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Background: Structural changes and myocardial fibrosis quantification by cardiac imaging have become increasingly important to predict cardiovascular events in patients with mitral valve prolapse (MVP). In this setting, it is likely that an unsupervised approach using machine learning may improve their risk assessment., Objectives: This study used machine learning to improve the risk assessment of patients with MVP by identifying echocardiographic phenotypes and their respective association with myocardial fibrosis and prognosis., Methods: Clusters were constructed using echocardiographic variables in a bicentric cohort of patients with MVP (n = 429, age 54 ± 15 years) and subsequently investigated for their association with myocardial fibrosis (assessed by cardiac magnetic resonance) and cardiovascular outcomes., Results: Mitral regurgitation (MR) was severe in 195 (45%) patients. Four clusters were identified: cluster 1 comprised no remodeling with mainly mild MR, cluster 2 was a transitional cluster, cluster 3 included significant left ventricular (LV) and left atrial (LA) remodeling with severe MR, and cluster 4 included remodeling with a drop in LV systolic strain. Clusters 3 and 4 featured more myocardial fibrosis than clusters 1 and 2 (P < 0.0001) and were associated with higher rates of cardiovascular events. Cluster analysis significantly improved diagnostic accuracy over conventional analysis. The decision tree identified the severity of MR along with LV systolic strain <21% and indexed LA volume >42 mL/m 2 as the 3 most relevant variables to correctly classify participants into 1 of the echocardiographic profiles., Conclusions: Clustering enabled the identification of 4 clusters with distinct echocardiographic LV and LA remodeling profiles associated with myocardial fibrosis and clinical outcomes. Our findings suggest that a simple algorithm based on only 3 key variables (severity of MR, LV systolic strain, and indexed LA volume) may help risk stratification and decision making in patients with MVP. (Genetic and Phenotypic Characteristics of Mitral Valve Prolapse, NCT03884426; Myocardial Characterization of Arrhythmogenic Mitral Valve Prolapse [MVP STAMP], NCT02879825)., Competing Interests: Funding Support and Author Disclosures This work was supported by Foundation Coeur et Recherche (Dr Le Tourneau, 2013, Paris, France) and French Ministry of Health “PHRC-I 2012” (Dr Le Tourneau, API12/N/019, Paris, France). The STAMP study (Drs Huttin and Selton-Suty) was supported by a grant from the French Ministry of Health (APJ 2015, n°: 2016-A00954-47). Dr Huttin has received honoraria form General electric and Pfizer. Dr Girerd was supported by the French National Research Agency Fighting Heart Failure (ANR-15-RHU-0004), the French PIA project Lorraine Université d’Excellence GEENAGE (ANR-15-IDEX-04-LUE) programs, and the Contrat de Plan Etat Région Lorraine and FEDER IT2MP; and has received honoraria from Lilly, Bayer, Roche Diagnostics, Novartis, AstraZeneca, Boehringer, and Vifor. Dr Le Tourneau was supported by an INSERM Translational Research Grant (2012-2016, Paris, France). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Marfan syndrome is a connective tissue disease that rarely presents first with peripheral aortic aneurysms. We highlight the case of a young man with Marfan syndrome presenting with an abdominal aortic aneurysm due to a heterozygous fibrillin-1 gene mutation., (Copyright © 2021 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)