loop diuretics demonstrating diuretic resistance.31 Common diuretics used to augment loop diuretics are metolazone, hydrochlorothiazide, chlorothiazide and bendroflumethiazide. Metolazone is commonly used for combination therapy, but no study thus far has demonstrated one

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Clinical Practice thiazide-type drug as being superior to another in augmenting diuresis and this appears to be a class effect.31,32 This strategy does have risks, as a more robust diuresis with combination diuretic therapy can lead to electrolyte abnormalities including hypokalemia and, less commonly, hyponatremia and hypochloremia. The increase in decongestion can also worsen renal function if hypovolemia develops. Due to this, frequent monitoring of electrolytes and renal function is paramount when this strategy is used. When starting combination therapy in the outpatient setting, starting with a low dose of metolazone such as 2.5 mg (or equivalent dosing of another thiazide-type diuretic) two to three times a week and close monitoring to assess response and monitor for adverse events is warranted.31 Similar to loop diuretics, the concentration of thiazide diuretics must be adequate enough to cross a threshold for effectiveness. After the initial dose of thiazide diuretic, if there is not an augmentation in diuretic response, a higher dose of thiazide diuretic is then warranted. Although it is common clinical practice to administer the thiazide-type diuretic at least 30 min prior to the loop diuretic, evidence is lacking with regard to this practice as most studies administered the diuretics simultaneously.31

Mineralocorticoid Receptor Antagonists Mineralocorticoid receptor antagonists (MRA), such as spironolactone and eplerenone, are common medications used in chronic heart failure to reduce adverse clinical outcomes.33–35 However, the doses commonly used and studied in these landmark trials have minimal diuretic effect, with their prognostic benefits likely resulting from their neurohormonal antagonism. In contrast, small studies have suggested that using doses in the range of 100–200 mg spironolactone daily may increase diuretic efficacy in those thought to have diuretic resistance and improve symptoms of congestion.36–38 The Aldosterone Targeted NeuroHormonal Combined with Natriuresis Therapy – Heart Failure study evaluated spironolactone 100 mg versus placebo in hospitalised patients with acute decompensated heart failure and did not find differences in their primary end point between NT pro B-type natriuretic peptide levels or their secondary outcome of dyspnea relief, clinical congestion, net urine output or weight loss between standard of care and high-dose spironolactone.39 Of note, there was no difference in the safety outcomes of incidence of hyperkalemia or change from baseline estimated glomerular filtration rates between the placebo and high-dose spironolactone groups at 96 h. Although this study demonstrated that high doses of spironolactone were reasonably well tolerated, the use of high-dose MRAs for treating diuretic resistance remains unclear. Although uncommon, the evolution of hyperkalemia remains a strong consideration with MRA use. Estimations suggest that the incidence rates in heart failure patients using MRAs range from 5 to 15 %.40–42

Vasopressin Antagonists Vasopressin antagonists have also been considered for use in those with diuretic resistance. This class of drug blocks the effects of vasopressin on the aquaporin V2 receptor at the cortical collecting duct, leading to an increase in free water excretion or ‘aquaresis’. In the Efficacy of Vasopressin Antagonism Heart Failure: Outcome Study with Tolvaptan, the investigators evaluated the addition of tolvaptan 30 mg daily to intravenous loop diuretics in hospitalised patients for heart failure. There was an increase in weight reduction and lower discharge weight in the tolvaptan group, but no evidence of long-term benefit in morbidity or mortality.43 When tolvaptan was started in addition to chronic loop diuretic therapy in ambulatory patients with heart failure, a dose-dependent weight loss was

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observed early on, but this failed to translate into any long-term improvement in congestion with continued therapy.44,45 As a result of these disappointing findings in combination with two recent null clinical trials attempting to elucidate the use of tolvaptan as part of an additional strategy for the management of acute decompensated heart failure, the role of tolvaptan for the routine management of congestion remains in question.46,47

Strategies to Guide Diuretic Titration Heart failure is the leading cause of hospitalisation in the US and Europe; from 2001 to 2009, more than 1 million patients were hospitalised with heart failure as the primary diagnosis, comprising 1–2 % of all hospitalisations.48 This, in addition to the high rate of readmission after heart failure hospitalisation, presents a significant burden to the healthcare system. Optimising diuretic therapy in heart failure patients is a challenging task. Not surprisingly, there has been an increase in research to investigate complementary tools to help clinicians monitor and adjust diuretics, as well as other therapies to avoid congestion and prevent subsequent hospitalisations. Here we review several recent advances, such as ambulatory hemodynamic monitoring both invasive and non-invasive, and the emergence of subcutaneous furosemide as a potential option for patients in the future. Many patients with heart failure have implantable cardioverter devices in place, and these devices allow for the collection of additional clinical data such as heart rate variability, patient activity and arrhythmias. These devices are also capable of calculating intrathoracic impedance, which is a characteristic of the electrical current that can pass from the device case to the right ventricular electrode.49 As pulmonary congestion increases, the device senses a drop in electrical impedance. Not surprisingly, there is an inverse correlation between the intrathoracic impedance with both pulmonary capillary wedge pressure and net fluid loss during an acute hospitalisation.49 This information, in addition to other parameters measured from the device, may identify patients at risk for heart failure events, heart failure hospitalisations or 30-day rehospitalisations after discharge. However, whether thoracic impedance might guide diuretic titration and or impact doses in other heart failure therapies still has no clear impact on rehospitalisations or clinical outcomes.50–53 Direct pulmonary artery pressure device monitors have had promising results in the management of heart failure. The CardioMEMSTM device by St Jude Medical (Atlanta, GA, USA) is placed in the pulmonary artery during a right heart catheterisation and allows for measurement of pulmonary artery pressures. The CardioMEMSTM Heart Sensor Allows Monitoring of Pressure to Improve Outcomes in NYHA class III Heart Failure Patients (CHAMPION) study evaluated this device. In that study, clinicians had access to the treatment group’s pulmonary artery pressure results but not the pressure results of the control group.54 Patients in the treatment group had a 28 % relative risk reduction in the primary end point of heart failure hospitalisations at 6 months, and over the entire study period of 15 months there was a 37 % relative risk reduction in heart failure hospitalisation in the treatment group.54 One caveat when interpreting these results is that the treatment group had additional clinical contact and support due to having a device implanted, which may have a had a confounding effect on clinical outcome.55 Post-marketing efficacy studies are ongoing, testing whether the benefits in the CardioMEMSTM device translate into improvement in real-world patient care.56 For example, a recent retrospective cohort study observed a reduction in heart

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Dilemmas in the Dosing of HFrEF Drugs: Titrating Diuretics in Chronic Heart Failure failure hospitalisations in the 6 months after CardioMEMS implantation compared with the 6 months prior to implantation, supporting the results of the CHAMPION trial on the use of the device in addition to standard of care.57 At this point, however, the therapeutic role of the CardioMEMS device continues to evolve. In contrast to invasive implantable monitors, non-invasive systems have been developed to assess pulmonary congestion and avoid acute decompensations. An electromagnetic energy-based technology (Remote Dielectric Sensing, ReDSTM) non-invasively measures the dielectric properties of tissues using low-power electromagnetic signals. This is performed non-invasively as patients wear a vest for a few minutes in which this measurement is recorded to give an indication of the fluid content in the lungs, a method that has been validated in prior studies using chest computed tomography as a comparator.58 A recent small study using 50 patients shows that in patients recently discharged for decompensated heart failure, outpatient therapy guided by the ReDSTM device results appears to decrease the rate of heart failure rehospitalisation.59 A larger multicentre trial is ongoing to further confirm these preliminary findings.60 Non-invasive lung impedance monitoring is also being investigated as a surrogate to monitor for pulmonary edema. A recent moderate-sized randomised control trial performed at two centres demonstrated that patients who had their heart failure therapies guided by lung impedance measurements at outpatient visits over 1 year had a reduction in hospitalisations for heart failure.61 If proven effective in improving clinical outcomes, these devices can potentially serve as another tool available for clinicians to monitor patients in the ambulatory setting and provide additional objective data to assist in titrating diuretics. However, non-invasive assessments, whether through measurement of electrical impedance, lung impedance or dielectric properties, with routine care have yet been shown to improve hard clinical outcomes in patients with heart failure. Nevertheless, these methods do provide additional insight that may guide chronic decongestion strategies.

ang J, Mensah GA, Croft JB, Keenan NL. Heart failureF related hospitalization in the US, 1979 to 2004. J Am Coll Cardiol 2008;52:428–34. DOI: 10.1016/j.jacc.2008.03.061; PMID:18672162 2. Yancy CW, Jessup M, Bozkurt B, et al. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013;62:e147–239. DOI: 10.1016/j.jacc.2013.05.019; PMID: 23747642 3. Neuberg GW, Miller AB, O’Connor CM, et al. Diuretic resistance predicts mortality in patients with advanced heart failure. Am Heart J 2002;144:31–8. DOI: 10.1067/ mhj.2002.123144; PMID: 12094185 4. Wilcox CS. New insights into diuretic use in patients with chronic renal disease. J Am Soc Nephrol 2002;13:798–805. PMID: 11856788 5. Michael Felker G. Diuretic management in heart failure. Congest Heart Fail 2010;16Suppl1:S68–72. DOI: 10.1111/j.17517133.2010.00172.x; PMID: 20653715 6. Felker GM, Mentz RJ. Diuretics and ultrafiltration in acute decompensated heart failure. J Am Coll Cardiol 2012;59:2145–53. DOI: 10.1016/j.jacc.2011.10.910; PMID: 22676934 7. Beermann B, Midskov C. Reduced bioavailability and effect of furosemide given with food. Eur J Clin Pharmacol 1986;29:725–7. DOI: 10.1007/BF00615967; PMID: 3709617 8. Bard RL, Bleske BE, Nicklas JM. Food: an unrecognized source of loop diuretic resistance. Pharmacotherapy 2004;24:630–7. DOI: 10.1592/phco.24.6.630.34736 9. McCrindle JL, Li Kam Wa TC, Barron W, Prescott LF. Effect of food on the absorption of frusemide and bumetanide in man. Brit J Clin Pharmacol 1996;42:743–6. DOI: 10.1046/j.13652125.1996.00494.x; PMID: 8971430 10. Vasko MR, Cartwright DB, Knochel JP, et al. Furosemide absorption altered in decompensated congestive heart failure. Ann Intern Med 1985;102:314–8. DOI: 10.7326/00034819-102-3-314; PMID: 3970471 11. Gottlieb SS, Khatta M, Wentworth D, et al. The effects of diuresis on the pharmacokinetics of the loop diuretics furosemide and torsemide in patients with heart

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19.

20.

21.

Although furosemide has been used for decades, utilisation of a novel administration route by subcutaneous injection may be a viable alternative for patients with congestive heart failure. One early case series evaluated subcutaneous furosemide to a placebo injection of normal saline, with the group receiving subcutaneous furosemide demonstrating a higher amount of urine voided and higher urine sodium concentration.62 The subcutaneous furosemide group demonstrated a 30-min onset of diuresis and a duration of increased diuresis of be 3–4 h. Currently, this is an off-label use, with the limited published data available being from small case studies and uses in palliative care.63–65 The sc2WearTM Infusor, developed by scParmaceuticalsTM (Burlington, MA, USA), is a small pump that attaches to the body via an adhesive and delivers subcutaneous injections of medications. It is currently being investigated for the administration of subcutaneous furosemide in patients admitted for acute decompensated heart failure.66 The study will compare the strategy of early discharge with subcutaneous furosemide compared with usual care in a group of patients that have been stabilised but need further decongestion with intravenous diuretics. As a result, subcutaneous furosemide may be another option in the ambulatory setting for patients that become less responsive to escalating oral diuretic doses.

Conclusion Diuretics play a pivotal role in the management of heart failure to decrease congestion and symptoms. This is made difficult by the development of diuretic resistance, which is sometimes insurmountable with the common practices of increased doses of diuretics or a change in the loop diuretic. Combination therapy is frequently employed, with thiazide-type diuretics being the most commonly used. Despite diuretics being used for decades in the treatment of heart failure, guidance on the best management strategy of heart failure is still lacking. Continued investigations into diuretics and fluid-management strategies will benefit our knowledge base on the use of these medications and hopefully improve clinical outcomes for our patients with heart failure. n

failure. Am J Med 1998;104:533–8. DOI: 10.1016/S00029343(98)00111-9 Voelker JR, Cartwright-Brown D, Anderson S, et al. Comparison of loop diuretics in patients with chronic renal insufficiency. Kidney Int 1987;32:572-8. DOI: 10.1038/ ki.1987.246; PMID: 3430953 Vargo DL, Kramer WG, Black PK, et al. Bioavailability, pharmacokinetics, and pharmacodynamics of torsemide and furosemide in patients with congestive heart failure. Clin Pharmacol Ther 1995;57:601–9. DOI: 10.1016/00099236(95)90222-8 Murray MD, Haag KM, Black PK, et al. Variable furosemide absorption and poor predictability of response in elderly patients. Pharmacotherapy 1997;17:98–106. PMID: 9017769 Vormfelde SV, Brockmoller J. The genetics of loop diuretic effects. Pharmacogenomics J 2012;12:45–53. DOI: 10.1038/ tpj.2010.68; PMID: 20877298 Cosin J, Diez J. Torasemide in chronic heart failure: results of the TORIC study. Eur J Heart Fail 2002;4:507–13. DOI: 10.1016/ S1388-9842(02)00122-8 Muller K, Gamba G, Jaquet F, Hess B. Torasemide vs furosemide in primary care patients with chronic heart failure NYHA II to IV – efficacy and quality of life. Eur J Heart Fail 2003;5:793–801. DOI: 10.1016/S1388-9842(03)00150-8 Murray MD, Deer MM, Ferguson JA, et al. Open-label randomized trial of torsemide compared with furosemide therapy for patients with heart failure. Am J Med 2001;111:513– 20. DOI: 10.1016/S0002-9343(01)00903-2 Yamato M, Sasaki T, Honda K, et al. Effects of torasemide on left ventricular function and neurohumoral factors in patients with chronic heart failure. Circ J 2003;67:384-90. DOI: 10.1253/ circj.67.384; PMID: 12736474 Tsutamoto T, Sakai H, Wada A, et al. Torasemide inhibits transcardiac extraction of aldosterone in patients with congestive heart failure. J Am Coll Cardiol 2004;44:2252–3. DOI: 10.1016/j.jacc.2004.09.009; PMID: 15582326 Konecke LL. Clinical trial of bumetanide versus furosemide in patients with congestive heart failure. J Clin Pharmacol 1981;21:688–90. DOI: 10.1002/j.1552-4604.1981.tb05684.x; PMID: 7040496

22. T estani JM, Brisco MA, Turner JM, et al. Loop diuretic efficiency: a metric of diuretic responsiveness with prognostic importance in acute decompensated heart failure. Circ Heart Fail 2014;7:261–70. DOI: 10.1161/ CIRCHEARTFAILURE.113.000895; PMID: 24379278 23. Valente MA, Voors AA, Damman K, et al. Diuretic response in acute heart failure: clinical characteristics and prognostic significance. Eur Heart J 2014;35:1284-93. DOI: 10.1093/ eurheartj/ehu065; PMID: 24585267 24. Aronson D, Burger AJ. Diuretic response: clinical and hemodynamic predictors and relation to clinical outcome. J Card Fail 2016;22:193–200. DOI: 10.1016/j.cardfail.2015.07.006; PMID: 26209003 25. ter Maaten JM, Dunning AM, Valente MA, et al. Diuretic response in acute heart failure – an analysis from ASCENDHF. Am Heart J 2015;170:313–21. DOI: 10.1016/j.ahj.2015.05.003; PMID: 26299229 26. Kaissling B, Stanton BA. Adaptation of distal tubule and collecting duct to increased sodium delivery. I. Ultrastructure. Am J Physiol 1988;255:F1256–68. PMID: 3202189 27. Huerta C, Varas-Lorenzo C, Castellsague J, Garcia Rodriguez LA. Non-steroidal anti-inflammatory drugs and risk of first hospital admission for heart failure in the general population. Heart 2006;92:1610–5. DOI: 10.1136/hrt.2005.082388; PMID: 16717069 28. Buckley LF, Carter DM, Matta L, et al. Intravenous diuretic therapy for the management of heart failure and volume overload in a multidisciplinary outpatient unit. JACC Heart Fail 2016;4:1–8. DOI: 10.1016/j.jchf.2015.06.017; PMID: 26656139 29. Ryder M, Murphy NF, McCaffrey D, et al. Outpatient intravenous diuretic therapy: potential for marked reduction in hospitalisations for acute decompensated heart failure. Eur J Heart Fail 2008;10:267–72. DOI: 10.1016/j.ejheart.2008.01.003; PMID: 18308632 30. Makadia S, Simmons T, Augustine S, et al. The diuresis clinic: a new paradigm for the treatment of mild decompensated heart failure. Am J Med 2015;128:527–31. DOI: 10.1016/j. amjmed.2014.11.028; PMID: 25576670 31. Jentzer JC, DeWald TA, Hernandez AF. Combination of loop diuretics with thiazide-type diuretics in heart failure.

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44. G heorghiade M, Niazi I, Ouyang J, et al. Vasopressin V2-receptor blockade with tolvaptan in patients with chronic heart failure: results from a double-blind, randomized trial. Circulation 2003;107:2690–6. DOI: 10.1161/01. CIR.0000070422.41439.04; PMID: 12742979 45. Udelson JE, Bilsker M, Hauptman PJ, et al. A multicenter, randomized, double-blind, placebo-controlled study of tolvaptan monotherapy compared to furosemide and the combination of tolvaptan and furosemide in patients with heart failure and systolic dysfunction. J Card Fail 2011;17:973–81. DOI: 10.1016/j.cardfail.2011.08.005; PMID: 22123358 46. Felker GM, Mentz RJ, Cole RT, et al. Efficacy and safety of tolvaptan in patients hospitalized with acute heart failure. J Am Coll Cardiol 2017;69:1399–1406. DOI: 10.1016/j. jacc.2016.09.004; PMID: 27654854 47. Konstam MA, Kiernan M, Chandler A, et al. Short-term effects of tolvaptan in patients with acute heart failure and volume overload. J Am Coll Cardiol 2017;69:1409–19. DOI: 10.1016/j. jacc.2016.12.035; PMID: 28302292 48. Ambrosy AP, Fonarow GC, Butler J, et al. The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries. J Am Coll Cardiol 2014;63:1123–33. DOI: 10.1016/j. jacc.2013.11.053; PMID: 24491689 49. Yu CM, Wang L, Chau E, et al. Intrathoracic impedance monitoring in patients with heart failure: correlation with fluid status and feasibility of early warning preceding hospitalization. Circulation 2005;112:841–8. DOI: 10.1161/ CIRCULATIONAHA.104.492207; PMID: 16061743 50. Whellan DJ, Ousdigian KT, Al-Khatib SM, et al. Combined heart failure device diagnostics identify patients at higher risk of subsequent heart failure hospitalizations: results from PARTNERS HF (Program to Access and Review Trending Information and Evaluate Correlation to Symptoms in Patients With Heart Failure) study. J Am Coll Cardiol 2010;55:1803–10. DOI: 10.1016/j.jacc.2009.11.089; PMID: 20413029 51. Small RS, Wickemeyer W, Germany R, et al. Changes in intrathoracic impedance are associated with subsequent risk of hospitalizations for acute decompensated heart failure: clinical utility of implanted device monitoring without a patient alert. J Card Fail 2009;15:475–81. DOI: 10.1016/j. cardfail.2009.01.012; PMID: 19643357 52. Abraham WT, Compton S, Haas G, et al. Intrathoracic impedance vs daily weight monitoring for predicting worsening heart failure events: results of the Fluid Accumulation Status Trial (FAST). Congest Heart Fail 2011; 17:51–5. DOI: 10.1111/j.1751-7133.2011.00220.x; PMID: 21449992 53. Crossley GH, Boyle A, Vitense H, et al. The CONNECT (Clinical Evaluation of Remote Notification to Reduce Time to Clinical Decision) trial: the value of wireless remote monitoring with automatic clinician alerts. J Am Coll Cardiol 2011;57:1181–9. DOI: 10.1016/j.jacc.2010.12.012; PMID: 21255955

54. A braham WT, Adamson PB, Bourge RC, et al. Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: a randomised controlled trial. Lancet 2011;377:658–66. DOI: 10.1016/S0140-6736(11)60101-3 55. Dhruva SS, Krumholz HM. Championing effectiveness before cost-effectiveness. JACC Heart Fail 2016;4:376–9. DOI: 10.1016/j. jchf.2016.02.001; PMID: 27039130 56. Medicines USIo. CardioMEMS HF System Post Approval Study. 2017. Available from: https://clinicaltrials.gov/ct2/show/ NCT02279888 (accessed 15 June 2017) 57. Desai AS, Bhimaraj A, Bharmi R, et al. Ambulatory Hemodynamic monitoring reduces heart failure hospitalizations in ‘real-world’ clinical practice. J Am Coll Cardiol 2017;69:2357–65. DOI: 10.1016/j.jacc.2017.03.009; PMID: 28330751 58. Amir O, Azzam ZS, Gaspar T, et al. Validation of remote dielectric sensing (ReDS) technology for quantification of lung fluid status: comparison to high resolution chest computed tomography in patients with and without acute heart failure. Int J Cardiol 2016;221:841–6. DOI: 10.1016/j.ijcard.2016.06.323; PMID: 27434357 59. Amir O, Ben-Gal T, Weinstein JM, et al. Evaluation of remote dielectric sensing (ReDS) technology-guided therapy for decreasing heart failure re-hospitalizations. Int J Cardiol 2017;240:279–84. DOI: 10.1016/j.ijcard.2017.02.120; PMID: 28341372 60. Health USNIo. Sensible Medical Innovations Lung fLuid Status Monitor Allows rEducing Readmission Rate of Heart Failure Patients (SMILE™). 2017. Available from: https:// clinicaltrials.gov/ct2/show/NCT02448342 (accessed 15 June 2017) 61. Shochat MK, Shotan A, Blondheim DS, et al. Non-invasive lung IMPEDANCE-guided preemptive treatment in chronic heart failure patients: a randomized controlled trial (IMPEDANCEHF Trial). J Card Fail 2016;22:713–22. DOI: 10.1016/j. cardfail.2016.03.015; PMID: 27058408 62. Verma AK, da Silva JH, Kuhl DR. Diuretic effects of subcutaneous furosemide in human volunteers: a randomized pilot study. Ann Pharmacother 2004;38:544–9. DOI: 10.1345/aph.1D332; PMID: 14982985 63. Farless LB, Steil N, Williams BR, Bailey FA. Intermittent subcutaneous furosemide: parenteral diuretic rescue for hospice patients with congestive heart failure resistant to oral diuretic. Am J Hosp Palliat Care 2013;30:791–2. DOI: 10.1177/1049909112465795; PMID: 23136114 64. Goenaga MA, Millet M, Sanchez E, et al. Subcutaneous furosemide. Ann Pharmacother 2004;38:1751. DOI: 10.1345/ aph.1E172; PMID: 15340122 65. Zacharias H, Raw J, Nunn A, et al. Is there a role for subcutaneous furosemide in the community and hospice management of end-stage heart failure? Palliat Med 2011;25:658-63. DOI: 10.1177/0269216311399490; PMID: 21398345 66. Health USNIo. Sub-Q Versus IV Furosemide in Acute Heart Failure. 2017. Available from: https://clinicaltrials.gov/ct2/ show/NCT02579057 (accessed 15 June 2017)

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Applying Heart Failure Management to Improve Health Outcomes: But WHICH One? Yih-Kai Chan, Alice M David, Caitlyn Mainland, Lei Chen and Simon Stewart Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia

Abstract We report on our learning from many years of research testing the value of nurse-led, multidisciplinary, home-based management of heart failure. We discuss and highlight the key challenges we have experienced in testing this model of care relative to alternatives and evolving patient population. Accordingly, we propose a pragmatic approach to adapt current models of care to meet the needs of increasingly complex (and costly) patients with multimorbidity.

