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 ± 17 years) 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 57 months, 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 (> 24 cm2), mechanical dispersion (> 49 ms), posterior wall thickness (> 1.8 cm), and TAPSE (27 mm) 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).

The author has not supplied her declaration of competing interest.

Copyright © 2023 Published by Elsevier Masson SAS