Improving Risk Stratification in Hypertrophic Cardiomyopathy: A Novel Score Combining Echocardiography, Clinical, and Medication Data

arXiv cs.LG / 3/30/2026

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Key Points

The study addresses the need for more accurate risk stratification in hypertrophic cardiomyopathy (HCM) to guide ICD therapy and follow-up decisions.
It introduces an explainable machine-learning risk score that predicts a 5-year composite cardiovascular outcome using routinely collected echocardiography, clinical, and medication data from EHRs.
The model uses a Random Forest ensemble trained on SHARE registry data (N=1,201) and externally validated on an independent cohort from Rennes Hospital (N=382).
Results show substantially better discriminative performance than the ESC score, with the ML model achieving an internal AUC of ~0.85 versus ~0.56 for ESC and improved external risk separation in survival analyses.
The proposed score is reported to be stable over time in event-free patients, supporting longitudinal, personalized risk monitoring in clinical practice.

Abstract

Hypertrophic cardiomyopathy (HCM) requires accurate risk stratification to inform decisions regarding ICD therapy and follow-up management. Current established models, such as the European Society of Cardiology (ESC) score, exhibit moderate discriminative performance. This study develops a robust, explainable machine learning (ML) risk score leveraging routinely collected echocardiographic, clinical, and medication data, typically contained within Electronic Health Records (EHRs), to predict a 5-year composite cardiovascular outcome in HCM patients. The model was trained and internally validated using a large cohort (N=1,201) from the SHARE registry (Florence Hospital) and externally validated on an independent cohort (N=382) from Rennes Hospital. The final Random Forest ensemble model achieved a high internal Area Under the Curve (AUC) of 0.85 +- 0.02, significantly outperforming the ESC score (0.56 +- 0.03). Critically, survival curve analysis on the external validation set showed superior risk separation for the ML score (Log-rank p = 8.62 x 10^(-4) compared to the ESC score (p = 0.0559). Furthermore, longitudinal analyses demonstrate that the proposed risk score remains stable over time in event-free patients. The model high interpretability and its capacity for longitudinal risk monitoring represent promising tools for the personalized clinical management of HCM.