Keywords Heart failure, multimorbidity, frailty, disease management, nurse-led intervention, home-based intervention. Disclosure: The authors have no conflicts of interest to declare. Received: 8 August 2017 Accepted: 20 September 2017 Citation: Cardiac Failure Review 2017;3(2):113–5. DOI: 10.15420/cfr.2017:11:1 Correspondence: Prof Simon Stewart, Mary MacKillop Institute for Health Research, NHMRC of Australia Centre of Research Excellence to Reduce Inequality in Heart Disease, Australian Catholic University, Level 5, 215 Spring Street, Melbourne, Victoria 3000, Australia. E: simon.stewart@acu.edu.au

Primarily due to significant treatment advancements to prevent previously fatal acute cardiac events, the burden of heart failure (HF), characterised by chronic symptoms, acute hospitalisations and premature mortality, continues to rise.1,2 Latest expert guidelines3,4 confirmed by meta-analyses5,6 support the application of multidisciplinary HF management programs to improve health outcomes. These programmes, built on a patient-centric model of care,7 involve a congruent coordination of healthcare interventions specifically designed to optimise the management of individuals with HF. In totality, there is strong evidence supporting the benefit of direct patient contact in the post-discharge setting via HF nurses and home visits to reduce readmission rates and prolong survival.8 However, the precise timing and extent of contact (clinic-based and/or home visits) to cost-effectively improve health outcomes is less clear,9 particularly given an increasingly older and more clinically complex patient population who demand more from limited healthcare resources whilst still experiencing high levels of morbidity and premature mortality.10 We report on our many years of work testing the value of a nurse-led, multidisciplinary, home-integrated approach in HF management and care. We reflect on and highlight the key learnings from our research programme and then propose a pragmatic approach to adapt the management of HF to meet the specific needs of increasingly complex (and costly) patients with multimorbidity.

Historical Development of Heart Failure Management: Establishing its Efficacy In the early years (mid-1990s) of developing the evidence-base for applying HF management there was a broad focus on simply demonstrating its efficacy. However, based on a series of seminal trials of multidisciplinary HF management, including those reported by Rich et al.11 and our subsequent reports on a nurse-led, home-based intervention (HBI),12,13

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it was clearly demonstrable that these programmes (in multiple forms) could reduce the risk of rehospitalisation (HF and all-cause) and prolong survival relative to standard care.14,15 Despite subsequent attempts to replace face-to-face contacts with structured telephone support (STS) and remote management techniques,16,17 the results of an individual analysis18 of early trials demonstrating an in-person team approach (either applied via home visits or a specialist clinic) works best has held true. It is on this basis that our group, like those of the Coordinating Study Evaluating Outcomes of Advising and Counseling in Heart Failure (COACH) Investigators,19 started to examine how best to apply what works in HF management to maximal effect.

Nurse-led, Multidisciplinary Management of Heart Failure Works: But Which One? Having reached the point of establishing the efficacy of HF management, as part of a broad network of collaborations with other health service research groups, we established the Which Heart Failure Intervention is Most Cost-effective (And Consumer Friendly) in Reducing Hospital Stay (WHICH?) trial group to examine which forms of HF management work best to improve health outcomes. To date, we have completed two WHICH? trials as part of a portfolio of >5 randomised trials evaluating different forms of HBI across the spectrum of heart disease.

The Original WHICH? Trial The head-to-head, multicentre randomised trial compared the efficacy of a home- versus specialist clinic-based approach to HF management in 280 typically older patients (71 +/- 14 years) with HF.20 Largely due to type II error (owing to lower than expected patient recruitment) we initially showed that a HBI was not superior to a clinic-based intervention in reducing all-cause death or hospitalisation during 12–18 months’ follow up.21 However, it was associated with significantly lower healthcare costs, attributable to fewer days of hospitalisation. Moreover, in the long term, HBI was associated with significantly

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Clinical Practice Figure 1: A Patient-centered, Multidisciplinary Heart Failure Management Programme Management Setting

Location

Home visits Urban

Structured telephone support Telemonitoring

Remote

Clinic visits Patient-centred Care GARDIAN-HF High-risk Medium-risk Low-risk ARISE-HF Multi-morbidity clusters

HF Management

Dietary advice Clinical monitoring Patient education Self-care strategies Clinical communication Weight monitoring Exercise therapy

Treatment adherence Profiling Tool

Management Components

ARISE-HF = Acknowledge, Routinely profile, Identify, Support, and Evaluate Heart Failure; HF = Heart Failure; GARDIAN-HF = Green, Amber, Red Delineation of rIsk And Need in Heart Failure.

prolonged survival (HR 0.62; 95 % CI 0.42–0.90) and reduced hospital stay (twofold less likely to be in upper quartile of hospital stay) compared to clinic-based care.22 Consistent with the most recent reviews,5,6 we continue to recommend that HF management programmes apply at least one home visit post-hospitalisation as part of the nurse-led multidisciplinary management of the syndrome.

WHICH? II Trial Most recently we reported on a multicentre randomised WHICH? II trial23 that compared the cost-efficacy of standard HF management (HBI or STS for metropolitan- and regional-dwelling patients as per local guidelines)24 versus a hybrid and intensified model of care designed to direct more care to those most at risk of poor health outcomes among 787 typically older patients with HF and multimorbidity. The latter group were initially profiled prior to hospital discharge according to the Green, Amber, Red Delineation of rIsk And Need in Heart Failure (GARDIAN-HF) tool25 to determine their level of risk of premature mortality or recurrent hospitalisation. All metropolitan-dwelling patients plus those remote-dwelling patients initially categorised as GARDIAN-HF Red (high risk) received a home visit to reassess management (revision of GARDIAN-HF status). A combination of repeat home visits and STS calls were then applied accordingly, with brain natriuretic peptide levels monitored and treatment titrated where appropriate. The primary endpoint was healthcare costs at 12 months, and despite a substantial investment in profiling patient risk and applying a combination of home visits and STS calls in the ‘intensified’ group, there were no group differences in total healthcare costs. Indeed, all outcomes slightly favoured standard HF management.

Contemporary Challenges in Heart Failure Management One of the key challenges to further enhance the benefits of our model of HF management (HBI) is the possibility of a ‘threshold effect’, whereby additional holistic support, surveillance and advice (with the prospect of invoking the previously described phenomenon of a counter-productive ‘clinical cascade’ effect)26 may limit any further benefits from applying a more intensified intervention. In other words, a ‘less-is-more’ rule applies. Moreover, a solitary HF diagnosis is becoming increasingly uncommon, with the syndrome often presenting amongst multiple comorbid conditions, challenging

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healthcare systems worldwide.10 In the WHICH? II cohort, approximately 88 % of patients had ≥3 comorbid conditions. On this basis, we have recently demonstrated there is potential for worse outcomes when HBI is applied to older patients with high levels of multimorbidity.27 Our preliminary subanalysis of the WHICH? II outcomes indicated a ‘malignant’ cluster of comorbidities (arrhythmias, respiratory disease and anaemia) is not only associated with significantly increased risk of 30-day unplanned readmission or death, but also with a higher mortality rate at 1 year. Identifying these key comorbidities is important for determining multidisciplinary care for this affected population with complex needs. Yet, the vast majority of HF studies mostly exclude patients with significant comorbidities or HF with preserved ejection fraction (due to limited therapeutic options), which would likely impact the treatment paradigm.

Next Steps: What Will Work? As previously mentioned, with an increasingly ageing and multimorbid population worldwide, a more nuanced approach to HF management is needed now more than ever. Multidisciplinary management plans comprising HBI are currently being promoted as best practice to reduce recurrent hospitalisation and save healthcare resources.4 In the recently published and largest meta-analysis to date, Van Spall et al. assessed outcomes from 53 randomised control trials (including 12,356 patients) and found nurse home visits were effective in decreasing all-cause mortality and readmission compared with other organised care for HF.5 However, as noted, our research suggests that the effects of a HBI are not consistent amongst all patient groups, with age and multimorbidities co-influencing outcomes.27 We propose that this phenomenon be described as the ‘goldilocks effect’, where HF patients may be divided into three categories: simple, complex and highly symptomatic. On one end of the spectrum, simple HF patients (often younger and with fewer comorbidities) do not necessarily need a complex care plan and can be treated with a clinic-based programme. On the other end of the spectrum, highly symptomatic HF patients (often older and with multimorbidity) may be beyond the point of benefiting from a multidisciplinary care plan, instead requiring specialist palliative care services. It is the complex HF patients in the middle of the spectrum who might have the most to gain from multidisciplinary HF management, consisting of a combination of HBI, STS and telemonitoring. With this in mind, Figure 1 provides an overview of a multidisciplinary HF management programme that encourages practitioners to screen patients using the Acknowledge, Routinely profile, Identify, Support and Evaluate Heart Failure (ARISE-HF) approach (a clinical framework comprising five steps designed to improve health outcomes in HF patients affected by multimorbidity)10 and GARDIAN-HF25 tools, and classify them into stratified categories for subsequent care. Based on these categories, an appropriate HF management programme tailored to the individual (taking into account patient demographic profile, socioeconomic status, location, disease management components and disease management setting) can be established in collaboration with the patient. As demonstrated in the WHICH? trial, personal preferences play a role in the value, adherence and subsequent benefits that a patient may receive from a tailored HF management programme.28 Furthermore, the link between physical frailty (measured using handgrip) and cardiovascular-related mortality is being increasingly explored in research such as the Prospective Urban Rural Epidemiology (PURE) study,29 which in a patient population of 139,691 participants across 17 countries demonstrated grip strength was inversely associated

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Heart Failure Management to Improve Health Outcomes with all-cause mortality, MI and stroke. This simple and inexpensive measurement of frailty could further enhance a risk-stratifying tool for HF patients. However, this proposal requires further investigation before being implemented.

Conclusion The overall value of nurse-led, multidisciplinary management of HF (incorporating home visits where possible) is largely incontrovertible. However, our experience with developing and robustly testing this model of care suggests a one-size-fits-all approach should not be

tewart S, Ekman I, Ekman T, et al. Population impact of S heart failure and the most common forms of cancer: a study of 1 162 309 hospital cases in Sweden (1988 to 2004). Circ Cardiovasc Qual Outcomes 2010;3:573–80. DOI: 10.1161/ CIRCOUTCOMES.110.957571; PMID: 20923990 2. Chen J, Normand SL, Wang Y, et al. National and regional trends in heart failure hospitalization and mortality rates for Medicare beneficiaries, 1998–2008. JAMA 2011;306:1669–78. DOI: 10.1001/jama.2011.1474; PMID: 22009099 3. Yancy CW, Jessup M, Bozkurt B, et al. 2017 ACC/AHA/HFSA Focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on clinical practice guidelines and the Heart Failure Society of America. J Card Fail 2017;23:628–51. DOI: 10.1016/ j.cardfail.2017.04.014; PMID: 28461259 4. Ponikowski P, Voors AA, Anker SD, et al. 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J 2016;37:2129–200. DOI: 10.1093/eurheartj/ehw128; PMID: 27206819 5. Van Spall HG, Rahman T, Mytton O, et al. Comparative effectiveness of transitional care services in patients discharged from the hospital with heart failure: a systematic review and network meta-analysis. Eur J Heart Fail 2017; DOI: 10.1002/ejhf.765; PMID: 28233442; epub ahead of press. 6. Feltner C, Jones CD, Cené CW, et al. Transitional care interventions to prevent readmissions for persons with heart failure: a systematic review and meta-analysis. Ann Intern Med 2014;160:774–84. DOI: 10.7326/M14-0083; PMID: 24862840 7. Coleman K, Austin BT, Brach C, Wagner EH. Evidence on the chronic care model in the new millennium. Health Aff (Millwood) 2009;28:75–85. DOI: 10.1377/hlthaff.28.1.75; PMID: 19124857 8. Clark AM, Savard LA, Thompson DR. What is the strength of evidence for heart failure disease-management programs? J Am Coll Cardiol 2009;54:397–401. DOI: 10.1016/j.jacc.2009. 04.051; PMID: 19628113 9. Salive ME. Multimorbidity in older adults. Epidemiol Rev 2013;35:75–83. DOI: 10.1093/epirev/mxs009; PMID: 23372025 10. Stewart S, Riegel B, Boyd C, et al. Establishing a pragmatic framework to optimise health outcomes in heart failure and multimorbidity (ARISE-HF): a multidisciplinary position statement. Int J Cardiol 2016;212:1–10. DOI: 10.1016/j.ijcard. 2016.03.001; PMID: 27015641

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routinely applied. A clinic-based model of care is probably more suited to younger individuals with less complex needs whilst HBI should be applied to the majority of HF patients. As consideration of specialist palliative management for much older patients with very complex needs is increasingly indicated, it is important that care providers develop a better understanding of the complex interactions between HF and multimorbidity. More innovative approaches to target specific clusters of multimorbidity are required to improve health outcomes and address the conundrum of increasingly complex patients and rising healthcare costs. n

11. R ich MW, Beckham V, Wittenberg C, et al. A multidisciplinary intervention to prevent the readmission of elderly patients with congestive heart failure. N Engl J Med 1995;333:1190–5. DOI: 10.1056/NEJM199511023331806; PMID: 7565975 12. Stewart S, Pearson S, Horowitz JD. Effects of a homebased intervention among patients with congestive heart failure discharged from acute hospital care. Arch Intern Med 1998;158:1067–72. PMID: 9605777 13. Stewart S, Marley JE, Horowitz JD. Effects of a multidisciplinary, home-based intervention on unplanned readmissions and survival among patients with chronic congestive heart failure: a randomised controlled study. Lancet 1999;354:1077–83. PMID: 10509499 14. Inglis SC, Pearson S, Treen S, et al. Extending the horizon in chronic heart failure: effects of multidisciplinary, home-based intervention relative to usual care. Circulation 2006;114:2466– 73. DOI: 10.1161/CIRCULATIONAHA.106.638122; PMID: 17116767 15. Pearson S, Inglis SC, McLennan SN, et al. Prolonged effects of a home-based intervention in patients with chronic illness. Arch Intern Med 2006;166:645–50. DOI: 10.1001/ archinte.166.6.645; PMID: 16567604 16. Ong MK, Romano PS, Edgington S, et al. Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure: the better effectiveness after transition - heart failure (BEAT-HF) randomized clinical trial. JAMA Intern Med 2016;176:310–8. DOI: 10.1001/ jamainternmed.2015.7712; PMID: 26857383 17. Böhm M, Drexler H, Oswald H, et al. Fluid status telemedicine alerts for heart failure: a randomized controlled trial. Eur Heart J 2016;37:3154–63. DOI: 10.1093/eurheartj/ehw099; PMID: 26984864 18. McAlister FA, Stewart S, Ferrua S, McMurray JJ. Multidisciplinary strategies for the management of heart failure patients at high risk for admission: a systematic review of randomized trials. J Am Coll Cardiol 2004;44:810–9. DOI: 10.1016/j.jacc.2004.05.055; PMID: 15312864 19. Jaarsma T, van der Wal MH, Lesman-Leegte I, et al. Effect of moderate or intensive disease management program on outcome in patients with heart failure: Coordinating Study Evaluating Outcomes of Advising and Counseling in Heart Failure (COACH). Arch Intern Med 2008;168:316–24. DOI: 10.1001/archinternmed.2007.83; PMID: 18268174 20. Stewart S, Carrington MJ, Marwick T, et al. The WHICH? trial: rationale and design of a pragmatic randomized, multicentre comparison of home- vs. clinic-based management of chronic

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heart failure patients. Eur J Heart Fail 2011;13:909–16. DOI: 10.1093/eurjhf/hfr048; PMID: 21616952 Stewart S, Carrington MJ, Marwick TH, et al. Impact of home versus clinic-based management of chronic heart failure: the WHICH? (Which Heart Failure Intervention Is Most CostEffective & Consumer Friendly in Reducing Hospital Care) multicenter, randomized trial. J Am Coll Cardiol 2012;60:1239–48. DOI: 10.1016/j.jacc.2012.06.025; PMID: 23017533 Stewart S, Carrington MJ, Horowitz JD, et al. Prolonged impact of home versus clinic-based management of chronic heart failure: extended follow-up of a pragmatic, multicentre randomized trial cohort. Int J Cardiol 2014;174:600–10. DOI: 10.1016/j.ijcard.2014.04.164; PMID: 24825029 Scuffham PA, Ball J, Horowitz JD, et al. Standard vs. intensified management of heart failure to reduce healthcare costs: results of a multicentre, randomized controlled trial. Eur Heart J 2017;38:2340–8. DOI: 10.1093/eurheartj/ehx259; PMID: 28531281 Krum H, Jelinek MV, Stewart S, et al. 2011 update to National Heart Foundation of Australia and Cardiac Society of Australia and New Zealand Guidelines for the prevention, detection and management of chronic heart failure in Australia, 2006. Med J Aust 2011;194:405–9. PMID: 21495941 Carrington MJ, Kok S, Jansen K, Stewart S. The Green, Amber, Red Delineation of Risk and Need (GARDIAN) management system: a pragmatic approach to optimizing heart health from primary prevention to chronic disease management. Eur J Cardiovasc Nurs 2013;12:337–45. DOI: 10.1177/1474515112451702; PMID: 22752080 Mold JW, Stein HF. The cascade effect in the clinical care of patients. N Engl J Med 1986;314:512–4. DOI: 10.1056/ NEJM198602203140809; PMID: 3945278 Stewart S, Wiley JF, Ball J, et al. Impact of nurse-led, multidisciplinary home-based intervention on event-free survival across the spectrum of chronic heart disease: composite analysis of health outcomes in 1226 patients from 3 randomized trials. Circulation 2016;133:1867–77. DOI: 10.1161/ CIRCULATIONAHA.116.020730; PMID: 27083509 Whitty JA, Stewart S, Carrington MJ, et al. Patient preferences and willingness-to-pay for a home or clinic based program of chronic heart failure management: findings from the Which? trial. PLoS One 2013;8:e58347. DOI: 10.1371/journal. pone.0058347; PMID: 23505491 Leong DP, Teo KK, Rangarajan S, et al. Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet 2015;386:266–73. DOI: 10.1016/S0140-6736(14)62000-6; PMID: 25982160

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Value of Telemonitoring and Telemedicine in Heart Failure Management Gian Franco Gensini, 1 Camilla Alderighi, 2 Raffaele Rasoini, 2 Marco Mazzanti 3 and Giancarlo Casolo 4 1. Digital SIT (Italian Telemedicine Society); 2. Fiorentino Institute of Care and Assistance (IFCA), Florence, Italy; 3. International Research Framework on Artificial Intelligence in Cardiology, Royal Brompton Hospital and Harefield NHS Foundation Trust, London, UK; 4. Cardiology Unit, New Versilia Hospital, Lido di Camaiore (LU), Italy

Abstract The use of telemonitoring and telemedicine is a relatively new but quickly developing area in medicine. As new digital tools and applications are being created and used to manage medical conditions such as heart failure, many implications require close consideration and further study, including the effectiveness and safety of these telemonitoring tools in diagnosing, treating and managing heart failure compared to traditional face-to-face doctor–patient interaction. When compared to multidisciplinary intervention programs which are frequently hindered by economic, geographic and bureaucratic barriers, non-invasive remote monitoring could be a solution to support and promote the care of patients over time. Therefore it is crucial to identify the most relevant biological parameters to monitor, which heart failure sub-populations may gain real benefits from telehealth interventions and in which specific healthcare subsets these interventions should be implemented in order to maximise value.

