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).

Predictors of Subcutaneous Implantable Cardioverter-Defibrillator Shocks and Prognostic Impact in Patients With Structural Heart Disease.

BACKGROUND: In this study we aimed to assess long-term outcomes in subcutaneous implantable cardioverter-defibrillator (S-ICD) recipients with structural heart disease by focussing especially on shock incidence, predictors, and associated prognoses. METHODS: In this multicenter registry‒based study, we retrospectively included all patients who underwent S-ICD implantation at 3 tertiary centers. The prognostic impact of S-ICD shock was assessed with a composite outcome that included all-cause death and hospitalisation for heart failure. RESULTS: A total of 351 patients with underlying cardiomyopathy were included in the investigation. Using multivariable Fine and Gray regression models, secondary prevention, left ventricular ejection fraction (LVEF), conditional shock threshold, and QRS duration appeared to be independent predictors of appropriate S-ICD shock occurrence. In the multivariate Cox regression model adjusted for age, baseline LVEF, underlying cardiomyopathy subtype, New York Heart Association class, and appropriate shocks were significantly associated with increased composite prognostic outcome risk (hazard ratio [HR], 2.61; 95% confidence interval [CI], 1.21-5.65; P = 0.014), whereas inappropriate shocks were not (HR, 1.35; 95% CI, 0.75-4.48; P = 0.18). The analysis of each component of the composite prognostic outcome highlighted that the occurrence of appropriate shocks was associated with an increased risk of hospitalisation for heart failure (HR, 3.10; 95% CI, 1.26-7.58; P = 0.013) and a trend for mortality (HR, 2.19; 95% CI, 0.78-6.16; P = 0.14). CONCLUSIONS: Appropriate S-ICD shocks were associated with a 3-fold increase in acute heart failure admission, whereas inappropriate shocks were not. Conditional shock threshold programming is an independent predictor of S-ICD shock, and its prognostic impact should be investigated further in patients with structural heart disease.