Structural changes and myocardial fibrosis quantification by cardiac imaging have become increasingly important to predict cardiovascular events in hypertrophic cardiomyopathy patients. In this setting, it is likely that a supervised approach, using machine learning, may improve their risk assessment.

We retrospectively included patients with confirmed HCM (n=265, 52±17years) through clinical and echocardiographic. A supervised machine learning prognosis algorithm, based on echocardiographic data, was obtained to predict cardiovascular (CV) outcomes, and subsequently investigated for their association with myocardial fibrosis (n=185) assessed by CMR imaging.

At follow-up at 57months, 13 (4.9%) of patients died and 114 (43%) had been hospitalized for CV events. Patient with CV events had higher indexed LV mass, worse diastolic dysfunction, and more severe LV obstruction. HCM-patients with myocardial fibrosis have more severe LV hypertrophy (OR: 3.1; P=0.003) and longitudinal myocardial deformation (OR: 0.8; P=0.008). Prognosis algorithm established using machine learning identified left atrium area (>24cm²), mechanical dispersion (>49ms), posterior wall thickness (>1.8cm), and TAPSE (27mm) as the four most relevant variables to correctly predict cardiovascular events.

Our findings suggest that a simple algorithm based on four key variables (posterior wall thickness, mechanical dispersion, LA area and TAPSE) may help risk stratification and decision-making in patients with HCM. Using new treatments to target these parameters might improve outcomes in HCM-patients (Fig. 1).