Keywords Telehealth, telemedicine, heart failure management, remote patient monitoring, digital medical tools, telemonitoring. Disclosure: The authors have no conflicts of interest to declare. Received: 28 March 2017 Accepted: 4 July 2017 Citation: Cardiac Failure Review 2017;3(2):116–21. DOI: 10.15420/cfr.2017:6:2 Correspondence: Gian Franco Gensini, Director, Digital SIT (Italian Telemedicine Society), Via Teodoro Valfrè, 11, Rome, Italy. E: gfgensini@gmail.com

Telehealth is a multiform term embracing the applications of telematics to medicine, in order to enable diagnosis and/or treatment remotely through a set of communication tools, including phones, smartphones and mobile wireless devices, with or without a video connection.1 Until a few years ago, digital applications in medicine were restricted to the use of data obtained from electronic health records (EHR), but, in more recent times, the technological context has notably expanded: the number of existing internet-connected mobile devices has roughly doubled every five years. This phenomenon will probably lead to the simultaneous operability of around 50 billion devices by 2020.2

Sensors Sensors are tools that are capable of detecting, recording and responding to specific inputs coming from a physical setting (e.g. a patient’s vital signs) and are increasingly embedded in smartphones and other mobile devices. Recording and quantifying biological variables by means of sensors is generating large digital datasets that are suitable for transmission in real-time to healthcare and non-healthcare professionals. Computer applications arising from these phenomena are potentially numberless and will probably drive changes in both doctor–patient relationships and healthcare economic scenarios. Several insurance companies have already introduced better money premiums for customers who demonstrate regular use of smartphone applications aimed at illness prevention.1 Some issues that will need to be addressed in the near future concern patient privacy and data safety.3 As the practice of selling personal data to third parties for commercial purposes has come to light, increased attention has focused on data security of digital

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platforms and mobile devices.4,5 Several reports published recently have revealed a concerning lack of details regarding the way that personal data is managed by telehealth application developers.5 The Global Privacy Enforcement Network has disclosed that around 60 % of the applications they evaluated exhibited criticisms regarding privacy issues, as they did not properly inform users how their personal data would be used and the number of personal questions asked was considered inappropriate.6

Heart Failure Epidemiology Heart failure (HF) is a common clinical syndrome associated with high morbidity and mortality. It is a major public health problem, with a prevalence of over 5.8 million people affected in the US, and over 26 million people worldwide.7 In the US and in Europe, HF prevalence ranges from 1.1 % to 2.2 % in the general population. Most of the HF burden is situated in people aged over 65 years, who account for more than 80 % of deaths and prevalent cases in the US and in Europe.8,9 The lifetime probability of developing HF is believed to be one in five. Notwithstanding the historical equation that attributes HF genesis to a reduced left ventricular ejection fraction (LVEF), it has been shown that, in real medical practice, HF with preserved LVEF is more prevalent than HF with reduced LVEF in patients over 60 years of age (median prevalence 4.9 % and 3.3 %, respectively)10. Despite recent advances in the diagnosis and treatment of HF with reduced LVEF, management of HF with preserved LVEF is debated, and both types of HF still carry substantial morbidity and mortality, with 5-year mortality rates that are in some cases comparable to those of some cancers with a poor prognosis. In addition, HF is a leading cause of hospitalisation and hospital readmission worldwide. Data from the ARNO Observatory have shown

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Telemonitoring and Telemedicine in HF a hospitalisation rate in HF of 2.8 patients per 1000,11 which represents 1–4 % of all the hospitalisations in the US and Europe.12 Moreover, 30-day readmission rates of HF patients range from 19 % to 25 % and have been reported to be up to 50 % at 1 year,13 even if discrepancies between actual causes of HF admissions (frequently attributable to comorbidities) and hospital diagnoses from clinical records (usually assigned to decompensated HF) have increased the possibility of an overestimation of HF-related hospital readmission rates.14 Nonetheless, one of the most challenging issues for the healthcare systems nowadays is finding innovative ways to reduce the high hospital admission and readmission rates of patients with HF.13

Purposes and Goals Some studies have shown that some interventions aimed at improving the management of patients with HF after hospital discharge, in particular, periodic monitoring of symptoms/signs and reviews of pharmacological therapy, are related to a significant decrease in hospital readmission rates.15,16 However, the heavy economic costs related to the systematic organisation of patient follow-ups after hospital discharge have pushed the development of remote monitoring systems for the continuous control of clinical variables, such as blood pressure, oxygen saturation, heart rate, electrocardiogram and intracardiac/pulmonary pressure. The implementation of these monitoring tools has been hypothesised to augment medical control over the unstable syndrome of HF in order to prevent decompensations and to concurrently gain time and resources when compared to traditional care.

Artificial Intelligence as a Clinical Support Tool for HF Care A development in computer science that could be applied in future HF management is artificial intelligence (AI). In cardiology, AI is being investigated in the application of domains that span from clinical decision support systems to imaging interpretation. Some machine learning (ML) techniques allow computers, whether “trained” with wide datasets that have been previously correctly classified and labelled by doctors, to “learn” and develop autonomous (and sometimes inscrutable) rules in order to apply the learned classifications to new inputs as far as these new inputs are similar enough to those included in the training datasets. This process is mainly focused on the development of automated decision support systems aimed at diagnostic or predictive prognostic purposes. However, an appropriate classification of telemedical systems based on ML techniques is lacking and profiles of patients who could benefit most from ML-based telemedicine solutions are unknown and need to be adequately investigated.17 Prevention and treatment of disease exacerbations and promotion of patient self-empowerment are the main objectives of telemedicine in HF. Individual characteristics of patients with HF obtained from the analysis of a large number of EHR may allow the identification of those patients at higher risk of negative outcomes who could most likely benefit from individualised medical treatments. For example, the Seattle Heart Failure Model is an ML-based framework for calculating mortality risk in HF that examines multiple clinical features obtained from EHR to predict HF prognosis and incorporates the potential impact of HF therapies on patient outcomes.18 The Seattle Heart Failure Model was developed at the Mayo Clinic, where an ML risk prediction model was trained with routinely collected clinical data obtained from EHR. This decision support system showed a potential usefulness in the identification of patients with HF at higher risk of negative outcomes, but presented barriers to implementation (it was time

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consuming, expensive, required doctor familiarity with computers and did not account for clinical variables that could not be included as part of the collected data).18 Proper management of follow-up in HF patients is considered critical to reduce common causes of re-hospitalisation, that can lead to worse outcomes and increasing costs to patients and society.19 In this setting, ML techniques could be potentially valuable in remote monitoring of high-risk HF patients.

Results of Clinical Trials of Telemedicine in HF The 2016 European Society of Cardiology Guidelines for the diagnosis and treatment of acute and chronic HF recommend for the first time “remote patient monitoring” of HF patients with a recommendation of grade IIb, Level of Evidence B.20 In HF patients, telemonitoring is mainly focused on predicting acute decompensation episodes that are usually associated with fluid congestion and require optimisation of therapy. Clinical practice guidelines on chronic HF recommend daily weight measurements and include a warning alert when an increased weight of more than 2 lbs in a day is observed.20 However, even if body weight trend is rightly considered a critical element to predict decompensations, sensitivity and specificity of body weight variability alone as a proxy of total body water has revealed to be an inaccurate predictor of HF decompensations.21 Other variables have been explored in the Multisensor Monitoring in Congestive Heart Failure (MUSIC) and Sensitivity of the InSync Sentry OptiVol Feature for the Prediction of Heart Failure (SENSE-HF) trials.22,23 In the MUSIC study, a multisensor, non-invasive external device was used to measure and remotely transmit bio-impedance, heart rate, respiratory rate and volume, physical activity duration and intensity, and body posture. Investigators used a development cohort to identify a single or a multiparameter reliable algorithm based on three main components: fluid index, breath index, and personalisation parameters. Use of all three parameters yielded a sensitivity of 65 % and a specificity of 90 % in predicting acute HF decompensations. The failure rate of the device used in MUSIC was shown to be approximately 45 %, reflecting the need for further improvements.22 In the SENSE-HF study, performed on patients with chronic systolic HF who had been implanted with cardiac implantable electronic devices (CIED), an intrathoracic impedence-derived fluid index (intrathoracic impedence was measured between the lead and the pace maker’s case) consistently showed low sensitivity and low positive predictive value for hospitalisation prediction.23 Other studies have assessed the effectiveness of remote monitoring through CIED (cardiac resynchronisation therapy with or without defibrillator function) in reducing clinical decompensations, overall mortality or hospitalisations in HF patients. In the Evolution of Management Strategies of Heart Failure Patients with Implantable Defibrillators (EVOLVO) study, 200 patients with chronic systolic HF and a mean age of 66 years were randomised to remote monitoring (through CIED) of intra-thoracic impedance, atrial arrhythmias and ICD-shocks versus usual care (scheduled visits every 4 months). A significant reduction of emergency visits in the remote monitoring group was observed when compared to usual care.24 More recently, in the Implant-Based Multiparameter Telemonitoring of Patients with Heart Failure (IN-TIME) trial, 716 HF patients with a mean age 65 years and a mean LVEF of 26 %, who had been previously implanted with CIED, were randomly assigned to a telemonitoring strategy or a control “standard care” group: in the active arm patient

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Devices data were transmitted and reviewed both by the study investigators and by a central monitoring unit (composed of trained study nurses and supporting physicians). A clinical response (standardised telephone call or additional clinical care) was undertaken at the discretion of investigators. After 1 year, a modest benefit was observed in a clinical composite score (all causes of death, overnight hospital admission for HF, change in New York Heart Association (NYHA) class and change in patient global self-assessment).25 In the Optimization of Heart Failure Management using OptiVol Fluid Status Monitoring and CareLink (OptiLink HF) study, conducted in ICD carriers with severe systolic HF randomised to have fluid status alerts or usual care, no significant effect was detected in the composite endpoint of all-cause of death and cardiovascular hospitalisations.26 Some authors have speculated that alerts may even be responsible for a delay in the detection of clinical deterioration, with a consequent postponement of appropriate treatment. In the multicentric Remote Management of Heart Failure Using Implantable Electronic Devices (REM-HF) study, which enrolled patients with a mean age of 70 years who had been previously implanted with CIED, no significant difference was detected between the CIED remote monitoring group (using weekly downloads) and the usual care group with respect to the primary endpoint of death for any cause or unplanned hospitalisation for cardiovascular reasons. A concern in this study has been raised by the report that approximately 70 % of the patients in the intervention group underwent additional actions that were driven by the results of remote monitoring. This result, whether interpreted in light of the observed lack of effect on outcomes, highlights the potential risks of medicalisation and overtreatment that may arise from inappropriate use of remote monitoring strategies.27 Aside from CIED, the basic concept of care that is extended beyond traditional healthcare settings is also well captured by the phone call monitoring strategies wherein patient compliance, symptoms, vital signs, and weight are followed remotely.28–30 The Randomised Trial of Telephone Intervention in Chronic Heart Failure (DIAL) study was one of the first trials investigating structured telephone support (STS) in 1,518 HF patients randomised to an STS intervention group or to a control “usual care” group.15 In the intervention group, dedicated nurses phoned patients every 14 days and adjusted the frequency accordingly thereafter for a year. Predetermined standardised questions were used to assess dyspnea/fatigue, daily weight monitoring, oedema progression, dietary/drug compliance and physical activity. Nurses were only allowed to change the diuretic dose and recommend a non-scheduled medical consultation. Nurses used a computer-aided software system to keep a log of conversations and receive reminders for phone calls. All study subjects were followed at the study centres on a 3-month basis irrespective of unscheduled visits and phone calls. Most of these patients had systolic dysfunction and NYHA class II-III symptoms. Overall, the intervention group had fewer hospital readmissions both in the short term and even at 1–3 years after stopping intervention. Mortality was similar in both groups. At the end of the study the intervention group had a better quality of life score than the usual care group. Similarly, in a meta-analysis, Inglis et al. reviewed 16 studies investigating STS interventions and detected a non-significant trend towards improved mortality with STS versus usual care (RR 0.88 [95 % CI 0.76–1.01], p=0.08), but a significant 23 % reduction of HF hospitalisations

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(RR 0.77 [95 % CI 0.68–0.87]).31 Of the 16 studies considered, six reported improved quality of life with STS in both overall and physical scores on the Minnesota Living with Heart Failure Questionnaire and on the Kansas City Cardiomyopathy Questionnaire. The Telemonitoring to Improve Heart Failure Outcomes (Tele-HF) study randomised 1,653 subjects within 30 days of an HF hospitalisation to a telephone-based interactive voice response system or usual care. The voice response system included a series of questions related to general health and HF symptoms, with patients entering their responses using their telephone keypad.32 The Trans-European Network – Home-Care Management System (TEN-HMS) study attempted to identify whether home telemonitoring was able to improve outcomes compared with nurse telephone support and usual care.33 Home telemonitoring consisted of twice-daily patient self-measurement of weight, blood pressure, heart rate, and heart rhythm with automated devices linked to a cardiology centre. The structured telephone support consisted of specialist nurses who were available to patients by telephone. Primary care physicians delivered usual care. The primary endpoint was days lost for death or hospitalsation with nurse telephone support (NTS) versus home telemonitoring (HTM) at 240 days. At the end of the study, the number of admissions and mortality were similar among patients randomly assigned to nurse telephone support or home telemonitoring. Patients randomly assigned to receive usual care had higher 1-year mortality than patients assigned to receive NTS or HTM, but with a weakly meaningful difference (p=0.032). A smaller study by Goldberg et al.34 reported a 10.4 % absolute and 56.2 % relative reduction in mortality in a monitoring system using only symptoms and weight monitoring. Another large telemonitoring study which evaluated feasibility and perception of the Telemedical Interventional Monitoring in Heart Failure (TIM-HF) trial35 used Bluetooth technology to transmit weight, blood pressure, heart rhythm, and a self-assessed health status over a mobile telephone connection. Apart from structured monthly phone calls, physician-led medical support was available 24 hours a day, 7 days a week. Intervention was provided based on set standards on an ongoing basis. A total of 710 patients were randomised to the monitoring system or to usual care. Compliance in the intervention arm was high: 81 % had at least 70 % daily data transmission. However, follow up at 26 months showed no difference in overall mortality, cardiovascular mortality, or hospitalisations.35 A pre-specified subgroup analysis for the TIM-HF trial pointed out that specific characteristics of patients (i.e. a depression model of Patient Health Questionnaire [PHQ-9]<10 or a prior HF decompensation or an ICD implantation), could be associated with better outcomes in mortality (only the subgroup with PHQ-9<10) and numbers of days lost due to hospitalisation for HF or death.36 Findings from two Cochrane meta-analyses including studies up to 201537,38 have shown that, compared with usual care, STS can reduce all-cause mortality at a follow-up of 6–12 months, and can reduce HF-related hospitalisations. The recent Better Effectiveness After Transition – Heart Failure (BEAT-HF) study,39 one of the largest trials in telemonitoring in HF, also needs to be mentioned. This is a multicentre randomised controlled trial conducted at six academic medical centres in California, which compared usual care with a telehealth-based care transition intervention for older patients (n=1457, median age

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Telemonitoring and Telemedicine in HF 73, 664 [46.2 %] female, 316 [46.2 %] African-American) discharged home after in-hospital treatment for decompensated HF. Patients assigned to the telemonitoring intervention group were scheduled to receive nine telephone coaching calls over a 6-month period, generally from the same nurse, who had access to patient medical histories and medication records. All telephone calls covered content reinforcing the pre-discharge education materials. Patients were asked to use the telemonitoring equipment daily to transmit their weight, blood pressure, heart rate, and responses to three questions about symptoms, which were sent via cellular bandwidth to a secure server and accessed daily by the telephone call centre nurses. Readings that exceeded predetermined thresholds triggered nurses to telephone the patient so that they could investigate potential causes. When symptoms were of concern, patients were encouraged to contact their health call centre. Nurses also called patients who had stopped transmitting data to determine why and to encourage them to resume daily monitoring. Only 61.4 % (439 of 715) and 55.4 % (396 of 715) of patients randomised to the intervention were more than 50 % adherent to telephone calls and telemonitoring. This study, characterised by very poor adherence, found that a combination of remote patient monitoring with care transition management did not reduce all cause readmission at 180 days after hospitalisation for HF when compared to usual care. Hospitalisations in the first 30 days and 180-day mortality were also not reduced with telemonitoring intervention. Few studies have assessed the effectiveness of remote monitoring to promote cardiac exercise training in stable HF patients, the so-called “telerehabilitation”. In patients with stable HF, exercise training can improve life quality, symptoms, exercise capacity and hospitalisations. According to the 2016 ESC HF guidelines, all stable HF patients should undergo exercise training (class I level A).20 However, a gap has been identified between this recommendation and a lack of specific instructions about physical training. In this context, telerehabilitation has been advocated by some authors as a way to improve adherence and a practical way to promote regular exercise training in stable HF patients.40 One randomised trial on telerehabilitation in HF patients showed that an 8-week home-based telemonitored rehabilitation program based on walking training resulted as effective as an outpatient-based standard cardiac rehabilitation program and provided similar improvements in life quality.41 Another randomised trial, which included patients with CIED, compared an 8-week home-based telerehabilitation program to usual care (which did not include specific exercise programs except for lifestyle advice). This study showed better life quality and better 6-walk test distances in the telerehabilitation group, but results could have been affected by disparities in the extent of intervention between the groups.42 In summary, randomised clinical trials about telehealth interventions in HF have disclosed conflicting results regarding the ability of these interventions to reduce mortality and hospitalisation rates. Trials comparing remote telehealth interventions to usual care are nonetheless hardly comparable because of differences in the remote interaction processes, choice of monitoring systems and measured variables.43 Even in the most recent trials, little information is available on which specific therapeutic interventions have been adopted in response to abnormal changes of vital parameters and which measures have been taken to check whether patients were able to understand and follow the instructions received. Therefore, a large heterogeneity exists among current studies designs and outcomes because of

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the use of different monitoring techniques and differences among the clinical profile of the patients studied. For example, of the four different non-invasive remote monitoring strategies employed (STS, telemonitoring, videophone and interactive voice response device), only STS and telemonitoring have demonstrated in a few studies a reduction in all-cause mortality and HF-related hospitalisation.37,38 Moreover, although several clinical trials and two meta-analyses have demonstrated a benefit with the above strategies in mortality reduction and in HF-related hospitalisations, the impact of STS and telemonitoring in HF is not univocally considered to be cost-effective. Nevertheless, when compared to the uncommon chance of access to multidisciplinary intervention programs, that is frequently hindered by economic, geographic and bureaucratic barriers, non-invasive remote monitoring may be a solution to support and promote the care of HF patients over time, especially during the tricky early discharge phase after a hospitalisation. In view of the above-reported complex and heterogeneous literature, it is crucial to identify the most relevant biological parameters to monitor, which HF sub-populations may gain real benefits from telehealth interventions and in which specific healthcare subsets these interventions should be implemented in order to maximise their value. For example, a meta-regression analysis on the effectiveness of telehealth programs in patients with chronic HF showed significantly greater effectiveness in reducing mortality and hospitalisations in HF patients at higher risk.44 Another metaanalysis related the lowest mortality index for telehealth programs in HF with the promptness of feedback actions (interventions performed within 1 day of a change in the patient’s vital signs). Moreover, the complex literature on telehealth also seems marked by methodological issues, like publication bias and poor recruitment in clinical trials.45 For example, in the TELE-HF study, 14 % of patients assigned to telemonitoring never used the system and by the final week of the study period, only 55 % of the patients were still using the system at least three times a week.33 As an appropriate adherence to a given intervention can contribute to an adequate external validity of the studies, improvement of adherence represents a key element of the future research on telehealth. In the end, it has been hypothesised that a “judicious and flexible use” of technology could exist in daily clinical practice, but it might not have been intercepted by too strict and inflexible study protocols that are not able yet to fit real world settings.45

Barriers to Implementation The clear-cut reimbursement restriction of telehealth services is one of the biggest hurdles to their dissemination. In the US, while some insurance programs related to Medicaid – each one with remarkable restrictions – reimburse telehealth services in 48 states, Medicare limits reimbursements to those areas where an inadequate supply of healthcare services has been clearly established. It has been estimated that Medicare paid around five million dollars for telehealth services in 2012, which is less than 0.001 % of its expenditure.1 The second barrier to telehealth dissemination concerns the replacement of traditional face-to-face evaluations with digital ones, highlighting some of the critical issues related to the quality of doctor– patient relationship, to the potential incompleteness of “touch-free” virtual objective examinations and, in general, to the care process itself. Moreover, the fragmentation of care that would probably be delivered by heterogeneous and non-interconnected professionals may result in patients receiving different and possibly conflicting

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Devices recommendations for identical clinical pictures. With regard to legal issues, physicians who operate in the context of telehealth are not yet requested any specific accreditation: in some countries, as in the US, however, doctors need to provide verifiable references to be allowed to practice telehealth, but difficulties can arise in practicing outside the state where a physician obtained their license.1

Costs and Sustainability Telemedicine is believed to have the potential to improve costs related to healthcare.1 Direct-to-consumer telehealth, such as patient–physician meetings via videoconference, may become an efficient way to deliver care as it could reduce costs to both the patient (e.g. travel expenses, work loss, etc.) and healthcare systems. Nonetheless, the scientific literature lacks studies in good methodological quality about the comprehensive economic evaluations of telehealth services. A recent review on the cost/effectiveness of telemedicine use in chronic HF concluded that, without full economic analyses, the cost-effectiveness of telehealth interventions in chronic HF remains very difficult to be reliably determined.46 Otherwise, a recent sensitivity analysis showed that cost savings of telehealth programs are most sensitive to patient risk (i.e. more cost-effective in higher risk patients).47 This further underlines the importance of an adequate risk stratification of patients included in clinical studies on telehealth. Moreover, concerns have been raised about some of the potential unintended consequences of telemedicine medical encounters. Despite their hypothesised efficiency, virtual medical visits may paradoxically have physicians schedule more future virtual visits than they would in traditional face-to-face encounters, with a consequential unexpected increase in healthcare costs.48 A recent study analysed commercial claims data on 300,000 patients to explore patterns of spending for acute respiratory illnesses. The study concluded that direct-to-consumer telehealth may increase access to care by making it more accessible and convenient for some patients, but at the same time it may also increase utilisation and healthcare expenditure.49 In the above study, costs were lower for patients who underwent direct-to-consumer telehealth visits but increased overall because of a noticeable rise in the number of new utilisations. The authors estimated that only 12 % of direct-to-consumer telehealth visits replaced visits to other providers, but 88 % were new utilisations.49 Despite the above concerns, no sufficient and reliable evidence is available about cost-effectiveness of telehealth services, and therefore no informed decision at a policy level about delivery of such services will be well-grounded until evidence becomes available.

