Characterization of responder profiles for cardiac resynchronization therapy through unsupervised clustering of clinical and strain data

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

Edité par HAL CCSD ; Elsevier

International audience. 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, on the basis of clinical and echocardiographic preimplantation data, integrating automatic quantification of longitudinal strain signals. Methods - This was a multicenter observational study of 250 patients with chronic heart failure evaluated before CRT device implantation and followed up to 4 years. Clinical, electrocardiographic, and echocardiographic data were collected. Regional longitudinal strain signals were also analyzed with custom-made algorithms in addition to existing approaches, including myocardial work indices. Response was defined as a decrease of ≥15% in LV end-systolic volume. Death and hospitalization for heart failure at 4 years were considered adverse events. Seventy features were analyzed using a clustering approach (k-means clustering). Results - Five clusters were identified, with response rates between 50% in cluster 1 and 92.7% in cluster 5. These five clusters differed mainly by the characteristics of LV mechanics, evaluated using strain integrals. There was a significant difference in event-free survival at 4 years 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 to CRT. These clusters demonstrate the complexity of LV mechanics and prediction of response to CRT. Automatic quantitative analysis of longitudinal strain curves appears to be a promising tool to improve the understanding of LV mechanics, patient characterization, and selection for CRT.

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