Arrhythmic Mitral Valve Prolapse Phenotype: An Unsupervised Machine Learning Analysis Using a Multicenter Cardiac MRI Registry.

Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant mitral regurgitation or left ventricular (LV) dysfunction undergoing late gadolinium enhancement (LGE) cardiac MRI between October 2007 and June 2020 in 15 European tertiary centers. The study end point was a composite of sustained ventricular tachycardia, (aborted) sudden cardiac death, or unexplained syncope. Unsupervised data-driven hierarchical k-mean algorithm was utilized to identify phenotypic clusters. The association between clusters and the study end point was assessed by Cox proportional hazards model. Results A total of 474 patients (mean age, 47 years ± 16 [SD]; 244 female, 230 male) with two phenotypic clusters were identified. Patients in cluster 2 (199 of 474, 42%) had more severe mitral valve degeneration (ie, bileaflet MVP and leaflet displacement), left and right heart chamber remodeling, and myocardial fibrosis as assessed with LGE cardiac MRI than those in cluster 1. Demographic and clinical features (ie, symptoms, arrhythmias at Holter monitoring) had negligible contribution in differentiating the two clusters. Compared with cluster 1, the risk of developing the study end point over a median follow-up of 39 months was significantly higher in cluster 2 patients (hazard ratio: 3.79 [95% CI: 1.19, 12.12], P = .02) after adjustment for LGE extent. Conclusion Among patients with MVP without significant mitral regurgitation or LV dysfunction, unsupervised machine learning enabled the identification of two phenotypic clusters with distinct arrhythmic outcomes based primarily on cardiac MRI features. These results encourage the use of in-depth imaging-based phenotyping for implementing arrhythmic risk prediction in MVP. Keywords: MR Imaging, Cardiac, Cardiac MRI, Mitral Valve Prolapse, Cluster Analysis, Ventricular Arrhythmia, Sudden Cardiac Death, Unsupervised Machine Learning Supplemental material is available for this article. © RSNA, 2024.

Myocardial Fibrosis at Cardiac MRI Helps Predict Adverse Clinical Outcome in Patients with Mitral Valve Prolapse.

Background Patients with mitral valve prolapse (MVP) may develop adverse outcomes even in the absence of mitral regurgitation or left ventricular (LV) dysfunction. Purpose To investigate the prognostic value of mitral annulus disjunction (MAD) and myocardial fibrosis at late gadolinium enhancement (LGE) cardiac MRI in patients with MVP without moderate-to-severe mitral regurgitation or LV dysfunction. Materials and Methods In this longitudinal retrospective study, 118 144 cardiac MRI studies were evaluated between October 2007 and June 2020 at 15 European tertiary medical centers. Follow-up was from the date of cardiac MRI examination to June 2020; the minimum and maximum follow-up intervals were 6 months and 156 months, respectively. Patients were excluded if at least one of the following conditions was present: cardiomyopathy, LV ejection fraction less than 40%, ischemic heart disease, congenital heart disease, inflammatory heart disease, moderate or worse mitral regurgitation, participation in competitive sport, or electrocardiogram suggestive of channelopathies. In the remainder, cardiac MRI studies were reanalyzed, and patients were included if they were aged 18 years or older, MVP was diagnosed at cardiac MRI, and clinical information and electrocardiogram monitoring were available within 3 months from cardiac MRI examination. The end point was a composite of adverse outcomes: sustained ventricular tachycardia (VT), sudden cardiac death (SCD), or unexplained syncope. Multivariable Cox regression analysis was performed. Results A total of 474 patients (mean age, 47 years ± 16 [SD]; 244 women) were included. Over a median follow-up of 3.3 years, 18 patients (4%) reached the study end point. LGE presence (hazard ratio, 4.2 [95% CI: 1.5, 11.9]; P = .006) and extent (hazard ratio, 1.2 per 1% increase [95% CI: 1.1, 1.4]; P = .006), but not MAD presence (P = .89), were associated with clinical outcome. LGE presence had incremental prognostic value over MVP severity and sustained VT and aborted SCD at baseline (area under the receiver operating characteristic curve, 0.70 vs 0.62; P = .03). Conclusion In contrast to mitral annulus disjunction, myocardial fibrosis determined according to late gadolinium enhancement at cardiac MRI was associated with adverse outcome in patients with mitral valve prolapse without moderate-to-severe mitral regurgitation or left ventricular dysfunction. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Gerber in this issue.