Replacement Myocardial Fibrosis in Patients With Mitral Valve Prolapse: Relation to Mitral Regurgitation, Ventricular Remodeling, and Arrhythmia.

BACKGROUND: Mitral valve prolapse (MVP) is a frequent disease that can be complicated by mitral regurgitation (MR), heart failure, arterial embolism, rhythm disorders, and death. Left ventricular (LV) replacement myocardial fibrosis, a marker of maladaptive remodeling, has been described in patients with MVP, but the implications of this finding remain scarcely explored. We aimed at assessing the prevalence, pathophysiological and prognostic significance of LV replacement myocardial fibrosis through late gadolinium enhancement (LGE) by cardiac magnetic resonance in patients with MVP. METHODS: Four hundred patients (53±15 years of age, 55% male) with MVP (trace to severe MR by echocardiography) from 2 centers, who underwent a comprehensive echocardiography and LGE cardiac magnetic resonance, were included. Correlates of replacement myocardial fibrosis (LGE+), influence of MR degree, and ventricular arrhythmia were assessed. The primary outcome was a composite of cardiovascular events (cardiac death, heart failure, new-onset atrial fibrillation, arterial embolism, and life-threatening ventricular arrhythmia). RESULTS: Replacement myocardial fibrosis (LGE+) was observed in 110 patients (28%; 91 with myocardial wall including 71 with basal inferolateral wall, 29 with papillary muscle). LGE+ prevalence was 13% in trace-mild MR, 28% in moderate MR, and 37% in severe MR, and was associated with specific features of mitral valve apparatus, more dilated LV and more frequent ventricular arrhythmias (45% versus 26%, P<0.0001). In trace-mild MR, despite the absence of significant volume overload, abnormal LV dilatation was observed in 16% of patients and ventricular arrhythmia in 25%. Correlates of LGE+ in multivariable analysis were LV mass (odds ratio, 1.01 [95% CI, 1.002-1.017], P=0.009) and moderate-severe MR (odds ratio, 2.28 [95% CI, 1.21-4.31], P=0.011). LGE+ was associated with worse 4-year cardiovascular event-free survival (49.6±11.7 in LGE+ versus 73.3±6.5% in LGE-, P<0.0001). In a stepwise multivariable Cox model, MR volume and LGE+ (hazard ratio, 2.6 [1.4-4.9], P=0.002) were associated with poor outcome. CONCLUSIONS: LV replacement myocardial fibrosis is frequent in patients with MVP; is associated with mitral valve apparatus alteration, more dilated LV, MR grade, and ventricular arrhythmia; and is independently associated with cardiovascular events. These findings suggest an MVP-related myocardial disease. Last, cardiac magnetic resonance provides additional information to echocardiography in MVP.

Machine Learning-Based Phenogrouping in MVP Identifies Profiles Associated With Myocardial Fibrosis and Cardiovascular Events.

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/m2 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).