Patient Participation A recent policy statement of the American Heart Association on telemonitoring-based management of HF has suggested that effective programs need timely data, appropriate staff, and a feedback loop to patients with sufficient empowerment to understand and follow the proposed interventions.50 Participation of patients to the HF care process is a basic need for the success of any management program and particularly for a telemonitoring-based approach. Self-management support may be a key to the implementation of telehealth models and requires the active participation of patients. For example, in a

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qualitative study led with interviews, it was observed that non-video telehealth technologies fostered the sharing of personal information and a non-judgemental attitude in patients, but each contact between a telehealth professional and a patient required a skilful negotiation of the relationship to engage the patient as an “expert of their own illness”.51 In addition, it has been pointed out recently that HF selfmanagement may be associated with reduced hospital admissions only in a subgroup of patients with HF (i.e. patients under 65 years of age), whereas in other subgroups (patients with moderate or severe depression), involvement in self-management may be even associated with a reduced survival rate.45 Again, careful stratification of patients enrolled in clinical studies seems to be a pivotal pre-requirement for a valuable application of telehealth to different healthcare contexts.

Need for a New Approach In recent times, technological developments have expanded to the medical sector, with the ambitious objective to gain a dominant role in the future of healthcare improvements. Some authors,52 in the wake of evidence-based medicine, but also according to ethical primum non nocere and economic issues, have highlighted that new technologies, such as telehealth models, should be evaluated in methodologically sound and reproducible studies and compared to usual care before being approved and implemented in medical practice. Nonetheless, even this may turn out to be an insufficient approach. Indeed, Greenhalgh et al,52 by recalling the principle of the philosopher Heidegger that technology has its maximum value when it helps achieve “what matters to us”, have underlined that the use of technological tools in healthcare must be only considered in the precise context of the physical, material and symbolic spaces in which they are applied and perfectly embedded in the social and cultural contexts in which they must operate. This perspective could overcome the old dichotomy between “high tech” and “high touch” and potentially lead to the development of technologies that are natural extensions of both the patient’s and doctor’s intents and are not felt by users as obligations or as a waste of time. Based on results of a qualitative study performed on 40 people with comorbidities aged 60 to 98 years, the ARCHIE framework52 has suggested requirements that any new technology applied to healthcare should meet before implementation. In particular, telehealth products should be “anchored in what matters to users; realistic about the natural history of illness, continuously co-created (developing and adapting solutions in an ongoing way with those who are using them), underpinned by strong human relationships and embedded in social networks; integrated using the principles of computer-supported cooperative work (maximising mutual awareness and mobilising knowledge and expertise across the network)”.52

Conclusion The essential premise for any technological solution applied to health is the real (not theoretical or experimental) fulfilment of individual needs for whom that product had been conceived. This implies a shift from standard blinded “one size fits all” models to open personalised ones. We believe that such perspective represents a necessary starting point for future research on telehealth that is focused on a real supporting role for suffering people. n

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Telemonitoring and Telemedicine in HF

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22. A nand IS, Tang WH, Greenberg BH, et al. Design and performance of a multisensor heart failure monitoring algorithm: results from the Multisensor Monitoring in Congestive Heart Failure (MUSIC) study. J Card Fail 2012;18:289–95. DOI: 10.1016/j.cardfail.2012.01.009; PMID: 22464769. 23. Viviane M. Conraads, et al. Sensitivity and positive predictive value of implantable intrathoracic impedance monitoring as a predictor of heart failure hospitalizations: the SENSE-HF trial. Eur Heart J 2011;32:2266–73. DOI: 10.1093/eurheartj/ehr050; PMID: 21362703. 24. Landolina M, Perego GB, Lunati M, et al. Remote monitoring reduces healthcare use and improves quality of care in heart failure patients with implantable defibrillators: the Evolution of Management Strategies of Heart Failure Patients With Implantable Defibrillators (EVOLVO) study. Circulation 2012;125:2985–92. DOI: 10.1161/ CIRCULATIONAHA.111.088971; PMID: 22626743. 25. Hindricks G, Taborsky M, Glikson M, et al. Implant-Based Multiparameter Telemonitoring of Patients with Heart Failure (IN-TIME): a randomised controlled trial. Lancet 2014;384: 583–90. DOI: 10.1016/S0140-6736(14)61176-4; PMID: 25131977. 26. Böhm M, Drexler H, Oswald H, et al. Fluid status telemedicine alerts for heart failure: a randomized controlled trial. OptiLink HF study investigation. Eur Heart J 2016;37:3154–63. DOI: 10.1093/eurheartj/ehw099; PMID: 26984864. 27. Morgan JM, Kitt S, Gill J, et al. REM-HF trial. Remote management of heart failure using implantable electronic devices. Eur Heart J 2017. DOI: 10.1093/eurheartj/ehx227; PMID: 28575235. epub ahead of press. 28. Bichindaritz I, Vaidya S, Jain A, Jain LC. In: Computational intelligence in healthcare 4: advanced methodologies. Heidelberg, Germany: Springer; 2010. 29. Jerant AF, Azari R, Nesbitt TS. Reducing the cost of frequent hospital admissions for congestive heart failure. A randomized trial of a home telecare intervention. Med Care 2001;39:1234–45. PMID: 11606877. 30. D’Anker S, Koehler F. On the horizon of heart failure. Lancet 2011;378:637. DOI: 10.1016/S0140-6736(11)61314-7; PMID: 21856462. 31. Inglis SC, Clark RA, McAlister FA, et al. Which components of heart failure programmes are effective? A systematic review and meta-analysis of the outcomes of structured telephone support or telemonitoring as the primary component of chronic heart failure management in 8323 patients: Abridged Cochrane Review. Eur J Heart Fail 2011;13:1028–40. DOI: 10.1093/eurjhf/hfr039; PMID: 21733889. 32. Chaudhry SI, Mattera JA, Curtis JP, et al. Telemonitoring in patients with heart failure. N Engl J Med 2010;363:2301–09. DOI: 10.1056/NEJMoa1010029; PMID: 21080835. 33. Cleland JG, Louis AA, Rigby AS, et al. Noninvasive home telemonitoring for patients with heart failure at high risk of recurrent admission and death: the Trans-European NetworkHome-Care Management System (TEN-HMS) study. J Am Coll Cardiol 2005;45:1654–64. DOI: 10.1016/j.jacc.2005.01.050; PMID: 15893183. epub ahead of press. 34. Goldberg LR, Piette JD, Walsh MN, et al. WHARF Investigators. Randomized trial of a daily electronic home monitoring system in patients with advanced heart failure: the Weight Monitoring in Heart Failure (WHARF) trial. Am Heart J 2003;146:705–12. DOI: 10.1016/S0002-8703(03)00393-4; PMID: 14564327. 35. Koehler F, Winkler S, Shieber M, et al. Impact of remote telemedical management on mortality and hospitalizations in ambulatory patients with chronic heart failure. The telemedical interventional monitoring in heart failure study. Circulation 2011;123:1873–80. DOI: 10.1161/ CIRCULATIONAHA.111.018473; PMID: 21444883. 36. Koehler F, Winkler S, Schieber M, et al. Telemedicine in heart failure: pre-specified and exploratory subgroup analyses from the TIM-HF trial. Int J Cardiol 2012;161:143–50. DOI: 10.1016/ j.ijcard.2011.09.007; PMID: 21982700.

37. I nglis SC, Clark RA, McAlister FA, et al. Structured telephone support or telemonitoring programmes for patients with chronic heart failure. Cochrane Database Syst Rev 2010 Aug 4;(8):CD007228. DOI: 10.1002/14651858.CD007228.pub2; PMID: 20687083. 38. Inglis SC, Clark RA, Dierckx R, et al. Structured telephone support or non invasive telemonitoring for patients with heart failure. Review. Cochrane Database of Systematic Reviews 2015, Issue 10. Art. No. CD007228. DOI: 10.1002/14651858. CD007228.pub3; PMID: 26517969. 39. Ong MK, Romano PS, Edginton S, et al. Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure: The Better Effectiveness After Transition – Heart Failure (BEAT-HF) Randomized Clinical Trial. JAMA Int Med 2016;176:310–8. PMID: 26857383; DOI: 10.1001/ jamainternmed.2015.7712. 40. Piotrowicz E, Piepoli MF, Jaarsma T, et al. Telerehabilitation in heart failure patients: The evidence and the pitfalls. Int J Cardiol 2016;220:408–13. DOI: 10.1016/j.ijcard.2016.06.277; PMID: 27390963. 41. Piotrowicz E, Baranowski R, Bilinska M, et al. A new model of home-based telemonitored cardiac rehabilitation in patients with heart failure: effectiveness, quality of life and adherence. Eur J Heart Fail 2010;12:164–71. DOI: 10.1093/eurjhf/hfp181; PMID: 20042423. 42. Piotrowicz E, Zielinski T, Bodalski R, et al. Home-based telemonitored Nordic walking training is well accepted, safe, effective and has high adherence among heart failure patients, including those with cardiovascular implantable electronic devices – a randomized controlled study. Eur J Prev Cardiol; 2015;22:1368–77. DOI: 10.1177/2047487314551537; PMID: 25261268. 43. Dierckx R, Inglish S, Robyn AC, et al. Telemedicine in heart failure: new insights from the Cochrane meta analyses. Eur J Heart Fail 2017;19:304–6. DOI: 10.1002/ejhf.759; PMID: 28251777. 44. Xiang R, Li L, Liu SX. Meta-analysis and meta-regression of telehealth programmes for patients with chronic heart failure. J Telemed Telecare 2013;19:249–59. DOI: 10.1177/1357633X13495490; PMID: 24163234. 45. Greenhalgh T, A’Court C, Shaw S. Understanding heart failure; explaining telehealth – a hermeneutic systematic review. BMC Cardiovasc Disord 2017;17:156. DOI: 10.1186/s12872-017-0594-2; PMID: 28615004. 46. Grustam AS, Severens JL, van Nijnatten J, et al. Cost effectiveness of telehealth interventions for chronic heart failure patients: a literature review. Int J Technol Assess Health Care 2014;30:59–68. PMID: 28615004; DOI: 10.1186/s12872017-0594-2. 47. Liu SX, Xiang R, Lagor C, et al. Economic modelling of heart failure telehealth programs: when do they become cost saving? International journal of telemedicine and applications, 2016; PMID: 27528868; DOI: 10.1155/2016/3289628. 48. Kahn JM. Virtual visits — confronting the challenges of telemedicine. N Engl J Med 2015;372:1684–5. PMID: 25923547 DOI: 10.1056/NEJMp1500533. 49. Ashwood JS, Mehrotra A, Cowling D, Uscher-Pines L. Direct-toconsumer telehealth may increase access to care but does not decrease spending. Health Aff (Millwood), 2017;36:485-91. DOI: 10.1377/hlthaff.2016.1130. 50. Schwamm LH, Chumbler N, Brown Ed, et al. Recommendations for the Implementation of Telehealth in Cardiovascular and Stroke Care. A Policy Statement from the American Heart Association. Circulation 2016. PMID 27998940. epub ahead of press. DOI: 10.1161/CIR.0000000000000475; PMID: 27998940. 51. Heckemann B, Wolf A, Ali L, et al. Discovering untapped relationship potential with patients in telehealth: a qualitative interview study. BMJ Open. 2016;6:e009750. DOI: 10.1136/ bmjopen-2015-009750; PMID: 26936904. 52. Greenhalgh T, Procter R, Wherton J, et al. What is quality in assisted living technology? The ARCHIE framework for effective telehealth and telecare services. BMC Med 2015;13:91. DOI: 10.1186/s12916-015-0279-6; PMID: 25902803.

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Clinical Practice

Predictors of Post-discharge Mortality Among Patients Hospitalized for Acute Heart Failure Ovidiu Chioncel, 1 Sean P Collins, 2 Stephen J Greene, 3 Peter S Pang, 4 Andrew P Ambrosy, 3 Elena-Laura Antohi, 1 Muthiah Vaduganathan, 5 Javed Butler 6 and Mihai Gheorghiade 7 * 1. Carol Davila University of Medicine and Pharmacy, Emergency Institute for Cardiovascular Diseases, Bucharest, Romania; 2. Vanderbilt University Medical Center, Nashville,TN, USA; 3. Duke Clinical Research Institute and Division of Cardiology, Duke University Medical Center, Durham, NC, USA; 4. Department of Emergency Medicine, Indiana University School of Medicine, Indiana, IN, USA; 5. Brigham and Women’s Hospital Heart and Vascular Center and Harvard Medical School, Boston, MA, USA; 6. Stony Brook University, Stony Brook, NY, USA; 7. Center for Cardiovascular Innovation, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

Abstract Acute Heart Failure (AHF) is a “multi-event disease” and hospitalisation is a critical event in the clinical course of HF. Despite relatively rapid relief of symptoms, hospitalisation for AHF is followed by an increased risk of death and re-hospitalisation. In AHF, risk stratification from clinically available data is increasingly important in evaluating long-term prognosis. From the perspective of patients, information on the risk of mortality and re-hospitalisation would be helpful in providing patients with insight into their disease. From the perspective of care providers, it may facilitate management decisions, such as who needs to be admitted and to what level of care (i.e. floor, step-down, ICU). Furthermore, risk-stratification may help identify patients who need to be evaluated for advanced HF therapies (i.e. left-ventricle assistance device or transplant or palliative care), and patients who need early a post-discharge follow-up plan. Finally, risk stratification will allow for more robust efforts to identify among risk markers the true targets for therapies that may direct treatment strategies to selected high-risk patients. Further clinical research will be needed to evaluate if appropriate risk stratification of patients could improve clinical outcome and resources allocation.

Keywords Acute heart failure, risk stratification, prognosis models, risk scores Disclosure: SPC received research support from NIH, ANRQ, PCORI, AHA, Novartis Consulting: Novartis, Trevena. All other authors have no conflicts of interest to declare. Received: 6 September 2017 Accepted: 24 October 2017 Citation: Cardiac Failure Review 2017;3(2):122–9. DOI: 10.15420/cfr.2017:12:1 Correspondence: Ovidiu Chioncel, Carol Davila University of Medicine and Pharmacy, Emergency Institute for Cardiovascular Diseases “Prof. C.C. Iliescu”, Bucharest 950474, Romania. E: ochioncel@yahoo.co.uk. *Mihai Gheorghiade passed away on 24 August 2017.

Hospitalisation is a critical event in the clinical course of heart failure (HF) and despite relatively rapid relief of symptoms, hospitalisation is followed by an increased risk of death and re-hospitalisation.1 While performance measures have been developed in the last few years with the intent of improving post-discharge outcomes, post-discharge mortality rates remain unchanged or have slightly worsened.2 The mechanisms of these high post-discharge event rates are incompletely understood3 and, to date, no treatment has improved such outcomes. Although long-term mortality is the result of the continuous deterioration of cardiac substrate, worsening of comorbidities, and progression of HF, there is considerable diversity of both the underlying pathophysiology and the patients involved, which makes it difficult to find an explanation that is suitable for all patients.4

attempted to explore their relationship with post-discharge mortality. Knowledge of mortality predictors can be used to generate predictive models that can aid clinicians in their decision-making, in particular by identifying patients who are at high or low risk of death. These models could be used as a framework to discuss prognosis and provide evidence to support rational decision-making.

Registry data reveal that 20 % of patients are discharged despite persistent signs and symptoms of HF, including minimal decrease or even increase in body weight. These findings suggest failure to relieve clinical congestion during the index hospitalisation may potentially contribute to the high post-discharge mortality rate.4

Risk Stratification in Acute Heart Failure

Post-hoc analyses of these clinical trials and international registries have identified several prognostic factors in AHF patients and have

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Even if the phenotypic heterogeneity of AHF patients5 makes it difficult to find a risk model suitable for all patients, many parameters are common to several of the models. Demographic characteristics, renal function, markers of organ injury, and non-cardiac comorbidities are included in most risk models (see Figure 1). Our goal in the present paper is to review the most important prediction models developed for the risk-stratification of patients with AHF.

Heart failure hospitalisation represents an important opportunity to assess patient prognosis. In the care of patients with HF, estimating and communicating prognosis is recommended by clinical guidelines6 and is considered to be an important component of high-quality health care. A better understanding of the mechanisms underlying the poor prognosis of patients hospitalised for HF may help provide better care and improve post-discharge mortality.

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Predictors of Post-discharge Mortality Among AHF Patients One of the major goals of AHF risk stratification is to match the risk profile of the patient with the type and intensity of care. AHF is not one distinct pathophysiologic entity, but rather a heterogeneous syndrome with multiple contributors to the progression of the disease and prognosis. A comprehensive assessment in these patients is necessary to identify multiple prognostic characteristics that may become possible therapeutic targets. Moreover, the phenotypic heterogeneity of AHF patients, either at presentation or during the hospital course, suggests that an algorithm is needed to classify these patients. For initial presentation, a “6-axis model” has been proposed to classify AHF patients.7 While this was designed for the initial assessment, each component of this model has long-term prognostic value (see Figure 1). Candidate predictors can be obtained from patient demographics, clinical history, physical examination, disease characteristics, laboratory tests, and previous treatment. Studied predictors should be clearly defined, standardised, and reproducible to enhance generalisability and application of study results to practice.8 Prognostic studies use a multivariable approach in their design and analysis to determine the important predictors of the studied outcomes and to provide outcome probabilities for different combinations of predictors. The aim is to determine whether an outcome can reliably be attributed to a particular risk factor, with adjustment for other causal factors (confounders) using a multivariable approach. Predictors can be derived from registries (see Table 1) or from randomised clinical trials (RCTs) (see Table 2). Clinical characteristics of patients enrolled in RCTs may differ to those in the general population with HF, and prognostic models obtained from RCT data may have restricted generalisability because of strict eligibility criteria for the trial, low recruitment levels, lower rate of associated comorbidities, or large numbers of patients refusing consent. Registries have increased predictive power due to the large number of patients enrolled, but collection of clinical variables may not be as rigorous and complete as in RCTs. One important consideration when assessing predictors of postdischarge mortality is the time frame of data collection.22 Variables collected upon admission may be less likely to be linked to 6-month or 1-year prognosis, as changes in clinical status or medical interventions performed during hospitalisation may affect medium or long-term outcomes. However, some variables collected at AHF admission are unmodifiable risk factors, such as age, gender and presence of comorbidities. In addition, when considering all prognostic factors, it is important to carefully review the selection criteria for the cohort from which the predictor was reported. For example, some RCT enrolled patients with reduced ejection fraction (EF), while other RCTs were inclusive AHF irrespective of EF. Among patients with reduced EF, the Efficacy of Vasopressin Antagonism in Heart Failure: Outcome Study with Tolvaptan (EVEREST) trial database23 allowed the opportunity for numerous sub-analyses which have provided valuable insights in understanding post-discharge mortality predictors (see Table 3).

Variables Predictive of Post-discharge Outcomes in AHF In RCTs and registries, the predictive factors for post-discharge mortality included age, history of previous hospitalisation, congestion, systolic blood pressure (SBP), heart rate (HR), QRS duration, renal

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Figure 1: 6-axis Risk Model for Post-discharge Mortality in Patients Hospitalized for Acute Heart Failure SBP, HR, QRS De novo vs chronic worsening HF Dyspnea/congestion Clinical severity Precipitants Comorbidities

“6-axis model”

Multi-organ injury

Post-discharge mortality

Congestion

Renal function

Non CV Comorbidities Admission

Discharge

In acute heart failure, multiple entities contribute to post-discharge mortality. Assessment at initial presentation by 6-axis model offers significant prognostic information. Markers reflecting severity of congestion and multi-organ injury are determinants for in-hospital and post-discharge course. Severity of disease, mirrored by alterations in cardiac electromechanical substrate, as well as severity and number of associated non-cardiovascular comorbidities negatively impact post-discharge prognosis. CV = cardiovascular; HR = heart rate; SBP = systolic blood pressure.

function, markers of organ injury, and non-cardiac comorbidities (such as diabetes, cerebrovascular disease, chronic obstructive pulmonary disease, liver cirrhosis, and anaemia) (see Tables 1–3). Although, there are many candidates that have additional prognostic value, the key variables are SBP and renal function. These two are the best discriminators between patients who survive hospitalisation and those who die or are readmitted post-discharge. We have highlighted some of these important prognostic markers that are relevant in clinical practice.

Congestion Clinical trials testing short-term IV therapies have focused on dyspnoea improvement. However, dyspnoea assessments remain imprecise and regardless of how it is measured, the vast majority of resolves or significantly improves in the first 24 to 48 hours of IV standard therapies.24 In the Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure (ASCEND-HF),25 the relationship between in-hospital dyspnoea improvement and post-discharge outcomes was inconsistent and study medication failed to show any post-discharge outcomes benefit. Furthermore, pathophysiology of congestion is more complex, and the subjective feeling of dyspnoea may poorly correlate with objective measures of decongestion, such as weight change25 or urine output.26 Nonetheless, congestion is the leading cause for AHF readmission, and represents an important therapeutic target of inpatient management, and a major determinant of discharge decision-making. Indeed, a clinical score, including orthopnoea, JVD and pedal oedema was used in the EVEREST trial, and this congestion score was associated with an increased risk of 30-day and 1-year mortality.27 However, despite the clinical importance of targeting signs and symptoms during hospitalisation, patients with absent or minimal signs and symptoms

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Clinical Practice Table 1: Independent Predictors of Post-discharge Mortality in Registries Registry

Year of Sample Prediction period publication size

Independent predictor results from multivariate analysis

2008 4400 60-day mortality OPTIMIZE-HF9

Creatinine; sodium; age; HR; liver disease; previous CVA/TIA; peripheral vascular disease; race; left ventricular systolic dysfunction; COPD; SBP; previous HF hospitalisation

2006 599 1-month and 12-month mortality EFICA11

Shock; renal dysfunction; ischaemia; liver dysfunction; previous ADHF episode; comorbidity; SBP; pulmonary oedema

2013 5306 1-month and 12-month mortality MOCA12

Age; sex; SBP and DBP; eGFR; sodium; haemoglobin; heart rate; NT-proBNP; CRP; MR-proADM; sST2

2006 620 FINN AKVA13

age, male gender; lower systolic blood pressure (SBP) on admission; C-reactive protein and serum creatinine >120 mmol/L

2016 5039 1-year mortality ESC-HF-LT registry14

Age; SBP; EF; NYHA III–IV; congestion; aortic stenosis; diabetes; COPD; previous stroke; renal dysfunction; hepatic dysfunction

2011 3438 1-year mortality AHEAD15

Age; creatinine; valvular disease; LVEF <30 %; previous stroke or TIA; de novo vs worsening chronic HF

2013 1855 1-year mortality IN-HF16

Age; low SBP; somnolent or confused; Na <136 mEq/l; creatinine >1.5 mg/dl; BUN >50 mg/dl; Hb <12 g/dl; APE; COPD

ADHF = acute decompensated heart failure; AHEAD = Acute Heart Failure Database; APE = acute pulmonary embolism; BUN = blood urea nitrogen; COPD = chronic obstructive pulmonar disease; CRP = C-reactive protein; CVA/TIA = cerebrovascular accident/transitory ischaemic accident; DBP = diastolic blood pressure; EF = ejection fraction; EFICA = Etude Francaise de l’Insuffisance Cardiaque Aigue; eGFR = estimated glomerular filtration rate; ELAN = European Collaboration on Acute Decompensated Heart Failure; ESC HF LT = European Society of Cardiology Heart Failure Long-Term Registry; FINN AKVA = Finnish Acute Heart Failure Study; Hb = haemoglobin; HF = heart failure; HR = heart rate; IN HF = Italian Network on Heart Failure; LVEF = left ventricular ejection fraction; MOCA = Multinational Observational Cohort on Acute Heart Failure; MR-proADM = mid-regional pro-adrenomedullin; NT-proBNP = N-terminal pro brain natriuretic peptide; NYHA = New York Heart Association; OPTIMIZE-HF = Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure; SBP = systolic blood pressure; sST2 = soluble suppression of tumorigenicity 2.

