A fully automated machine learning assisted pipeline to predict disease progression for early-stage hypertrophic cardiomyopathy from echocardiography

Abstract

Introduction Echocardiography is critical for the diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM). However, its value to predict disease progression in pre-symptomatic HCM remains to be fully explored. This study aims to assess the prognostic value of echocardiography in pre-symptomatic HCM using a fully automated machine learning (ML) pipeline to predict disease progression. Methods Echocardiographic data (B-mode acquisitions of apical 4-chamber sequences) from 260 NYHA I HCM patients, collected retrospectively from two centers, was used. An ML pipeline was built on the discovery cohort (N=212 patients) to predict disease progression, defined as a composite of NYHA class worsening and unplanned cardiovascular-related hospitalizations. A fully automated deep learning segmentation pipeline was used to delineate cardiac chambers in apical 4-chamber views and identify end-diastolic frames. Shape-based radiomic features extracted from these segmentations were used to train a survival model based on gradient-boosted trees. The ML model and a derived actionable echocardiographic marker were validated on an external cohort (N=48 patients). Results The 3-year risk of disease progression was 12% in the discovery cohort and 15% in the validation cohort. The ML model achieved a C-index of 0.66 (95% CI [0.54, 0.77], nested cross-validation folds) in the discovery cohort and 0.67 (95% CI [0.46, 0.88], 100 bootstrapped samples) in the validation cohort. Following model interpretation, the left atrioventricular coupling index (area-derived LACI) at end-diastole was derived, and used as a risk score, achieving a C-index of 0.67 (95% CI [0.58, 0.77]) and 0.72 (95% CI [0.56, 0.88]) in the discovery and validation cohorts, respectively (100 bootstrapped samples). The high-risk group, with LACI>0.51, had a 3-year risk of disease progression of 19% (95% CI [13%, 36%]) compared to 8% (95% CI [5%, 15%]) for the low-risk group LACI ≤0.51 in the discovery cohort. Conclusion A fully automated ML model identifies area-derived LACI at end-diastole as a robust feature associated with disease progression, providing improved risk stratification for pre-symptomatic HCM.