Composite Deep Learning ECG Algorithm Trained to Identify Structural Heart Diseases Can Identify Clinically Ascertained Hypertrophic Cardiomyopathy

AHA Scientific Sessions 2022 PRESENTATION
Authors Greg Lee, Martin Kang, Alvaro Ulloa Cerna, Dustin Hartzel, Daniel Rocha, Arun Nemani, Brandon Fornwalt, Ruijun Chen, John Pfeifer and Christopher M Haggerty

Introduction: A deep learning ECG algorithm, rECHOmmend, can accurately identify patients with any of seven structural heart diseases: five valvular diseases, low ejection fraction and interventricular septal (IVS) thickening. Components of the rECHOmmend composite label (IVS>15mm, mitral regurgitation) are also associated with hypertrophic cardiomyopathy (HCM). We hypothesized that despite being trained without HCM-specific labels, rECHOmmend can reliably identify HCM patients and achieve comparable performance to an HCM-specific classifier.

Methods: Algorithms were developed from 2,898,979 ECGs acquired from 661,366 patients between 1984-2021. rECHOmmend was trained on a composite label derived from echocardiography and electronic health record (EHR) data. This ensemble model consists of 7 disease specific models with an aggregate model to predict a composite structural heart disease endpoint with shared clinical actionability. Separately, an HCM-specific model was trained on a binary label derived from EHR. To enable comparison, both classifiers were tested on a shared ECG holdout set (ECG prevalence 1.24%, patient prevalence 0.52%).

Results: Despite being trained without HCM specific labels, the rECHOmmend ensemble showed comparable performance to a HCM-specific classifier (C-statistic: 0.92 [0.90-0.93] vs 0.90 [0.89-0.91]). At an operating point optimized for the F1-score, the sensitivity to HCM was higher for rECHOmmend at 0.42 [0.33-0.50] compared to 0.18 [0.15-0.21] for the HCM-specific classifier. rECHOmmend sustained performance across a range of IVS thicknesses, suggesting it was not solely reliant on IVS thickening for HCM identification and other ensemble components contributed to performance.

Conclusions: A composite deep learning algorithm trained to identify structural heart diseases can identify clinically ascertained HCM with good performance, despite being trained without HCM-specific labels.