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Feasibility, reproducibility, and clinical implications of the novel fully automated assessment for global longitudinal strain
REFERENCE Kawakami H, Wright L, Nolan M, Potter E, Yang H, Marwick T. J Am Soc Echocardiogr 2021; 34:136-45
REVIEWED BY Chrissy Thomson ASA SIG: Cardiac
WHY THE STUDY WAS PERFORMED:
The aim of this study was to compare the feasibility, reproducibility and predictive value of fully automated global longitudinal strain (GLS) analysis with manual and semiautomated assessment of GLS.
HOW THE STUDY WAS PERFORMED:
In this retrospective study, GLS analysis was performed on echocardiograms from 561 asymptomatic patients (≥65 years) with heart failure risk factors. All patients were required to have image quality sufficient for GLS analysis and available data on follow-up outcomes of cardiac events (new heart failure and cardiac death). Left ventricular GLS was performed on apical 4, apical 2 and apical long-axis images and was calculated using three methods – fully automated, semi-automated (whereby the automated analysis was adjusted by an experienced investigator) and manual analysis. In addition, a subset of 50 randomly selected patients was evaluated for calculation time, and inter- and intra-observer variability.
WHAT THE STUDY FOUND:
The fully automated GLS analysis was reviewed by an experienced investigator and found to be feasible in 60.6% of patients. Of note, the apical segments in all views, the mid anterior and the basal anteroseptum required frequent manual correction (40% of cases). Figure 1 demonstrates the quality of automated tracking in each segment.
Figure 1 – adapted from Figure 2: J Am Soc Echocardiogr 2021;34:136-45
The mean value of fully automated GLS (absolute values) was 17.6 ± 3.1% compared with 19.4 ± 2.3% for semi-automated and 18.5% ± 2.6% for manual GLS. Using cut-off values of ≥18% (normal), >16-<18% (borderline) and <16% (abnormal), a considerable number of patients who were classified as borderline or abnormal using fully automated GLS were reclassified as normal when using semi-automated or manual GLS.
Whilst the data demonstrated that fully automated GLS was found to be effective for the detection of normal and abnormal LV function, it was significantly less effective than semiautomated analysis. In addition, semi-automated GLS was found to be most effective at predicting cardiac events in the cohort.
As would be expected, the calculation time for fully automated GLS (0.5 ± 0.1min/patient) was significantly shorter than for semi-automated (2.7 ± 0.6min/patient) and manual assessment (4.5 ± 1.6min/patient). Automated GLS also demonstrated the highest degree of reproducibility compared with the other methods.
RELEVANCE TO CLINICAL PRACTICE:
GLS is increasingly being incorporated into clinical decision making and its use is supported by many guideline documents. The ability to produce accurate, reproducible data is essential. Whilst automated GLS is extremely fast to perform, is feasible and has excellent reproducibility, manual adjustment is still required in a substantial number of cases to improve accuracy. This suggests, at this stage, that fully automated GLS should not be solely relied upon and careful analysis and adjustment of GLS tracking is often required by an experienced operator.
Previous research by Chan et al1 suggests that ‘expert competency’ in performing strain analysis can be achieved in as little as fifty cases across a three month period. Whilst a learning curve does exist, it is not an onerous one, with clear benefits in relation to accuracy and predictive value compared to fully automated analysis. n
REFERENCES
1 Chan J, Shiino K, Obonyo N, Hanna J, Chamberlain R, Small A, et al. Left ventricular global strain analysis by twodimensional speckle-tracking echocardiography: the learning curve. J Am Soc Echocardiogr 2017;30:1081-90