Table 2: Independent Predictors of Post-discharge Mortality in Randomised Controlled Trials RCT

Year of Sample size publication

Prediction period

Independent predictor results from multivariate analysis

OPTIME-CHF17

2004

60-day mortality

Age; NYHA functional class; SBP; BUN; sodium

949

2010 423 6-month mortality ESCAPE18

BNP; cardiopulmonary resuscitation or mechanical ventilation during hospitalisation; blood urea nitrogen; serum sodium, age >70 years; daily loop diuretic, furosemide equivalents >240 mg; lack of betablocker; 6-min walk test

2016 1990 90-day mortality PROTECT19

Age; COPD; SBP; WBC count; serum sodium; bicarbonate; BUN; uric acid

2007 7572 2-year mortality CHARM20

Age; LVEF; diabetes-insulin treated; low BMI; male; NYHA Class IV; current smoker; cardiomegaly; prior HF hospitalisation within 6 months

2015 7141 30-day and 180-day mortality ASCEND HF21

Age; BUN; baseline sodium, SBP>140 mmHg; baseline dyspnoea

ASCEND-HF = Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure; BMI = body mass index; BNP = brain natriuretic peptide; BUN = blood urea nitrogen; CHARM = Candesartan in Heart Failure-Assessment of Reduction in Mortality and Morbidity; COPD = chronic obstructive pulmonary disease; ESCAPE = Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness; HF = heart failure; LVEF = left ventricular ejection fraction; NYHA = New York Heart Association; OPTIME-CHF = Outcomes of a Prospective Trial of Intravenous Milrinone for Exacerbations of Chronic Heart Failure; PROTECT = Placebo-controlled Randomized study of the selective A(1) adenosine receptor antagonist rolofylline for patients hospitalized with acute heart failure and volume Overload to assess Treatment Effect on Congestion and renal function; RCT = randomised clincal trial; SBP = systolic blood pressure; WBC = white blood count.

of congestion may experience a lower, but comparable, post-discharge event rate as compared to the overall cohort. This finding raises the hypothesis that treating beyond resolution of congestion may mediate improvements in post-discharge outcomes.7 Further research is necessary to prospectively validate the clinical utility of targeting provocative manoeuvres, including an assessment of orthopnoea, orthostatic hypotension, lung ultrasound, and completion of a 6-minute walk test and haemodynamic biomarkers, such as BNP/NT-proBNP, in patients hospitalized for HF.27

Natriuretic Peptides Natriuretic peptides (NPs) represent a sensitive and noninvasive measure of ventricular filling pressures, which correlates with overall

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cardiac function and informs prognosis, irrespective of ejection fraction.28 During hospitalisation and the post-discharge period, persistence of elevated levels of NPs after resolution of clinical congestion signifies haemodynamic congestion. In clinical studies, the absolute level of NPs measured at discharge29,30 and NPs percentage variation during hospitalisation31 correlate with post-discharge mortality. Also, BNP level at 1-week post-discharge was associated with the largest increase in prognostic value having the best accuracy of grading patients’ likelihood of death.32 Additionally, change in natriuretic peptide levels at hospitalisation to 1-month postdischarge carries incremental predictive value above the absolute value of the 1-month measurement alone.33

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Predictors of Post-discharge Mortality Among AHF Patients Systolic Blood Pressure A large number of registries and RCTs have shown that systolic blood pressure (SBP) assessment at admission provides important, independent prognostic information in patients with HF with both reduced and preserved EF. Furthermore, SBP at hospital admission can effectively identify groups of patients that differ with respect to clinical characteristics, prognosis, underlying pathophysiology and therapeutic approach.34 In the EVEREST trial, low SBP determined either after the initiation of standard therapy or after the resolution of the “acute” phase of hospitalisation remained an indicator of poor prognosis.35 Elevated SBP in the acute setting is a result of high sympathetic tone, termed reactive hypertension, indicating the presence of functional cardiac reserve in the face of an acute physiologic stressor. In contrast, low, or even normal SBP at presentation, which may be the goal of treatment in the ambulatory setting, may be a more ominous finding, reflecting a low cardiac output and suboptimal or inadequate endorgan perfusion.34

Heart Rate At the time of admission for AHF, heart rate (HR) is a reflection of the patient’s haemodynamic status,36 and changes in HR during hospitalisation have not been associated to short-term outcomes.37 A higher HR at both 1 and 4 weeks post-discharge was independently predictive of increased mortality during subsequent follow-up in patients with reduced EF and worsening HF.38

QRS Duration Electrical dys-synchrony, as evidenced by a prolonged QRS duration, remains the currently accepted guidelines indicator to evaluate potential candidates for CRT.6 Prolonged QRS duration was independently associated with high post-discharge mortality in the EVEREST trial.39 The presence of a prolonged QRS duration associated with reduced LVEF is not only a marker for significantly increased mortality but becomes a potential therapeutic target. The Cardiac Resynchronisation in Heart Failure (CARE-HF) study demonstrated that CRT improved symptoms and reduced the risk of death in patients with reduced ejection fraction and prolonged QRS duration in the outpatient setting.40

Hyponatremia In the ESC-HF-LT registry,41 hyponatremia (serum sodium <135 mEq/l) has been reported on admission at 25 % of patients and at discharge at 18 % of patients. The pathophysiology of hyponatremia in HF has been described as a result of neurohormonal activation, including stimulation of vasopressin, which in complex interactions impairs water excretion (i.e. dilutional hyponatremia).42 Also, diuretic agents and several other common non-cardiac comorbidities may decrease serum sodium concentration. 43 Hyponatremia is one of the most constant cited predictors of mortality in clinical trials and registries, and has been associated with a threefold increase in post-discharge mortality.44 A similar finding has been found for serum osmolality and lower discharge serum osmolality was predictive of post-discharge outcomes in a sub-analysis of the EVEREST study.45

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Table 3: Main Predictive Factors for Post-discharge Mortality Derived from EVEREST Trial Sub-analyses

Baseline

Discharge

1 week

Systolic blood pressure

Heart rate

Congestion score

B-type natriuretic peptide

Large QRS duration

Low cholesterol

Hepatic function test: • ALT, AST • Increased bilirubine • Low albumin

No + +

Haematocrit

- + -

Low osmolality

Hyponatraemia

Potassium

High seric uric acid*

Anaemia

No +

*Only in patients with normal baseline renal function. + = predictive value; - = no information; ALT = alanine aminotransferase; AST = aspartate aminotransferase.

Although Tolvaptan was successful in reducing hyponatremia and increasing serum osmolality, inducing weight and fluid loss, it has not been shown to improve clinical outcomes.23 This suggests that hyponatremia and serum osmolality, despite of markers of prognosis, are not targets for drug therapy. Distinct to hyponatremia, baseline and in-hospital changes in potassium, although may significantly impact in-hospital care and may limit the implementation of evidence-based therapies, they are not associated with all-cause mortality.46

Renal function The majority of registries and RCTs considered baseline renal impairment as a predictor of poor outcome in AHF.47 Markers of renal function included serum creatinine, BUN, and uric acid. Baseline creatinine has not been consistently included in prognostic models and a major limitation of this biomarker is that creatinine is not only filtered but is also secreted by the kidney, and its production is dependent on muscle mass.48 Although serum blood urea nitrogen (BUN) is considered to be a less specific marker of renal function compared with serum creatinine, it varies independently of changes in creatinine in HF patients because of neurohormonal activation and enhanced tubular reabsorption. Thus, elevated serum BUN in AHF patients may reflect both altered intrinsic renal function and potentially reversible “vasomotor nephropathy” secondary to the haemodynamic and neurohormonal effects of destabilised HF,49 and its predictive value extends beyond hospitalisation. Prognostic utility of high serum uric acid (sUA) is limited, and sUA is predictive of post-discharge mortality only in patients with preserved admission renal function.50

Markers of Organ Injury Injury or end-organ dysfunction, including myocardial damage, worsening renal function, and hepatic impairment, have been independently associated with mortality in AHF. Although many other organs (e.g. brain, lung, intestine, endothelium, vasculature) are

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Clinical Practice exposed to injury during AHF episodes, organ-specific injury markers for these organs suitable for clinical practice are missing.51 While data support such markers as prognostic, clinical trial efforts to date with therapies designed to abort end-organ injury during episodes of AHF have been disappointing.

Troponin An increase in plasma troponin levels is very common in patients hospitalised for HF. The percentage of patients with “elevated” troponin in AHF depends substantially on the severity of HF, the cut-point chosen, as well as the sensitivity of the assay employed.52 Furthermore, Troponin release in AHF is often persistent. In ASCEND-HF, at 30 days post-discharge, 62 % of patients had detectable values of troponin I and 28 % had elevated values >99 % of URL53 Although the exact mechanisms of myocardial injury in HF are uncertain, ischaemia, haemodynamic stress, oxidative stress, inflammation, altered calcium handling and impaired renal clearance have all been proposed as mechanisms of troponin elevation.54 Multiple studies have evaluated the association between baseline elevated circulating cTn and post-discharge mortality in various AHF settings. Despite variations in study design, patient populations, and assay characteristics, there has been a mostly consistent association between cTn elevation and worsened post-discharge outcomes.55–57 Thus, measurement of cTnI in patients hospitalised for AHF is warranted, given the desire to identify patients at high risk for adverse outcomes, as well as to identify patients in whom ischaemia appears to be a trigger of decompensation.6 Studies have varied in the type of troponin assay used (I or T), traditional or high sensitivity (hsTn), as well as the cut-off values used to define a positive test. In addition to baseline values and a rise in serum troponin levels during hospitalisation, an index of an eventrelated myocardial necrosis, is a powerful predictor of outcomes.53,58 In the Placebo-Controlled Randomized Study of the Selective Adenosine A1 Receptor Antagonist Rolofylline for Patients Hospitalized with Acute Decompensated Heart Failure and Volume Overload to Assess Treatment Effect on Congestion and Renal Function (PROTECT) study, positive troponin at baseline, and conversion during hospitalisation from negative to positive levels, were associated with worse outcomes at 60 days.59 In the Relaxin in Acute Heart Failure (RELAX-AHF) study, an increase in troponin during the hospital stay had an independent relation with 180 days mortality.60

Worsening Renal Function A review of the wide body of literature suggests an inconsistent relationship between in-hospital worsening renal function (WRF) and post-discharge outcomes. Data suggest that the clinical context of WRF is essentially in determining its prognostic implication. For example, WRF in the setting of effective decongestion, as exemplified by haemoconcentration, has consistently been associated with favourable long-term prognosis. This is often referred to as “pseudo worsening renal function”.61 Importantly, longitudinal follow-up of these patients shows a strong tendency for renal function to return to baseline, suggesting that no permanent renal injury takes place despite a change in laboratory values. In contrast, WRF outside the context of active fluid removal may predict worse outcomes.62 Thus, in patients with AHF, serum creatinine changes during admission are associated with adverse outcome only in the presence of congestion. Persistence

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of congestion during hospitalisation is the most important prognostic factor and WRF has clinical significance only when occurring in patients with persistent fluid overload.63 A further decline in eGFR (especially if urinary output decreases or the clinical status of a patient simultaneously deteriorates) may represent true WRF, which is associated with substantially worse long-term outcomes and thus should be avoided.47 Furthermore, targeting improvement or preservation of renal function did not lead to an improved survival rate in the PROTECT trial.64

Liver Injury Previous studies conducted in patients hospitalised for HF65–68 have found the association between transaminases and mortality to not be statistically significant after adjusting for natriuretic peptide concentrations66 or invasive haemodynamic measurements.67,68 However, in RELAX I, increases in serum transaminases (AST and ALT) were associated with increased 180-day all-cause mortality.60

Comorbidities Two-thirds of readmissions within 30 days from a HF hospitalisation are for non-HF primary issues, regardless of EF. Comorbidities are highly prevalent in this population, and not only do they precipitate rehospitalisation, uncontrolled comorbidities worsen HF over time.69 In the ESC-HF-LT-registry,14 a number of non-cardiac comorbidities, including hepatic or renal dysfunction, previous stroke, diabetes and chronic obstructive pulmonary disease (COPD), were found to be independent predictors for 1-year mortality in patients hospitalised for HF with both preserved and reduced EF. Although without a graded relationship between baseline glycaemia and outcomes, diabetic status has been found to be one of the most important predictors of 1-year all-cause mortality, independent of EF, eGFR and other comorbidities.70 Concurrent COPD independently predicts mortality in patients with reduced and preserved ejection fraction,71–73 and greater airflow obstruction is associated with worsening survival.74 In EVEREST, anaemia at discharge, but not admission, was independently associated with increased all-cause mortality.75 However, it is unclear whether anaemia is truly a prognostic marker or a mediator of risk. It has been postulated that anaemia is a marker of disease severity for HF, or that other factors associated with anaemia are responsible for the increased events. Multiple RCTs have investigated the impact of anaemia treatment on mortality, including the recent RED-HF trial,76 but have failed to demonstrate significant benefit. Since the aetiology of anaemia in AHF is multifactorial, targeting anaemia in the broad population with AHF may not be a viable strategy until there is an improved understanding of how anaemia or different components of anaemia directly affect post-discharge outcomes.

Risk Scores Post-hoc analyses have combined various risk markers from multivariable models in risk scores, in order to better stratify patients and thus identify the highest-risk patients.23 Probability as an individual patient who experienced an event had a higher risk score than a patient who had not experienced the event is evaluated by C-statistic.77

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Predictors of Post-discharge Mortality Among AHF Patients Table 4: Risk Stratification in Acute Heart Failure and Clinical Relevant Risk Scores Risk score variables

C-statistic

OPTIMIZE-HF9 4400 60-day mortality

Study

Sample size

Prediction period

Age; HR; SBP; serum creatinine; serum sodium; primary cause of admission (heart failure or other); and LVEF

0.74

1301 180-day mortality ELAN10

NT-proBNP at discharge; NT-proBNP reduction; age; peripheral oedema; SBP; low sodium; serum urea; NYAH III and IV

0.78

949 60-day mortality OPTIME-CHF17

Age; lower; SBP; NYHA class IV; symptoms; elevated BUN; decreased sodium

0.77

423 6-month mortality ESCAPE18

Age >70 years; BUN >40; BUN >90 mg/dl; 6MWT; NA <130 mEq/l; cardiopulmonary resuscitation or mechanical ventilation during hospitalisation; furosemide equivalents >240 mg; lack of betablocker; BNP >500 and BNP >1300 pg/ml

0.74

ASCEND-HF21

7141

6-month mortality

Age; low SBP; low sodium; high BUN; dyspnoea at rest

0.70

MOCA12

5306

12-month mortality

Age; SBP; eGFR <60 ml/min; sodium; haemoglobin; heart rate

0.73

6MWT = 6-minute walking test; ASCEND-HF = Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure; BNP = brain natriuretic peptide; BUN = blood urea nitrogen; COPD = chronic obstructive pulmonary disease; eGFR = estimated glomerular filtration rate; ELAN = European collaboration on acute decompensated Heart Failure; ESCAPE = Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness; HF = heart failure; HR = heart rate; LVEF = left ventricular ejection fraction; MOCA = Multinational Observational Cohort on Acute Heart Failure; MR-proADM = mid-regional pro-adrenomedullin; NT-proBNP = N-terminal pro brain natriuretic peptide; NYHA = New York Heart Association; OPTIME-CHF = Outcomes of a Prospective Trial of Intravenous Milrinone for Exacerbations of Chronic Heart Failure; OPTIMIZE-HF = Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure; SBP = systolic blood pressure.

Given the complexity and heterogeneity of AHF, prior attempts for risk modelling have not been easily adapted to clinical practice and generally have not had high discriminatory capacity. Most models were derived from demographic, clinical and biological data collected at admission (see Table 4) and only a few have used discharge data. Also, including a variable in a risk model will be negatively impacted by the quantity of missing data, and the rate of collection has varied among registries. Also, the current available models do not include the use of devices, which may carry a strong impact on prognosis in the long term.78 Actually, risk stratification by scoring methods remains only informative in the clinical decision-making process.

Limitation of Prognostic Models Despite numerous clinical and biological variables showing in registries and RCTs independent predictive value for post-discharge mortality, very few represent true targets for in-hospital therapies such as congestion, QRS duration, and non-cardiac comorbidities. In addition, heart rate measured at 1-week and 1-month post-discharge could be considered a target for If antagonist Ivabradine. Although in the RELAXAHF I trial markers of organ injury were associated with 180 days mortality and decreased as result of Serelaxin treatment, the RELAX II study did not confirm these beneficial effects. Another major limitation of prognostic models in AHF is the absence of prospective validation and lack of the impact studies. The main ways to evaluate or validate the performance of a prognostic model on a new dataset are to compare observed and predicted event rates for groups of patients (calibration) and to quantify the model’s ability to distinguish between patients who do or do not experience the event of interest (discrimination).77

heorghiade M, Shah AN, Vaduganathan M, et al. Recognizing G hospitalized heart failure as an entity and developing new therapies to improve outcomes: academics’, clinicians’, industry’s, regulators’, and payers’ perspectives. Heart Fail Clin 2013;9:285–90. DOI: 10.1016/j.hfc.2013.05.002; PMID: 23809415. Gheorghiade M, Peterson ED. Improving postdischarge outcomes in patients hospitalized for acute heart failure syndromes. JAMA 2011;305:2456–7. DOI: 10.1001/ jama.2011.836; PMID: 21673297. Butler J, Fonarow GC, Gheorghiade M. Need for increased awareness and evidence-based therapies for patients hospitalized for heart failure. JAMA 2013;310:2035–6. DOI: 10.1001/jama.2013.282815; PMID: 24240925.

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Furthermore, studies to evaluate the effect of using a prognostic model on current medical practice and on patient outcome would be informative and could lead to clinical implementation of such a model.79 An impact analysis can determine whether use of the model is better than usual care.80 This remains an unmet need.

Conclusion In AHF, the attempts to develop risk models are justified by the evidence that the risk of post-discharge mortality and rehospitalisation remains high. Furthermore, developing risk models would aid in targeting limiting resources to the appropriate patients. Even if the phenotypic heterogeneity of AHF patients makes it difficult to find a risk model suitable for all patients, some parameters recur in many models. However, in spite of limitations, prognostic models add to our understanding of the determinants of the course and outcome of patients with AHF. The impact of stratification of AHF patients on current clinical practice should be further evaluated in prospective studies.

