Ultrasound as an “objective adjunct” can improve outcomes of cannulation

Ultrasound is a powerful tool for assessing haemodialysis (HD) access for maturity, but may also be used to guide HD access cannulations and has the potential to “actually change the process of care” according to Vandana Dua Niyyar (Emory University, Atlanta, USA). The president of the American Society of Diagnostic and Interventional Nephrology (ASDIN) has spoken to Renal Interventions about her view of the field of renal ultrasonography.

Niyyar emphasises the value of ultrasound throughout the spectrum of kidney disease, right from the first visit of the patient to the nephrologist, while also noting its wider variety of applications including assessment and cannulation of dialysis access. Cannulation has traditionally been done by experienced dialysis staff, she says, but with the increased turnover in dialysis staff after the COVID-19 pandemic, combined with the subjective nature of cannulation criteria, there is plenty of room for the extra objectivity provided by ultrasound. As an “adjunct to physical examination and experience”, she argues, it can transform the patient experience.

Addressing the changing landscape of fistula creation, Niyyar praises the influence of new technology in the field. While technology is already being used in the creation of fistulas, it is also worth putting it to use for the end user. She references patients with complex anatomy that she doubted would have been successfully cannulated without ultrasound. Niyyar says the technology is able to transform care of patients with arteriovenous fistula (AVF), which she stated had been “stagnant for quite a few years” despite technological advances in AVF creation. Ultrasound and other new technologies offer an opportunity to review how care is provided and a chance to “challenge the status quo”.

“I do not think an ultrasound should replace anything,” Niyyar stipulates, “but the ultrasound in combination with experience and physical examination can change the trajectory of what the patient experiences at the end point”. She repeatedly stresses the importance of the patient experience, in fact, saying that is “ultimately what we are here for”. Ultrasound has its limitations, she conceded. Though the ultrasound she uses is handheld, easy to “carry directly to the patient, and “relatively inexpensive”, she notes that it lacks flow measurements. She does also point out, however, that this is due to be remedied in future versions of the point-of-care ultrasound.

A study recently published in Kidney International Reports (KIR) has found that urinary peptidome (UP) analysis can help predict a patient’s risk of developing kidney failure. It also describes the “potential” for the process to “uncover new pathophysiological chronic kidney disease (CKD) progression pathways”.

THE STUDY AUTHORS, including lead authors Ziad A Massy (Paris West University, Paris, France) and Oriane Lambert (University Versailles-Saint Quentin, Villejeuf, France), as well as corresponding author Julie Klein (Institute of Metabolic and Cardiovascular Disease, Toulouse, France), drew attention first to the existing risk equations for predicting kidney failure, one of which, that devised by Tangri et al, uses urinary albumin-to-creatinine ratio as part of its formula. They argued that previous attempts to assess urinary proteins as biomarkers had demonstrated “inconsistent or still limited findings”. Their study, meanwhile, would compare the performance of a risk equation that also analysed peptides.

In designing the trial, the authors sought to “describe the urinary peptidome diversity” in CKD patients to “better characterise” their patient profile, while also uncovering whether it could “predict their progression to kidney failure.” It was carried out over 40 nephrology clinics across France, examining 1,000 adult CKD patients of stages 3–5 from the CKD-REIN study cohort. Fasting blood and second morning urine samples were taken at baseline, and patients were subsequently followed up annually over five years. The primary outcome was kidney failure, which was defined as beginning dialysis or receiving a pre-emptive transplantation.

Machine learning was used in the analysis of the data which was collected. A “clustering algorithm” was used to “classify patients at baseline,” dividing them into three groups of different CKD severity. Then, 5,000 peptides were sequenced with peptidome analysis and their abundances were compared with rates of kidney failure. Kidney failure prediction models were developed based on the results, with sets of predictors including the sequenced peptides as well as other risk factors.

A 90-peptide sequence was discovered which “achieved excellent discrimination in predicting the risk of progression” to kidney failure. However, it did not improve on the predictive capabilities of the kidney failure risk equation provided by Tangri et al. In their discussion, the study authors suggested that “in moderate to advanced CKD, the information contained in the urine peptidome, analysed with an untargeted holistic approach, is redundant with those captured by age, sex” and other variables. However, they did note that it may in fact be a “good predictor” for CKD itself in patients with type 2 diabetes and normoalbuminuria. Conceding that the study had limitations, the authors said it may have been improved by also examining earlier-stage CKD, rather than only looking at patients with stage 3–5 disease. Nevertheless, they concluded, the urinary peptidome “appears to represent a useful tool for the detection of new pathophysiological pathways” that are involved in progression to kidney failure, even though its purely predictive capabilities do not outstrip existing risk equations.