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In Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography

BACKGROUND : The mechanisms of improvement of left ventricular (LV) function with cardiac resynchronization therapy (CRT) are not yet elucidated. The aim of this study was to characterize CRT responder profiles through clustering analysis, based on clinical and echocardiographic pre-implantation data, integrating automatic quantification of longitudinal strain signals.

METHODS : This is a multicenter observational study of 250 patients with chronic heart failure evaluated before CRT device implantation and followed up to four-years. Clinical, electrocardiographic and echocardiographic data were collected. Regional longitudinal strain signals were also analyzed with custom-made algorithms in addition to the existing approaches including the myocardial work indices. The response was defined as a decrease ≥15% in LV end-systolic volume. Death and hospitalization for heart failure at 4-years defined the adverse event rate. 70 features were analyzed using a clustering approach (k-means).

RESULTS : 5 clusters were identified, with response rates between 50% in cluster 1 and 92.7% in cluster 5. These 5 clusters differed mainly by the characteristics of LV mechanics, evaluated by strain integrals. There was a significant difference in event-free survival at 4-year between cluster 1 and the other clusters. The quantitative analysis of strain curves, especially in the lateral wall was more discriminative than apical rocking, septal flash or myocardial work in most phenogroups.

CONCLUSIONS : Five clusters are described, defining groups of below-average to excellent responders. They demonstrate the complexity of LV mechanics and prediction of response to CRT. Automatic, quantitative analysis of longitudinal strain curves appears as a promising tool to improve the understanding of LV mechanics, patient characterization and selection for CRT.

Gallard Alban, Bidaut Auriane, Hubert Arnaud, Sade Elif, Marechaux Sylvestre, Sitges Martha, Separovic-Hanzevacki Jadranka, Rolle Virginie LE, Galli Elena, Hernandez Alfredo, Donal Erwan


Cardiac resynchronization therapy, echocardiography, machine learning, mechanical dyssynchrony, remodeling, strain imaging