Acknowledgement All of the authors acknowledge the important contribution of Professor Mihai Gheorghiade in this manuscript, which represents only one of his hundreds of research contributions. Mihai Gheorghiade was one of the most respected, quoted and liked cardiologists. Committed to excellence from early on in his career he was determined to excel in patient care, research and teaching. He felt that his highest calling was to mentor students, trainees, and junior colleagues. He was never more enthusiastic than when supporting new collaborations to take place between great minds in different countries. He passed away on 24 August 2017. The world of cardiology has lost an iconic figure, but his legacy of challenging concepts will live on for many years to come. n

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results from the Outcomes of a Prospective Trial of Intravenous Milrinone for Exacerbations of Chronic Heart Failure (OPTIME-CHF). Circ Heart Fail 2008;1:25–33. DOI: 10.1161/CIRCHEARTFAILURE.107.746933; PMID: 19808267. Vaduganathan M, Greene SJ, Ambrosy AP, et al. EVEREST trial investigators. Relation of serum uric acid levels and outcomes among patients hospitalized for worsening heart failure with reduced ejection fraction (from the efficacy of vasopressin antagonism in heart failure outcome study with tolvaptan trial). Am J Cardiol 2014;114:1713–21. DOI: 10.1016/ j.amjcard.2014.09.008; PMID: 25312638. Harjola VP, Mullens W, Banaszewski M, et al. Organ dysfunction, injury and failure in acute heart failure: from pathophysiology to diagnosis and management. A review on behalf of the Acute Heart Failure Committee of the Heart Failure Association (HFA) of the European Society of Cardiology (ESC). Eur J Heart Fail 2017;19:821–36. DOI: 10.1002/ ejhf.872; PMID: 28560717. Januzzi J, Filippatos G, Nieminen M, et al. Troponin elevation in patients with heart failure: on behalf of the third Universal Definition of Myocardial Infarction Global Task Force: Heart Failure Section. Eur Heart J 2012;33:2265–71. DOI: 10.1093/ eurheartj/ehs191; PMID: 22745356. Felker M, Hasselblad V, Tang W, et al. Troponin I in acute decompensated heart failure: insights from the ASCEND-HF study. Eur J Heart Fail 2012;11:1257–64. DOI: 10.1093/eurjhf/ hfs110; PMID: 22764184. Kociol RD, Pang PS, Gheorghiade M, et al. Troponin elevation in heart failure prevalence, mechanisms, and clinical implications. J Am Coll Cardiol 2010;56:1071–8. DOI: 10.1016/ j.jacc.2010.06.016; PMID: 20863950. You JJ, Austin PC, Alter DA, et al. Relation between cardiac troponin I and mortality in acute decompensated heart failure. Am Heart J 2007;153:462–70. DOI: 10.1016/ j.ahj.2007.01.027; PMID: 17383280. Parenti N, Bartolacci S, Carle F, et al. Cardiac troponin I as prognostic marker in heart failure patients discharged from emergency department. Intern Emerg Med 2008;3:43–7. DOI: 10.1007/s11739-008-0092-8; PMID: 18273567. La Vecchia L, Mezzena G, Zanolla L, et al. Cardiac troponin I as diagnostic and prognostic marker in severe heart failure. J Heart Lung Transplant 2000;19:644–52. PMID: 10930813 Wettersten N, Maisel J. Role of cardiac troponin levels in acute heart failure. Cardiac Fail Rev 2015;1:102–6. DOI: 10.15420/cfr.2015.1.2.102; PMID: 28785441. O’Connor CM, Fiuzat M, Lombardi C, et al. Impact of serial troponin release on outcomes in patients with acute heart failure analysis from the PROTECT pilot study. Circ Heart Fail 2011;4:724–32. DOI: 10.1161/CIRCHEARTFAILURE.111.961581; PMID: 21900185. Metra M, Cotter G, Davison BA, et al. Effect of serelaxin on cardiac, renal, and hepatic biomarkers in the Relaxin in Acute Heart Failure (RELAX-AHF) development program: correlation with outcomes. J Am Coll Cardiol 2013;61:196–206. DOI: 10.1016/j.jacc.2012.11.005; PMID: 23273292. Metra M, Cotter G, Gheorghiade M, et al. The role of the kidney in heart failure. Eur Heart J 2012;33:2135–42. DOI: 10.1093/eurheartj/ehs205; PMID: 22888113. Greene SJ, Gheorghiade M, Vaduganathan M, et al. Haemoconcentration, renal function, and post-discharge outcomes among patients hospitalized for heart failure with reduced ejection fraction: insights from the EVEREST trial. Eur J Heart Fail 2013;15:1401–11. DOI: 10.1093/eurjhf/hft110; PMID: 23845795. Metra M, Davison B, Bettari L, et al. Is worsening renal function an ominous prognostic sign in patients with acute heart failure? The role of congestion and its interaction with renal function. Circ Heart Fail 2012;5:54–62. DOI: 10.1161/ CIRCHEARTFAILURE.111.963413; PMID: 22167320. Voors AA, Dittrich HC, Massie BM, et al. Effects of the adenosine A1 receptor antagonist rolofylline on renal function in patients with acute heart failure and renal dysfunction: results from PROTECT (Placebo-Controlled Randomized Study of the Selective Adenosine A1 Receptor Antagonist Rolofylline for Patients Hospitalized with Acute Decompensated Heart Failure and Volume Overload to Assess Treatment Effect on Congestion and Renal Function). J Am Coll Cardiol 2011;57: 1899–1907. DOI: 10.1016/j.jacc.2010.11.057; PMID: 21545947. Ambrosy AP, Gheorghiade M, Bubenek S, et al. The predictive value of transaminases at admission in patients hospitalized for heart failure: findings from the RO-AHFS registry. Eur Heart J Acute Cardiovasc Care 2013;2:99–108. DOI: 10.1177/2048872612474906; PMID: 24222818. Ambrosy AP, Vaduganathan M, Huffman MD, et al. Clinical course and predictive value of liver function tests in patients hospitalized for worsening heart failure with reduced ejection fraction: an analysis of the EVEREST trial. Eur J Heart Fail 2012;14:302–11. DOI: 10.1093/eurjhf/hfs007; PMID: 22357577. van Deursen VM, Damman K, Hillege HL, et al. Abnormal liver function in relation to hemodynamic profile in heart failure patients. J Card Fail 2010;16:84–90. DOI: 10.1016/j. cardfail.2009.08.002; PMID: 20123323. Shinagawa H, Inomata T, Koitabashi T, et al. Prognostic significance of increased serum bilirubin levels coincident with cardiac decompesantion in chronic heart failure. Circ J. 2008;72:364–9. PMID: 18296830 Fonarow GC, Abraham WT, Albert NM, et al. Factors identified as precipitating hospital admissions for heart failure and clinical outcomes: findings from OPTIMIZE-HF. Arch Intern Med

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2008;168:847–54. DOI: 10.1001/archinte.168.8.847; PMID: 18443260. Targher G, Dauriz M, Laroche C, et al. In-hospital and 1-year mortality associated with diabetes in patients with acute heart failure: results from the ESC-HFA Heart Failure LongTerm Registry. Eur J Heart Fail 2017;19:54–65. DOI: 10.1002/ ejhf.679; PMID: 27790816. Nathaniel M, Virani HS, Ceconi C, et al. Heart failure and chronic obstructive pulmonary disease: the challenges facing physicians and health services. Eur Heart J 2013;34: 2795–803. DOI: 10.1093/eurheartj/eht192; PMID: 23832490. De Blois J, Simard S, Atar D, et al. Norwegian Heart Failure Registry COPD predicts mortality in HF: the Norwegian Heart Failure Registry. J Card Fail 2010;16:225–9. DOI: 10.1016/ j.cardfail.2009.12.002; PMID: 20206897. Mentz RJ, Schmidt PH, Kwasny MJ, et al. The impact of chronic obstructive pulmonary disease in patients

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hospitalized for worsening heart failure with reduced ejection fraction: an analysis of the Everest trial, J Card Fail, 2012;18:515–23). 74. Arnaudis B, Lairez O, Escamilla R, et al. Impact of chronic obstructive pulmonary disease severity on symptoms and prognosis in patients with systolic heart failure. Clin Res Cardiol 2012;101:717–26. DOI: 10.1007/s00392-012-0450-4; PMID: 22484345. 75. Mentz RJ, Greene SJ1, Ambrosy AP, et al. Clinical profile and prognostic value of anemia at the time of admission and discharge among patients hospitalized for heart failure with reduced ejection fraction: findings from the EVEREST trial. Circ Heart Fail 2014;7:401–8. DOI: 10.1161/CIRCHEARTFAILURE.113.000840; PMID: 24737459. 76. Swedberg K, Young JB, Anand IS, et al. Treatment of anemia with darbepoetin alfa in systolic heart failure. N Engl J Med

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2013;368:1210–9. DOI: 10.1056/NEJMoa1214865; PMID: 23473338. Altman DC, Vergouwe Y, Royston P, et al. Prognosis and prognostic research: validating a prognostic model. BMJ 2009;338:b605. PMID: 19477892. Giamouzis G, Kalogeropoulos A, Georgiopoulou V, et al. Hospitalization epidemic in patients with heart failure: risk factors, risk prediction, knowledge gaps, and future directions. J Card Fail 2011;17:54–75. DOI: 10.1016/j. cardfail.2010.08.010; PMID: 21187265. Moons KGM, Altman DG, Vergouwe Y, et al. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ 2009;338:b606. PMID: 19502216. Passantino A, Monitillo F, Iacoviello M, et al. Predicting mortality in patients with acute heart failure: Role of risk scores. World J Cardiol 2015 26;7:902–11. DOI: 10.4330/wjc. v7.i12.902; PMID: 26730296.

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Clinical Practice

Quality of Physician Adherence to Guideline Recommendations for Life-saving Treatment in Heart Failure: an International Survey Martin R Cowie and Michel Komajda Imperial College London (Royal Brompton Hospital), London, UK and Institute of Cardiology, Pitie-Salpetriere Hospital Group, Paris, France

Abstract QUALIFY (QUality of Adherence to guideline recommendations for LIFe-saving treatment in heart failure surveY) showed that good physician adherence to guideline recommendations for angiotensin converting enzyme inhibitors/angiotensin receptor blockers, beta blockers, mineralocorticoid receptor antagonists and ivabradine, with prescription of at least 50 % of recommended dosages, was associated with better 6-month outcomes than moderate or poor adherence. Poor adherence was associated with higher all-cause mortality (hazard ratio 2.21; 95 % CI [1.42–3.44]; p=0.001) and combined heart failure hospitalisation or death (hazard ratio 1.26; 95 % CI [1.08–1.71]; p=0.024) compared with good adherence. Heart failure hospitalisation is a good opportunity to review a patient’s medication and to optimise guideline adherence.

Keywords Heart failure, guidelines, adherence, medication, dosage, mortality, hospitalisation Disclosure: Martin R. Cowie and Michel Komajda have received speaker fees and provided consultancy advice to Servier. Acknowledgements: Writing assistance was provided by Jenny Bryan and funded by Servier. The author thanks Dr Irina Elyubaeva for her support in setting up the QUALIFY registry. Received: 12 September 2017 Accepted: 31 October 2017 Citation: Cardiac Failure Review 2017;3(2):130–3. DOI: 10.15420/cfr.2017:13:1 Correspondence: Martin R. Cowie, Professor of Cardiology, Imperial College London (Royal Brompton Hospital), Royal Brompton Hospital, Sydney Street, London SW3 6HP, UK. E: m.cowie@imperial.ac.uk

Advances in diagnosis and treatment have improved the outlook for the estimated 26 million patients with heart failure (HF) worldwide,1 but there remains a continuing need for further reduction in mortality and hospitalisation and the associated social and financial consequences of the disease. In a European study of outcomes after HF hospitalisation, 17 % of patients died within 12 months and 44 % were rehospitalised,2 and the most recent data from the British National Heart Failure Audit showed in-hospital mortality of 8.9 % and mortality at 1 year of 26.7 % in those surviving to leave hospital.3 Patients are at greatest risk in the first 30 days after hospitalisation,4 though there is some evidence that good adherence to HF treatment guidelines may improve clinical outcomes.5 The latest HF guidelines from the European Society of Cardiology (ESC) recommend key pharmacological therapies for patients with HF with reduced ejection fraction (HFrEF) in order to reduce mortality and hospitalisation.6 These include angiotensin converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs) if ACEIs are not tolerated, beta blockers (BBs), mineralocorticoid receptor antagonists (MRAs), ivabradine for patients in sinus rhythm with a heart rate ≥70 bpm, and sacubitril/valsartan as a replacement for ACEIs in ambulatory patients with HFrEF who remain symptomatic despite optimal ACEI, BB and MRA treatment (Figure 1).6 Yet, even when prescription levels of guidelines-based HF treatment are high, patients may fail to reach target doses of potentially lifesaving medicines,7 and there is some evidence that suboptimal dosing may adversely influence outcomes.8,9

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QUALIFY (QUality of Adherence to guideline recommendations for LIFe-saving treatment in heart failure surveY) was initiated to improve understanding of the impact of physicians’ adherence to guideline recommended HF medications, including dosages, on clinical outcomes and to consider the implications for optimising patient care.10

QUALIFY: A Global Perspective on Physicians’ Adherence to HF Guidelines QUALIFY is an international, prospective, observational, longitudinal survey of physicians’ adherence to ESC guidelines recommendations for five key classes of HF medications at the time the study was initiated – ACEIs/ARBs, BBs, MRAs and ivabradine.11 Physicians’ adherence scores and follow-up data have been reported for 6,669 patients with chronic heart failure and left ventricular ejection fraction ≤40 % who had been hospitalised for worsening HF in the previous 1–15 months.12 Patients were treated at 547 centres in 36 countries in Africa, Asia, Australia, Europe, the Middle East and North, Central and South America (Figure 2).10,12

Measuring Physicians’ Adherence The focus of QUALIFY was on physicians’ adherence to HF guidelines and did not include patient adherence to prescribed treatment. The adherence score used in QUALIFY was the ratio of the treatment actually prescribed to the treatment that should theoretically have been prescribed. The latter took account of eligibility criteria, guidelines-based contraindications to drugs or treatments and use in ≥50 % of recommended dosages. Target doses were defined by ESC guidelines when available11 and, in the case of ivabradine, according

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QUALIFY: Adherence and Outcomes in Heart Failure Figure 1: QUALIFY Centres Worldwide

North America Canada

Europe Ireland Portugal Spain France Austria

Germany Denmark Greece Lithuania

Hungary Slovakia Belarus Russia Romania Ukraine Poland

Asia Brunei China Korea Malaysia Thailand

Caucasus Armenia Azerbaijan Georgia

South America Ecuador

Africa Morocco

Middle East Bahrain Jordan Kuwait Kazakhstan Oman Turkey Qatar Lebanon UAE Egypt

Australia

Orange = countries participating in the QUALIFY registry; QUALIFY = QUality of Adherence to guideline recommendations for LIFe-saving treatment in heart failure surveY.

to the therapeutic regimen used in the systolic HF treatment with the I(f) inhibitor ivabradine trial.13 For each medicine, a patient was scored 0 points for non-prescription in the absence of contraindications, 0.5 points for the use of <50 % of target dosage (TD) (<100 % of TD for MRA as most patients received ≥50 % of TDs of MRA) or 1 point for use in ≥50 % of TDs (TD for MRA). Physicians’ adherence scores ranged from 0 (very poor) to 1 (excellent) and were defined at three levels: good adherence (score=1); moderate adherence (score=>0.5 to <1); and poor adherence (score=≤0.5).

Global Adherence and Effects of Comorbidities The global adherence score at baseline (ACEIs/ARBs, BBs, MRAs and ivabradine) was good in 23 % of patients, moderate in 55 % of patients and poor in 22 % of patients. A slightly higher proportion of women were in the poor adherence group rather than the good or moderate groups (p=0.008) compared with men, and significantly more patients with a good adherence score were Caucasian rather than Asian or Middle Eastern (p<0.001).12 Patients with some common comorbidities were more likely to have a good adherence score. These included atrial fibrillation/flutter (p<0.001), coronary artery bypass graft (p=0.002), diabetes mellitus (p<0.001), dyslipidaemia (p<0.001), history of hypertension (p<0.001), asthma or chronic obstructive pulmonary disease (p<0.001), or chronic kidney disease (p<0.001). However, a significantly higher proportion of patients with a poor adherence score had a history of cancer (p=0.043). Good adherence was associated with better New York Heart Association class of HF.

Adherence and Clinical Outcomes Good physicians’ adherence score for key HF medicines, with prescription of ≥50 % of recommended dosages, was associated with

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better clinical outcomes at 6-month follow-up than moderate or poor adherence score (Figure 3).12 Multivariate adjustment for baseline differences in patient characteristics showed that poor baseline adherence score was associated with significantly higher all-cause mortality than good adherence score (hazard ratio [HR] 2.21; 95 % CI [1.42–3.44]; p=0.001). Poor adherence score was also associated with significantly higher cardiovascular (CV) mortality (HR 2.27; 95 % CI [1.36–3.77]; p=0.003; HF mortality: HR 2.26; 95 % CI [1.21–4.2; p=0.032); combined HF hospitalisation or HF death (HR 1.26; 95 % CI [1.08–1.71]; p=0.024) and CV hospitalisation or CV death (HR 1.35; 95 % CI [1.08–1.69]; p=0.013). In addition, there was a strong trend between poor adherence score and HF hospitalisation (HR 1.32; 95 % CI [1.04–1.68]; p=0.069).

Implications for HF Care The QUALIFY 6-month data provide strong support for physicians’ adherence to guideline-recommended doses of key pharmacological therapies for HF. The global nature of the study suggests that these findings are relevant to clinicians working in very different healthcare settings.

Importance of Evidence-based HF Drug Dosages The current data underline the importance of using HF medication in recommended doses. In an earlier QUALIFY paper, we reported good overall adherence to ACEI/ARB, BB, MRA and ivabradine treatment of 67 %,10 and this was in line with data from the British National Heart Failure Audit. 3 However, as shown in the current analysis, good adherence was achieved in fewer than one-quarter of patients when drug dosages were taken into account. Few

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Clinical Practice Figure 2: European Society of Cardiology Therapeutic Algorithm for a Patient with Symptomatic HFrEF

Figure 3: A) All Cause Death or B) CV Death or CV Hospitalisation in Heart Failure Patients with Good, Moderate and Poor Physician Adherence to Guidelines

Class I

Patient with symptomatica HFrEFb

1.0

Therapy with ACE-Ic and beta-blocker (Up-titrate to maximum tolerated evidence-based doses)

Still symptomatic and LVEF ≤35 %

Log rank p=0.0041

Survival probability

Class IIa

Good (Adherence score = 1) Moderate (0.5 < Adherence score < 1) Poor (Adherence score ≤ 0.5) 0.9 0

Time to event (months) Number at risk

Add MR antagonistd,e (up-titrate to maximum tolerated evidence-based dose) Yes

Good

1543

1187

1157

1112

1050

909

474

Moderate

3627

2618

2514

2381

2230

1961

1053

Poor

1493

906

862

809

726

638

323

Still symptomatic and LVEF ≤35 %

1.0

Yes

Able to tolerate ACEI (or ARB)f,g

Sinus rhythm, QRS duration ≥130 msec

Sinus rhythm,h HR ≥70 bpm

ARNI to replace ACE-I

Evaluate need for CRTi,j

Ivabradine

Survival probability

If LVEF ≤35 % despite OMT or a history of symptomatic VT/VF, implant ICD

Diuretics to relieve symptoms and signs of congestion

Yes

Log rank p=0.0090

0.9 Good (Adherence score = 1) Moderate (0.5 < Adherence score < 1) Poor (Adherence score ≤ 0.5)

0.8

Time to event (months) Number at risk

These above treatments may be combined if indicated

Good

1475

1106

1060

1007

931

809

422

Moderate

3464

2424

2303

2139

1969

1706

911

Poor

1452

849

798

735

649

569

283

Source: Komadja, et al., 2017. Resistant symptoms Yes Consider digoxin or H-ISDN or LVAD, or heart transplantation

No No further action required Consider reducing diuretic dose

Green indicates a class I recommendation and yellow a class IIa recommendation. ACEI = angiotensin-converting enzyme inhibitor; ARB = angiotensin receptor blocker; ARNI = angiotensin receptor neprilysin inhibitor; BNP = B-type natriuretic peptide; CRT = cardiac resynchronisation therapy; HF= heart failure; HFrEF = heart failure with reduced ejection fraction; H-ISDN = hydralazine and isosorbide dinitrate; HR = heart rate; ICD = implantable cardioverter defibrillator; LBBB = left bundle branch block; LVAD = left ventricular assist device; LVEF = left ventricular ejection fraction; MR = mineralocorticoid receptor; NT-proBNP = N-terminal pro-B type natriuretic peptide; NYHA = New York Heart Association; OMT = optimal medical therapy; VF = ventricular fibrillation; VT = ventricular tachycardia. Source: Ponikowski, et al., 2016. aSymptomatic = NYHA Class II-IV. bHFrEF = LVEF<40 %. cIf ACEI not tolerated/contra-indicated, use ARB. dIf MR antagonist not tolerated/ contra-indicated, use ARB. eWith a hospital admission for HF within the last 6 months or with elevated natriuretic peptides (BNP>250 pg/ml or NTproBNP>500 pg/ml in men and 750 pg/ml in women). fWith an elevated plasma natriuretic peptide level (BNP≥150 pg/ml or plasma NT-proBNP≥600 pg/ml, or if HF hospitalisation within recent 12 months plasma BNP≥100 pg/ml or plasma NT-proBNP≥400 pg/mL). gIn doses equivalent to enalapril 10 mg b.i.d. hWith a hospital admission for HF within the previous year. iCRT is recommended if QRS≥130 ms and LBBB (in sinus rhythm). jCRT should/may be considered if QRS≥130 ms with non-LBBB (in a sinus rhythm) or for patients in AF provided a strategy to ensure biventricular capture is in place (individualised decision). Source: Ponikowski, et al., 2016. Published with permission from Wiley.

other studies have included dosages in adherence calculations, but the recent BIOSTAT-CHF study in 11 European countries also showed that a minority of patients reached target doses recommended by the ESC guidelines.14 In BIOSTAT-CHF, patients receiving >50 % of recommended doses of ACEIs and BBs had better outcomes than those taking lower doses.14 Similarly, in the Austrian Heart Failure Registry, improved guideline adherence related to dose escalation towards optimal levels was associated with reduced long-term mortality in ambulatory HFrEF patients surviving 1 year after registration.15

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Impact of Patient Frailty and Comorbidities The current QUALIFY data demonstrate the impact of patient frailty and HF severity on prescribing decisions as well as the complexity of guideline adherence. Poor adherence was more common in older patients and in those with severe HF (New York Heart Association Class IV) or cancer, suggesting that patient frailty may have played a role. Perhaps surprisingly, more patients had comorbidities in the good adherence group than in the moderate and poor adherence groups. It might have been expected that, with more medicines to prescribe, physicians’ adherence would suffer, but results of other studies have shown conflicting results, with comorbidities having both negative and positive effects on adherence.16–18 It is possible that in more complex patients, more care is taken to ensure that drug therapies are optimised.

Need for Communication and Education Physicians’ awareness of guideline recommendations is fundamental for successful implementation, and communication campaigns improve the rate of prescription of recommended medications.19 In QUALIFY, a large majority of participants were cardiologists, so it is unclear whether adherence levels in the study reflect those in general practice. Certainly, early experience from the international Optimize Heart Failure Care Program has shown that it is possible to raise HF awareness and optimise HF pharmacological therapy through the use of simple clinician- and patient-focused educational tools.20

Access and Affordability Issues Although QUALIFY has highlighted the importance of physicians’ adherence to guideline-recommended HF therapies, access to

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QUALIFY: Adherence and Outcomes in Heart Failure and affordability of HF treatments will inevitably affect adherence, depending on the economic status of a country. General organisation of healthcare systems and the role of reimbursement schemes, incentives or obligations to comply with guideline recommendations will all play a role in whether patients get the life-saving therapies they need.21,22

optimisation is sustained following discharge back into the community. With growing evidence that simple educational initiatives – for clinicians and patients – can help to optimise the use of key HF medicines, there is potential for another great leap forward in reducing the burden of HF for patients, families, clinicians and healthcare providers.

