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.

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