Machine learning model including left ventricular strain analysis for sudden cardiac death prediction in hypertrophic cardiomyopathy

Introduction

The excess mortality in HCM patients is mainly attributed to the occurrence of SCD. The prediction of ventricular arrhythmias remains challenging and could be improved. This study evaluated the added predictive value of a machine learning-based model combining clinical and conventional imaging parameters with information from left ventricular strain analysis to predict sudden cardiac death (SCD) in patients with hypertrophic cardiomyopathy (HCM).

Method

A total of 434 HCM patients (65% men, mean age 56 years) were retrospectively included from two referral centers (Oslo University Hospital, Rennes University Hospital) and followed longitudinally (mean duration 6 years). Mechanical and temporal parameters were automatically extracted from the left ventricle longitudinal strain (LV-LS) segmental curves of each patient and included in a Ridge Regression model alongside conventional clinical and imaging data. The composite endpoint included sustained ventricular tachycardia, appropriate implantable cardioverter defibrillator therapy, aborted cardiac arrest, or sudden cardiac death (Fig. 1).

Results

Thirty-four patients (7.8%) met the endpoint with an incidence of ventricular arrhythmias of 0.9%/years. From a subset of 18 most discriminating parameters, including 7 derived from LV-LS, and after n = 200 rounds of cross-validation, the final model showed superior predictive performance compared to conventional models with a mean area under the curve (AUC) of 0.83 ± 0.8.

Conclusion

A machine learning model including automatically extracted left ventricular strain-derived parameters was superior in the prediction of sustained ventricular arrhythmias and SCD in patients with HCM compared to existing models. Machine learning model including LV-LS analysis could improve SCD risk stratification in HCM patients (Fig. 1).