Summary Conclusions To make further progress in reducing mortality and hospitalisation for HF patients, it is essential that physicians prescribe evidencebased therapies according to international guidelines, and QUALIFY has confirmed the importance of adherence to recommended doses of key medicines for improving clinical outcomes. Although hospitalisation is undesirable as it is indicative of worsening HF, it is a good opportunity to update a patient’s medication to comply with guideline recommendations and to take steps to ensure that treatment

mbrosy AP, Fonarow GC, Butler J, et al. The global health A and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries. J Am Coll Cardiol 2014;63:1123–33. DOI: 10.1016/j.jacc.2013. 11.053; PMID: 24491689 Maggioni AP, Dahlstrom U, Filippatos G, et al. EURObservational Research Programme: regional differences and 1-year follow-up results of the Heart Failure Pilot Survey (ESC-HF Pilot). Eur J Heart Fail 2013;15:808–17. DOI: 10.1093/eurjhf/hft050; PMID: 23537547 British Society for Heart Failure. National Heart Failure Audit, April 2015–March 2016. Available at: www.ucl.ac.uk/nicor/ audits/heartfailure/documents/annualreports/annual-report2015-6-v8.pdf (accessed 29 August 2017) Marti NC, Fonarow GC, Gheorghiade M, Butler J. Timing and duration of interventions in clinical trials for patients with hospitalized heart failure. Circ Heart Fail 2013;6:1095–101. DOI: 10.1161/CIRCHEARTFAILURE.113.000518; PMID: 24046476 Komajda M, Lapuerta P, Hermans N, et al. Adherence to guidelines is a predictor of outcome in chronic heart failure: the MAHLER survey. Eur Heart J 2005;26:1653–9. DOI: 10.1093/ eurheartj/ehi251; PMID: 15827061 Ponikowski P, Voors AA, Anker SD, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail 2016;18:891–975. DOI: 10.1002/ejhf.592; PMID: 27207191 Maggioni AP, Anker SD, Dahlström U, et al. Are hospitalized or ambulatory patients with heart failure treated in accordance with European Society of Cardiology guidelines? Evidence from 12,440 patients of the ESC Heart Failure Long-Term Registry. Eur J Heart Fail 2013;15:1173–84. DOI: 10.1093/eurjhf/ hft134; PMID: 23978433 Packer M, Poole-Wilson PA, Armstrong PW, et al. Comparative effects of low and high doses of the angiotensin-converting enzyme inhibitor, lisinopril, on morbidity and mortality

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QUALIFY (QUality of Adherence to guideline recommendations for LIFe-saving treatment in heart failure surveY) has demonstrated that good physician adherence to guideline recommendations for key heart failure medication (angiotensin converting enzyme inhibitors/ angiotensin receptor blockers, beta blockers, mineralocorticoid receptor antagonists and ivabradine) is associated with lower patient mortality and hospitalisation rates across the world. It underlines the importance of physicians’ adherence to recommended drugs and drug dosages in improving clinical outcomes for patients with heart failure. n

in chronic heart failure. ATLAS Study Group. Circulation 1999;100:2312–8. DOI: 10.1161/01.CIR.100.23.2312; PMID: 10587334 Konstam MA, Neaton JD, Dickstein K, et al. Effects of highdose versus low-dose losartan on clinical outcomes in patients with heart failure (HEAAL study): a randomised, double-blind trial. Lancet 2009;374:1840–8. DOI: 10.1016/ S0140-6736(09)61913-9; PMID: 19922995 Komajda M, Anker SD, Cowie MR, et al. Physicians’ adherence to guideline-recommended medications in heart failure with reduced ejection fraction: data from the QUALIFY global survey. Eur J Heart Fail 2016;18:514–22. DOI: 10.1002/ejhf.510; PMID: 27095461 McMurray JJ, Adamopoulos S, Anker SD, et al. ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur Heart J 2012;33:1787–847. DOI: 10.1093/eurheartj/ehs104; PMID: 22611136 Komajda M, Cowie MR, Tavazzi L, et al. Physicians’ guideline adherence is associated with better prognosis in outpatients with heart failure with reduced ejection fraction: the QUALIFY international registry. Eur J Heart Fail 2017; DOI: 10.1002/ ejhf.887; epub ahead of print Swedberg K, Komajda M, Böhm M, et al. Ivabradine and outcomes in chronic heart failure (SHIFT): a randomized placebo-controlled trial. Lancet 2010;376:875–85. DOI: 10.1016/ S0140-6736(10)61198-1; PMID: 20801500 Ouwerkerk W, Voors AA, Anker SD, et al. Determinants and clinical outcome of uptitration of ACE-inhibitors and beta-blockers in patients with heart failure: a prospective European study. Eur Heart J 2017;38:1883–90. DOI: 10.1093/ eurheartj/ehx026; PMID: 28329163 Poelzl G, Altenberger J, Pacher R, et al. Dose matters! Optimisation of guideline adherence is associated with lower

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mortality in stable patients with chronic heart failure. Int J Cardiol 2014;175:83–9. DOI: 10.1016/j.ijcard.2014.04.255; PMID: 24857326 Granger BB, Ekman I, Granger CB, et al. Adherence to medication according to sex and age in the CHARM programme. Eur J Heart Fail 2009;11:1092–8. DOI: 10.1093/ eurjhf/hfp142; PMID: 19875409 Epstein M, Reaven NL, Funk SE, et al. Evaluation of the treatment gap between clinical guidelines and the utilization of renin-angiotensin-aldosterone system inhibitors. Am J Manag Care 2015;21(Suppl):S212–20. PMID: 26619183 Mathes T, Jaschinski T, Pieper D. Adherence influencing factors – a systematic review of systematic reviews. Arch Public Health 2014;72:37. DOI: 10.1186/2049-3258-72-37; PMID: 25671110 De Groote P, Isnard R, Clerson P, et al. Improvement in the management of chronic heart failure since the publication of the updated guidelines of the European Society of Cardiology. The Impact-Reco-Programme. Eur J Heart Fail 2009;11:85–91. DOI: 10.1093/eurjhf/hfn005; PMID: 19147461 Cowie MR, Lopatin YM, Saldarriaga C, et al. The Optimize Heart Failure Care Program: Initial lessons from global implementation. Int J Cardiol 2017;236:340–4. DOI: 10.1016/ j.ijcard.2017.02.033; PMID: 28214078 Peterson PN, Chan PS, Spertus JA, et al. Practice-level variation in use of recommended medications among outpatients with heart failure: Insights from the NCDR PINNACLE program. Circ Heart Fail 2013;6:1132–8. DOI: 10.1161/ CIRCHEARTFAILURE.113.000163; PMID: 24130004 Maggioni AP, Van Gool K, Biondi N, et al. Appropriateness of prescriptions of recommended treatments in Organisation for Economic Co-operation and Development health systems: findings based on the long-term registry of the European Society of Cardiology on Heart Failure. Value Health 2015;18:1098–104. DOI: 10.1016/j.jval.2015.08.005; PMID: 26686796

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Sleep-Disordered Breathing During Congestive Heart Failure: To Intervene or Not to Intervene? Ali Valika and Maria Rosa Costanzo Advocate Medical Group – Midwest Heart Specialists, Advocate Heart Institute, Oak Brook, IL, USA

Abstract Sleep-disordered breathing is common in heart failure patients and is associated with increased morbidity and mortality. Central sleep apnea occurs more commonly in heart failure-reduced ejection fraction, and obstructive sleep apnea occurs more frequently in heart failure with preserved ejection fraction. Although the two types of sleep-disordered breathing have distinct pathophysiologic mechanisms, both contribute to abnormal cardiovascular consequences. Treatment with continuous positive airway pressure for obstructive sleep apnea in heart failure has been well defined, whereas treatment strategies for central sleep apnea in heart failure continue to evolve. Unilateral transvenous neurostimulation has shown promise for the treatment of central sleep apnea. In this paper, we examine the current state of knowledge of treatment options for sleep-disordered breathing in heart failure.

Keywords Sleep-disordered breathing, sleep apnea, central sleep apnea, obstructive sleep apnea, heart failure. Disclosure: The authors have no conflicts to declare. Received: 16 July 2017 Accepted: 12 September 2017 Citation: Cardiac Failure Review 2017;3(2):134–9 DOI: 10.15420/cfr.2017:7:1 Correspondence: Ali A. Valika, Advocate Medical Group – Midwest Heart Specialist, Medical Director Heart Failure, Elmhurst Hospital, 133 Brush Hill Road, Suite #200, Elmhurst, IL 60126, USA. E: ali.valika@advocatehealth.com

Sleep-disordered breathing (SDB) is common in heart failure (HF) patients and is associated with increased morbidity and mortality. Abnormal sleep patterns are often characterised by cycles of significant pauses in breathing and partial neurological arousals that lead to maladaptive neurohormonal activation. SDB is broadly classified into two types: obstructive sleep apnea (OSA) and central sleep apnea (CSA). The former occurs in both the general and HF populations, whereas the latter is more often associated with HF.1,2 Whereas the benefits of treatment for OSA have been repeatedly confirmed in the literature, the effectiveness and safety of treatments for CSA in HF remains controversial. 3,4 This brief review will explore the pathophysiology of SDB in HF, and discuss the effects of various treatment options specifically in this patient population.

Definition and Epidemiology An apnea is defined as the absence of inspiratory airflow for at least 10 seconds. A hypopnea is a lesser decrease in airflow, associated with a drop in arterial oxygen saturation and/or an arousal. Apneas and hypopneas are classified according to type of SDB in which they occur: OSA occurs when upper airway occlusion occurs with continued activity of inspiratory thoracic pump muscles; CSA occurs when there is a reduction in neural stimulus to thoracic respiratory muscles (diaphragm and intercostal muscles), leading to a reduction/or absence of the breathing rhythm, without upper airway obstruction.5 The apnea-hypopnea index (AHI), defined as the mean number of apnea and/or hypopnea episodes that occur during sleep divided by the number of hours of sleep, expressed as events/h, defines severity of SDB. Mild severity is defined as an AHI between 5 and 15 events/h, moderate severity as an AHI ≥15 events/h but <30

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events/h, and severe sleep apnea as ≥30 events/h. Numerous studies have shown that mortality rises as the AHI increases.6,7 HF is one of the most common underlying conditions for SDB in adults, and more than 50 % of HF patients have SDB.8,9 This disorder occurs in both HF with reduced ejection fraction (HFrEF), as well as HF with preserved ejection fraction (HFpEF). In a meta-analysis of several realworld sleep studies, the combined incidence of sleep apnea in HF is estimated at 53 % in HFrEF and 48 % in HFpEF patients. CSA occurs more frequently in HFrEF (34 % of patients), and OSA occurs more frequently in HFpEF (25 % of patients).10 The OSA phenotype occurs more commonly in the general population as well, with 34 % men and 17 % women being affected.11 Obesity and advancing age are the major risk factor for OSA.12 Fluid overload states have also been identified as a risk factor because nocturnal fluid shifts to the neck and chest can cause collapse of the pharynx.13 The CSA phenotype predominates in HF patients, with estimated rates of 30–50 %. This prevalence is likely to be underestimated because symptoms of CSA may be indistinguishable from those of underlying HF.2 A number of risk factors have been identified for the development of CSA in HF, including male sex, higher New York Heart Association (NYHA) functional class, lower ejection fraction, higher B-type natriuretic peptide levels, waking hypocapnia (arterial partial pressure of carbon dioxide [PaCO2] <38 mmHg), higher prevalence of atrial fibrillation and frequent nocturnal ventricular arrhythmias.9,12,14 Patients with CSA are often distinct from those with OSA, in that they are often not obese, often have no history of snoring, and yet have more daytime fatigue.9,10 Although no screening tool has been validated to identify CSA in HF, this SDB should be suspected when one or more of the above abnormalities are present.15

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Sleep-Disordered Breathing in Congestive Heart Failure Pathophysiology Obstructive Sleep Apnea The pathogenesis of OSA stems from a complex interaction between unfavourable anatomic upper airway susceptibility and sleep-related changes in upper airway function. Sleep is associated with a decreased metabolic rate, loss of the wakefulness drive to breathe, and a subsequent decrease in ventilatory neural output to respiratory muscles, including upper airway muscles.16 In patients with unfavourable anatomy, such as alterations in craniofacial structures, enlarged tonsils, upper airway oedema, decreased lung volume due to pulmonary oedema and obesity, vulnerability to upper airway obstruction is more common.5 With reduction in the activity of the genioglossus muscle at the onset of sleep, the tongue falls backward, and individuals with altered mechanical properties of the upper airway are prone to upper airway obstruction.5,17 Non-anatomic factors, such as upper airway dilator muscle dysfunction, heightened chemosensitivity to CO2 and low arousal threshold, have also been implicated.5

Central Sleep Apnea Commonly seen in HF patients, CSA is distinguished by the temporary withdrawal of central (brainstem-mediated) respiratory drive that results in the cessation of respiratory muscle activity and airflow. The SDB pattern that subsequently results in CSA commonly manifests in the form of Cheyne–Stokes respiration, a form of periodic breathing with recurring cycles of crescendo decrescendo ventilation that culminates in a prolonged apnea or hypopnea episode.1 The pathogenesis of CSA in HF is complex and remains incompletely understood. However, a substantial body of research suggests that an increased respiratory control response to changes in PaCO2 above and below the apneic threshold is central to the pathogenesis of CSA in HF.18,19 The respiratory control system maintains tight regulation of levels of O2 and CO2, and during sleep PaCO2 becomes the primary stimulus for ventilation. Therefore, any increase in PaCO2 will stimulate ventilation, whereas any decrease in PaCO2 will suppress it. Respiration can cease altogether if PaCO2 falls below the tightly regulated level called the apneic threshold. Normally, at the onset of sleep, ventilation decreases and PaCO2 increases. This keeps the prevailing level of PaCO2 well above the apneic threshold, allowing normal, rhythmic breathing to continue throughout the night. However, it is important to consider that it may not be the absolute value of steady state PaCO2 that increases the likelihood of developing central apnea, but rather the absolute difference between the prevailing PaCO2 and apneic threshold PaCO2 that is more important.20 Furthermore, not only may there be static hyperventilation in HF, alterations in various components of the negative feedback system that control breathing also increase the likelihood of developing periodic breathing, during both sleep and wakefulness. Factors such as prolonged circulatory time, increased chemoreceptor gain, and exaggerated responses to ventilation provoke instability in the negative feedback loop and consequent abnormal periodic breathing and CSA.21 The neurohormonal and haemodynamic alterations occurring in HF also contribute to the development and progression of CSA. The three key factors leading to CSA in HF include hyperventilation, circulatory delay, and brain responses to altered concentrations of O2 and CO2.1 Factors leading to chronic hyperventilation typical of HF patients include pulmonary interstitial congestion due to rostral fluid displacement occurring in the supine position, activation of pulmonary stretch receptors stimulating increase in ventilation, and activation of peripheral chemoreceptors triggering an exaggerated response to

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lowered CO2 levels, a mechanism which contributes to the cyclical pattern of hyperventilation – hypoventilation and apnea.1 Reduced cardiac output in HF patients delays detection of changes in blood gases between the peripheral and the central chemoreceptors, further exacerbating the cyclical pattern of periodic breathing and increasing the duration of apneic events seen with CSA.22 Cerebrovascular reactivity is directly influenced by changes in PaCO2, and blunted responses are noted in HF and CSA patients, leading to an ineffective ability to dampen ventilatory hypoventilation or hyperventilation overshoot, perpetuating episodes of CSA.23

Pathological Consequences The repeated episodes of apnea, hypoxia, re-oxygenation, and arousal throughout the night have serious pathophysiological consequences, including further sympathetic nervous system (SNS) activation, oxidative stress, systemic inflammation and endothelial dysfunction. Repeated bursts of sympathetic activity are noted in patients with SDB, manifesting with increased urinary nocturnal norepinephrine secretion as well as increased daytime muscle sympathetic nerve activity.24,25 The link between increased SNS activity and higher mortality in HF is well known.26–29 Arterial blood gas abnormalities, excessive arousals, and large intrathoracic pressure swings are known to occur during SDB.5 These pressure changes may increase left ventricular afterload, increase myocardial oxygen demand and impede stroke volume. The exaggerated intrathoracic pressure changes during SDB can lead to increased transmural pressure exposure to the thin walled atria, leading to atrial stretch and susceptibility of atrial fibrillation.30 Increased oxidative stress and development of reactive oxygen species in the setting of repeated hypoxia-reoxygenation episodes have been postulated to occur with SDB.31 Several studies have demonstrated that patients with sleep apnea have increased levels of pro-inflammatory cytokines, cellular adhesion molecules, and activated circulating neutrophils.31–33 These mechanisms can lead to chronic inflammation in SDB, which has been postulated to contribute to pulmonary oedema as well as to the anorexia and cachexia that frequently occurs in patients with advanced HF.34,35 OSA is a known risk factor for the development of arterial hypertension, and is associated with an increased incidence of stroke, metabolic syndrome, and coronary heart disease.36–38 The occurrence of SDB complicated by recurrent episodes of oxygen desaturation has also been associated with an almost twofold increase in the risk of sudden death, independent of known risk factors.39 Both OSA and CSA have now been shown in prospective longitudinal studies to be independent predictors of incident HF.40,41 In fact, in patients with HFpEF, obstructive apnea events have been shown to cause increases in pulmonary capillary pressure, and OSA has been associated with an increase in LV mass and the development of diastolic dysfunction.10 These findings suggest that SDB is not simply a marker of HF, but may be a mediating factor contributing to the onset and progression of clinically overt HF. These negative cardiovascular consequences highlight the critical need for safe and effective treatment of SDB in HF patients.

Treatment Obstructive Sleep Apnea Continuous positive airway pressure (CPAP) ventilation is the most widely used treatment option for OSA. In multiple studies, this therapy has been shown to produce several cardiovascular benefits, including reduction in blood pressure, risk of stroke/transient ischaemic attack and arrhythmias.5,42,43 Several studies have been performed in patients

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Co-Morbidities Figure 1: Effect of Continuous Positive Airway Pressure Versus Control on Left Ventricular Ejection Fraction in Twenty Four Patients with Obstructive Sleep Apnea p=0.009

60 50 40 30 20 10 0 Base line 1 month Control group

p<0.001 Left ventricular ejection fraction (%)

Left ventricular ejection fraction (%)

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Base line 1 month Group treated with continuous positive airway pressure

A statistically-significant increase in ejection fraction is noted with CPAP treatment as compared to control (p=0.009) (absolute increase in EF of 8.8 ± 1.6 %, relative increase in EF of 35 % [p<0.001]). CPAP = continuous positive airway pressure; EF = ejection fraction; NS = not significant. Source: Kaneko, et al., 2003, published with permission.44

with HF and OSA. Among 24 patients with left ventricular dysfunction and OSA, compared to controls, CPAP therapy markedly reduced AHI, systolic blood pressure, and average heart rate. Furthermore, compared to no treatment, the use of CPAP reduced left ventricular end systolic dimension (54.5 ± 1.8 to 51.7 ± 1.2 mm [p=0.009]), and improved the left ventricular ejection fraction (25.0 ± 2.8 % to 33.8 ± 2.4 % [p<0.001])44 (see Figure 1). Reductions in overnight urinary norepinephrine excretion and improvements in quality of life also occurred with the treatment of OSA with CPAP in HF patients.45 In the largest retrospective cohort study of a US Medicare database of 30,719 patients with newly diagnosed HF between 2003 and 2005, treatment of SDB was associated with decreased readmission, healthcare cost and mortality among subjects who were diagnosed and treated, with an improved 2-year survival rate in those who were treated compared to those who were not (hazard ratio: 0.49, 95 % CI: 0.29–0.84, p=0.009).46 Hypoglossal nerve stimulation for the treatment of OSA has also been shown to reduce AHI and oxygen desaturation events, and now has reported long-term sustained benefits in patient reported outcomes.47,48 This technology that consists of an implantable pulse generator with sensing and stimulation leads to prevent airway collapse during sleep has been approved by the FDA for commercial use in the USA for patients with moderate to severe OSA who have failed or are unable to tolerate CPAP therapies.

