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Phenotyping of Mitral Valve Prolapse Without Severe Mitral Regurgitation Using Electrocardiographic and Echocardiographic Data

Year: 2025

Type: article

Abstract: BACKGROUND: Arrhythmic risk stratification in mitral valve prolapse (MVP) without significant mitral regurgitation (MR) remains elusive. Unsupervised machine learning may reveal phenotypic variation among arrhythmic MVP without severe MR. OBJECTIVES: In this study, the authors hypothesized that hierarchical clustering of echocardiographic and 12-lead electrocardiographic (ECG) parameters alone could identify MVP phenotypes without severe MR associated with sustained ventricular arrhythmia and excess mortality. METHODS: The authors identified 343 consecutive MVPs (58 ± 16 years; 51% female) with ≤moderate MR and comprehensive echocardiographic, 12-lead and ambulatory ECG data. They used hierarchical clustering analysis to identify distinctive MVP phenotypes and investigated their association with: 1) arrhythmic events (sudden cardiac arrest, ventricular fibrillation/tachycardia, or frequent ventricular ectopy); and 2) overall mortality (mean follow-up: 5.4 ± 2.7 years). RESULTS: Three clusters were identified: Cluster 1 (83% of MVP cases), Cluster 2 (9%), and Cluster 3 (8%). Despite mostly trace/mild MR, Cluster 3 exhibited more abnormal parameters of left atrial (LA) and left ventricular structure and function compared with Clusters 1 and 2 (all P < 0.001). Top clustering features included ECG intervals, LA systolic strain, and LA function index. Arrhythmic presentations (n = 77) were identified in 19%, 38%, and 43% of Clusters 1, 2, and 3 (P < 0.001), respectively. Compared with Cluster 1, Clusters 2 (HR: 5.01; P < 0.001) and 3 (HR: 5.85; P < 0.001) had significantly increased mortality risk. CONCLUSIONS: Hierarchical clustering based on standard ECG and echocardiographic data alone identifies 3 MVP clusters with distinct arrhythmic profiles and excess mortality, highlighting LA function as a novel risk parameter in MVP without significant MR.

Source: JACC. Clinical electrophysiology

DOI: 10.1016/j.jacep.2025.06.001