Central Sleep Apnea In HF patients with CSA, optimisation of medical therapy and effective decongestion are the first and foremost steps in the treatment of this sleep disorder. Treatment with cardiac resynchronisation therapy has been shown to effectively reduce AHI events in patients with CSA.49 Despite strict adherence to guideline-directed medical therapy, CSA remains a significant comorbidity in patients with HF. In contrast to OSA, where the safety and effectiveness of CPAP are no longer questioned, the role of this therapy in patients with CSA remains the subject of controversy. Early trials of CPAP for CSA and HF patients showed some positive effects, including a reduction in central apnea/hypopnea events, ventricular ectopic beats, and nocturnal urinary and daytime plasma norepinephrine levels, and a trend towards a reduction in mortality and need for cardiac transplantation.50–52 In the Canadian Positive Airway Pressure Trial for

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Patients with Congestive HF and Central Sleep Apnea (CANPAP) trial in 258 optimally treated HF patients with an LVEF <40 % and AHI >15 events/h, compared to control, CPAP did not prolong transplant-free survival, despite reduction of AHI from 40 to 19 events/h, improvement of nocturnal oxygenation, exercise tolerance and decrease in plasma norepinephrine levels after 3 months of CPAP therapy.53 In fact, the trial was stopped early for futility given a diverging trend towards increased mortality early on in the CPAP group, and yet the overall event rates of death or transplant did not differ after 18 months. Notably, in CANPAP the average duration of CPAP was 3.6 h/night and CSA was not adequately suppressed in 43 % of the study subjects.1 However, a post-hoc analysis showed that, compared to inadequately treated patients, those in whom CPAP decreased AHI below 15 events/h had significant prolongation of transplant-free survival.54 These findings suggest that mask-based therapeutic strategies may be limited by poor patient compliance. Adaptive pressure support servo-ventilation (ASV), an alternative noninvasive ventilatory support modality, was developed to make positive airway pressure more tolerable to patients with SDB. ASV delivers a baseline continuous positive airway pressure similar to CPAP, and yet can also detect episodes of central apneas and deliver several breaths at the tidal volume and respiratory rate previously determined to match the patient’s minute ventilation during stable breathing. The goal of ASV therapy is to prevent the increase in PaCO2 during apnea and the hyperventilation that follows, thereby breaking the abnormal periodic breathing cycle. Several small preliminary studies have suggested that ASV is better tolerated than CPAP, and may be more effective than CPAP in the treatment of CSA in HF.55 However, in a large randomised trial, the Treatment of Predominant Central Sleep Apnea by Adaptive Servo Ventilation in Patients With HF (SERVE-HF), ASV did not reduce the primary combined endpoint of all cause death, cardiac transplantation or ventricular assist device implantation, sudden cardiac arrest or HF hospitalisation. In fact, as compared to the controls, ASV was associated with an increase in cardiovascular mortality (hazard ratio for cardiovascular death: 1.34; 95 % CI: 1.09–1.65; p=0.006)56 (see Figure 2). Several postulates of the disconcerting results of SERVE-HF have been presented, including the possibility that positive airway pressure may further reduce cardiac output in a population that may be vulnerable with limited cardiac reserve, or that central sleep apnea could be a beneficial compensatory mechanism in patients with advanced HF. This latter theory appears inherently flawed, as the intermittent hypoxia and norepinephrine release associated with central sleep apnea events make it unlikely that this sleep disorder confers any long-term benefits to patients with HF. The first-generation ASV device used in SERVE-HF, no longer manufactured by the sponsor, had limited technology with fixed settings that may have applied pressures that were too low for some patients and excessive for others, leading to adverse cardiovascular consequences. Newer generation devices that incorporate novel algorithms with dynamic settings may allow for the prevention of excessive positive airway pressure and its potential detrimental cardiac effects.57 Ultimately, further trials will be required to mediate these discrepant results, and studies with newer generation ASV devices in HFrEF and SDB are ongoing.58

Phrenic Nerve Stimulation Transvenous unilateral neurostimulation is a unique physiological approach to the treatment of central sleep apnea. The remedē® System

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the 64 % of the study subjects who had underlying HF revealed that, compared to HF controls, the HF treatment group included a greater percentage of patients who had a reduction in AHI>50 % at 6 months (63 % versus 4 %, p<0.0001). In the HF group, tolerability and safety were similar to those of the overall population.59 The results of this trial indicate that transvenous neurostimulation produces significant improvements in reducing the severity of central sleep apnea, as measured by several pre-specified sleep indices obtained during polysomnography and scored by masked investigators in a core laboratory. Improvements were observed with use of the device in the arousal index, REM sleep, PGA scores, and ESS quality of life measures at 6 months of follow up. The therapy was well tolerated, with only two patients who were unable to adjust to therapy, and first implant success was high. Procedural complications, including lead dislodgements, were comparable with other implantable devices using transvenous lead technology. Results from the SERVE-HF trial showed an unexpected increase in the risk of cardiovascular mortality (p=0.006), despite a significant reduction in AHI from baseline to 12 months of follow up. However, it might not be appropriate or valid to assume that the effects

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Significantly more patients in the treatment group (51 %) had an AHI reduction from baseline of 50 % or greater at 6 months than had those in the control group (11 %). The difference between groups was 41 % (95 % CI 25–54, p<0.0001) (see Figure 3). One hundred and thirtyeight (91 %) of 151 patients had no serious-related adverse events at 12 months. Seven (9 %) cases of related-serious adverse events occurred in the control group and six (8 %) cases were reported in the treatment group. Seven patients died (unrelated to implant, system, or therapy): four deaths (two in the treatment group and two in the control group) during the 6-month randomisation period and three deaths between 6 months and 12 months. Thirty-seven percent of the treatment group patients reported non-serious therapy-related discomfort that was resolved with simple system reprogramming in all but one patient. All prespecified hierarchically tested secondary sleep and quality of life measure endpoints were improved in the treatment group compared to control.59 Exploratory evaluation of

Figure 2: Cumulative Incidence Curves for the Primary End Point, Death from Any Cause, and Cardiovascular Death in the SERVE-HF Trial

Hazard ratio, 1.28 (95 % CI, 1.06–1.55) p=0.01

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(Respicardia Inc) aims to stimulate a nerve to cause diaphragmatic movement producing changes in carbon dioxide concentrations and tidal volumes similar to normal breathing. Unilateral transvenous neurostimulation does not produce a ‘hiccup-type’ diaphragmatic response, such as that noted occasionally with direct stimulation of the diaphragm with cardiac resynchronisation therapy. Instead, the device provides neurostimulation pulses configured to smoothly engage the diaphragm, like normal breathing. The neurostimulator is placed in either the left or right pectoral region; the stimulation lead is placed in either the left pericardiophrenic or right brachiocephalic vein to stimulate the phrenic nerve. The sensing lead is placed in a thoracic vein, such as the azygos vein, to sense respiration by thoracic impedance. The system aims to automatically stimulate the phrenic nerve during the scheduled time at night when the patient is asleep and in a reclining position, which is detected by a position and motion sensor present in the device. Transvenous neurostimulation improved apnea indices, quality of life, and had an acceptable safety profile in pilot studies. In the remedē System Pivotal Trial, 151 eligible patients underwent device implantation and were then randomly assigned to initiate neurostimulation either 1 month later (treatment, n=73), or after the 6-month primary efficacy endpoint evaluation (control, n=68).

Hazard ratio, 1.34 (95 % CI, 1.09–1.65) p=0.006

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ASV treated patients had higher incidence of death from any cause and cardiovascular death. ASV = Adaptive servo ventilation. Source: Cowie, et al., 2015, published with permission.56

of ASV on the outcomes of patients with advanced HF also apply to the effects of neurostimulation in a different population. Although 96 (64 %) patients in this study had previous HF, only 59 (39 %) had HF severity and left ventricular dysfunction similar

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Co-Morbidities randomised controlled trials are needed to determine the role of oxygen in treating sleep apnea in HFrEF.

A Treatment

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Figure 3: Percentage Change in Apnea-hypopnea Index at 6 Months’ Follow up Compared with Baseline

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Patients who do not tolerate positive airway pressure therapy during sleep may consider treatment with a respiratory stimulant, such as acetazolamide or theophylline. Acetazolamide is a carbonic anhydrase inhibitor and a weak diuretic. It causes mild metabolic acidosis, which stimulates respiration and is shown to decrease the frequency of central apnea episodes.20 Theophylline, a methylxanthine drug that acts as a nonselective adenosine receptor antagonist, at therapeutic plasma concentration levels (11 μg/mL, range 7–15 μg/mL), has shown to reduce AHI events in patients with HF and CSA.64 Ultimately, there are no long-term data available on either of these medications, as well as narrow therapeutic ranges, and these therapies can have harmful side-effects that need to be monitored closely.

Knowledge Gaps

–50 –100 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 Patients (n=73)

More patients in the treatment group had a decrease in AHI as opposed to the control group. AHI = apnea-hypopnea index. Source: Costanzo, et al., 2016, published with permission.59

to the population in the SERVE-HF trial. Exploratory post-hoc analyses of the pivotal trial suggested that indeed the effects of neurostimulation in the subgroup of patients with HF were consistent with the findings in the overall trial population. 59 Importantly though, the mechanism of action of neurostimulation remains distinctly different from that of ASV. Specifically, while ASV delivers positive airway pressure, the diaphragmatic contraction triggered by neurostimulation generates negative intrathoracic pressure. In fact, unilateral transvenous stimulation has been the only therapy to show a reduction in sleep-related arousals, which are a manifestation of acute neurohormonal activation. Neurostimulation was associated with an improvement in quality of life measures, whereas ASV was not, suggesting clinical benefits beyond just that of improved sleep variables. Additional investigation will be warranted to study the hemodynamic effects of negative intrathoracic pressure in patients with HF, as well as trials focusing on cardiovascular outcomes to provide further supportive data.

Supplemental Oxygen Observational studies in patients with HFrEF have shown that nocturnal nasal oxygen improves CSA, with data suggesting improvement in exercise capacity; decreases in nocturnal urinary norepinephrine excretion; improvement in ventricular arrhythmias, and quality of life.60 Nocturnal hypoxemia is known to be an independent predictor of allcause mortality in HFrEF patients, and an O2 sat <78 % during SDB is a strong predictor of sudden cardiac death.61,62 Supplemental oxygen is indicated for patients with CSA who have confirmed hypoxemia during sleep. It can be used along with positive airway pressure therapy, or may also be considered for patients who do not tolerate or fail positive airway pressure therapy. However, oxygen therapy remains contraindicated in patients without hypoxemia, as there can be theoretical detrimental effects to this treatment strategy, such as lengthening apnea duration and accelerating CO2 retention.63 Overall studies have been small with short-term duration of follow up. Further

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Randomised control trials are needed to provide further assessment of the role of any sleep apnea intervention on cardiovascular associated morbidity and mortality. Low adherence to CPAP therapy has been a major limitation in clinical trials, and continues to be a dilemma with clinical application of mask-based therapies. Treatment of CSA with ASV could be harmful, and further trials are needed to determine whether newer generation devices may be of benefit. Unilateral transvenous neurostimulation shows promise for the treatment of CSA, but will need further validation for long-term cardiovascular outcome benefit.

Conclusion Our understanding of the causes and subsequent pathological consequences of SDB in HF has been greatly expanded over the past few decades. SDB is now recognised as an important, independent risk factor for the development of incident HF, worsening HF status, and reduced survival in patients with HF. Unfortunately, SDB is often under-recognised, and not tested for routinely; and yet, we know that treatment can improve outcomes in these patients. CPAP therapy for OSA in HF patients can improve AHI, improve blood pressure, and even improve left ventricular ejection fraction. Survival is also improved in observational studies of CPAP treatment in HF patients. Vigilance in diagnosis, testing, and treatment is paramount in this population. Therapies for CSA remain more complex. Because the pathological consequences of CSA are known to worsen HF, treatment strategies remain vital to management. Optimising medical therapy as first approach remains of utmost importance, as research has shown that often when HF is clinically improved, CSA often improves as well.65,66 In cases where CSA persists despite aggressive treatment of HF, further therapeutic interventions should be considered. The CANPAP trial did not reveal benefits of treatment with CPAP therapy for CSA. Results of the SERVE-HF trial suggest that treatment of CSA with ASV could be harmful. However, limitations of this study with utilisation of older generation devices and limited treatment algorithms still pose several questions.57 Transvenous unilateral neurostimulation is now recognised as another treatment option of CSA known to reduce AHI, but does not carry the negative consequence of increasing intrathoracic pressure. Future randomised control trials with unilateral transvenous neurostimulation for HF should be powered to determine cardiovascular outcomes. n

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The Future Role of Cardio-oncologists Radek Pudil 1st Department of Medicine – Cardioangiology, Charles University Prague, Medical Faculty and University Hospital Hradec Králové, Hradec Králové, Czech Republic

Abstract Cardiovascular (CV) disease and cancer remain the two most common causes of mortality in developed countries; however, progress in the treatment of malignant diseases significantly improved survival of oncological patients. Similarly, there is an increased number of the patients with malignancy who have a history of CV disease or an increased CV risk. Rates of CV problems from cancer-related therapeutics are high, and cardiotoxicity is the second most common cause of morbidity and mortality in cancer survivors. Therefore, there is a need for the development of an efficient programme to manage the problem of cardiotoxicity with the aim to decrease morbidity and mortality in patients and to improve their quality of life. For this purpose, cardio-oncological clinics should be an essential part of the strategy.

Keywords Cardiotoxicity, cardio-oncology clinic, detection, management, risk factors Disclosure: The author has no conflict of interest. Received: 17 September 2017 Accepted: 19 October 2017 Citation: Cardiac Failure Review 2017;3(2):140–2. DOI: 10.15420/cfr.2017:16:1 Correspondence: Prof Dr Radek Pudil, 1st Department of Medicine – Cardioangiology, Charles University Prague, Medical Faculty and University Hospital Hradec Králové, Sokolská 581, Hradec Králové 500 05, Czech Republic. E: pudilr@lfhk.cuni.cz

Cardiovascular (CV) disease and cancer remain the two most common causes of mortality in developed countries. According to recent data from the American Cancer Society, the lifetime probability of being diagnosed with an invasive cancer is higher for men (43 %) than for women (38 %).1 Within the last few decades the progress in the treatment of malignant diseases significantly improved survival of oncological patients. The decreased mortality is driven by both improved diagnostic and therapeutic modalities; however, the improved survival of oncological patients can be limited by adverse effects associated with intensive antitumorous treatment. In particular, cardiotoxicity may compromise the effectiveness of the anticancer therapy, independently of the oncological prognosis, and can negatively affect survival and quality of life of oncological patients. This includes the development of newly diagnosed CV problems, or the exacerbation of previously identified CV disease. Rates of CV problems from cancer-related therapeutics have been reported to be in excess of 30 %, and cardiotoxicity is the second most common cause of morbidity and mortality in cancer survivors.2,3 The number of patients at risk of problems are high. According to the latest data, on 1 January 2016 more than 15.5 million Americans with a history of cancer were alive, and this number is projected to reach more than 20 million by 1 January 2026. Furthermore, 56 % of survivors were diagnosed within the past 10 years, and almost half (47 %) were aged 70 years or older.4 Similarly, there is an increasing number of patients with malignancy who have a history of CV disease or an increased CV risk. Therefore, the CV problems in oncological patients are not only medical but also social and economic problems, and new strategies to solve this topic are needed.5 This brief review focuses on some issues that need to be addressed in the near future.

Wide Spectrum of Cardiovascular Complications of Cancer Treatment The first clinical manifestation of adverse effects from anticancer drugs on the CV system was depression of the left ventricle function leading to heart failure in patients treated with anthracyclines. Therefore, the term

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cardiotoxicity of cancer therapy was established for the development of heart failure as a result of anticancer treatment. Until now, myocardial dysfunction and heart failure are the most concerning CV complications of cancer therapy due to their role in an increase in morbidity and mortality in cancer patients. Myocardial dysfunction is associated with a broad spectrum of anticancer treatment (anthracyclines, alkylating agents, tyrosine kinase inhibitors, antimetabolites, etc.) with the incidence ranging from 2–40 %.6 Diagnosis of myocardial dysfunction was based on a decrease of left ventricular ejection fraction (LVEF) of at least 10 % to a level below normal from methods such as 2D and 3D echocardiography, cardiac MRI and multigated radionuclide angiography.6 Some studies revealed a potential beneficial role of cardiomarkers (troponins and natriuretic peptides) in the detection of early manifestation of cardiotoxicity.7,8 Serial evaluation of symptoms, ECG and echocardiography focused on left ventricle function were seen to be sufficient to cover all cardiotoxicity problems; however, further observations revealed a much wider spectrum of CV complications of cancer therapy.6 They are divided into nine categories according to their pathophysiology and clinical manifestation: myocardial dysfunction and heart failure; coronary artery disease; valvular disease; arrhythmias, especially those induced by QT-prolonging drugs; arterial hypertension; thromboembolic disease; peripheral vascular disease and stroke; pulmonary hypertension; and pericardial complications.9 Detection of CV complications from the cancer treatment requires the use of a broad spectrum of diagnostic techniques, such as ECG, blood pressure monitoring, CV imaging methods, biomarker testing, coronary arteriography, cardiac catheterization, etc. Therefore, there is a need for collaboration from a broad spectrum of specialists covering not only oncology, but all fields of cardiology.

Basic Concept for the Management of Patients Treated with Potentially Cardiotoxic Drugs It has been shown that early detection and adequate treatment of CV complications can improve survival and the quality of life of oncological

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The Future Role of Cardio-oncologists patients. The main part of the general strategy to minimise the CV risks of anticancer treatment is baseline risk assessment with the aim to identify patients who are at higher risk of CV complications. 6 There are four factors for CV risk: current myocardial disease (heart failure including asymptomatic left ventricular dysfunction; evidence of coronary artery disease; moderate or severe valvular heart disease; arterial hypertension with impaired LVF; hypertrophic, dilated or restrictive cardiomyopathy; cardiac sarcoidosis; and arrhythmias [AF and ventricular arrhythmias]); previous cardiotoxic cancer treatment (prior anthracycline medication, and chest and mediastinal irradiation); demographic risk factors (age, family history of premature CV disease, arterial hypertension, diabetes mellitus and hypercholesterolemia); and life-style risk factors (high alcohol intake, obesity, sedentary lifestyle and smoking). In patients treated with potentially cardiotoxic therapy, baseline risk assessment and LVEF should be determined before and periodically during the treatment using the same method.10,11 The regimen of the diagnostic tools for the detection of cardiotoxicity consists of ECG (resting tachycardia, ST-T changes, conduction disturbances and QT interval prolongation); echocardiography (2D or 3D LVEF assessment, global longitudinal strain, pericardial effusion, etc.); nuclear cardiac imaging (LVF assessment using multigated radionuclide angiography); cardiac MRI (LVF and structural changes of the myocardium); and biomarker assessment (challenging data were published on troponins and natriuretic peptides, which seems to be helpful to identify the patients at higher risk or those with early manifestation of cardiotoxicity).

appropriate measures to solve complications.2 This new situation has led to the development of the new cardiology subspecialty in cardiooncology, which is a multidisciplinary field with the aim to prevent and treat CV complications from cancer therapy. The cardio-oncology clinic must be able to cover all needs required to fulfil its aims, that is, they must be able to provide all examinations of the CV system (ECG, echocardiography and biomarkers), and have access to patients or provide other examinations (nuclear cardiology examinations, cardiac magnetic resonance, cardiac catheterization, etc.).

According to the current recommendations, precise timing and frequency of imaging and/or biomarkers sampling depends on the specific cancer treatment, total cumulative dose, delivery protocol and the patient’s baseline CV risk profile.6 In asymptomatic patients

The cardio-oncology nurse coordinator can be a useful member of the cardio-oncology team. The aim of the co-ordinator should be patient care co-ordination, triaging urgent CV issues and patient education. Education and research are also important functions of the successful centre. Education should be focused on staff education (oncology meetings, tumour boards, etc.), trainee education (conferences and workshops for residents and fellows) and community education (to increase public awareness about cardio-oncology problems), and research should be one of the essential functions of the academic centre.

with significantly decreased ejection fraction, the treatment with angiotensin-converting enzyme inhibitors in combination with betablockers should be considered as a prevention of further decrease of LVEF. These drugs are indicated for those with symptomatic left ventricular dysfunction; thus, the timing of the cardiotoxicity surveillance should be personalised to the patient with the aim to avoid cardiotoxicity, to detect early phases of the cardiotoxicity and to start appropriate measures (type and schedule of anticancer regimen, and treatment of heart failure). The optimal surveillance strategy to minimise the risk of cardiotoxicity has gaps in evidence and this strategy is frequently based on expert opinion; therefore, further studies are needed.

Cardio-oncology Team and Cardio-oncology Subspeciality Historically, oncologists were the first medical professionals to observe CV complications from cancer treatment. These patients were referred to cardiologist for further examination and CV treatment. This approach was shown to be ineffective in appropriate treatment of the cardiac problems in cancer patients, but mainly it was impossible to detect early phases of cardiotoxicity and avoid complications. Nowadays, the complexity of the cancer treatment requires tight co-operation between oncologist and cardiologist in the identification of at-risk patients, planning of treatment, and patient surveillance with the aim to prevent cardiotoxicity, detect early signs of cardiotoxicity and to take

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Cardio-oncology Clinic Cardio-oncology clinics are currently expanding in both academic centres and community practices. Some key components for the effective work of the cardio-oncology centre are:2,12–15 • High level of programme leadership (collaborative work of cardiologist and oncologist on all aspects of programme development). • Appropriate location (within cancer centre, in close proximity to those specialities that refer large numbers of patients, and adequate space to allow for future expansion and growth). • Experienced staff (patient evaluation must be provided by individuals who understand the complexities of cancer patients). • CV testing (onsite echocardiography with access to advanced echo technologies, and access to additional imaging modalities including cardiac MRI and coronary arteriography).

Conclusion Effective cardiotoxicity management needs to be comprehensive. It requires not only the building of a network of cardio-oncology clinics near oncology centres but also a tight co-operation with other primary healthcare clinics and providers who care for patients after the completion of oncology treatment. This requires close co-operation between medical professionals (oncologists and cardiologists) and education not only within the medical community but also for the general public. This must all be supported by adequate financial resources. Therefore, it is necessary to create standards for the effective functioning of such a system, aiming not only at minimising the mortality and mobility of this group of patients, but primarily for improving the quality of life, including the return-to-work process. In summary, these are the processes that must be led by medical societies in a discussion with other partners involved in the treatment of oncological patients: healthcare providers, the healthcare industry, health insurers, institutions involved in healthcare and the general public. n

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11. Q i WX, Shen Z, Tang LN, Yao Y. Congestive heart failure risk in cancer patients treated with vascular endothelial growth factor tyrosine kinase inhibitors: a systematic review and meta-analysis of 36 clinical trials. Br J Clin Pharmacol 2014;78:748–62. DOI: 10.1111/bcp.12387; PMID: 24661224 12. Chen CL, Steingart R. Cardiac disease and heart failure in cancer patients: is our training adequate to provide optimal care? Heart Fail Clin 2011;7:357–62. DOI: 10.1016/ j.hfc.2011.03.007; PMID: 21749887 13. Sulpher J, Mathur S, Graham N, et al. Clinical experience of patients referred to a multidisciplinary cardiac oncology clinic: an observational study. J Oncol 2015;2015:671232. DOI: 10.1155/2015/671232; PMID: 26300917 14. Albini A, Pennesi G, Donatelli F, et al. Cardiotoxicity of anticancer drugs: the need for cardio-oncology and cardiooncological prevention. J Natl Cancer Inst 2010;102:14–25. DOI: 10.1093/jnci/djp440; PMID: 20007921 15. Barac A, Murtagh G, Carver JR, et al. Cardiovascular health of patients with cancer and cancer survivors: a roadmap to the next level. J Am Coll Cardiol 2015;65:2739–46. DOI: 10.1016/ j.jacc.2015.04.059; PMID: 26112199

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