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.

Introduction: Cardiac amyloidosis (CA) is a common cause of progressive heart failure. New therapies can improve outcomes but most CA patients remain undiagnosed and untreated. Machine learning models deployed on electronic health record (EHR) data may be able to find patients with undiagnosed CA. To date, most models have focused on identification of undiagnosed amyloid from uncensored data modalities (Fig 1). Hypothesis: We hypothesized that lack of post-diagnosis censoring when training CA models leads to poor performance in predicting patients with undiagnosed CA whereas training with appropriate time censoring improves performance. Methods: We used 41 EHR features (demographics, labs, electrocardiogram/echocardiography measurements, vitals) to train a boosted decision tree model with and without time censoring. This was applied to 112 patients with confirmed CA and 22,400 controls matched on age, sex, encounter frequency and timespan of EHR. We also compared our findings to a web-based CA algorithm that was publicly available in 2020. Results: The EHR algorithm had modestly higher performance on at-risk, time-censored patients when trained with and without time censoring (area under the receiver operating characteristic curve (AUROC) 0.84±0.09 vs 0.79±0.07). Testing on temporally uncensored data showed higher performance (AUROC: 0.91±0.05) which may be unrepresentative of deployment scenarios where post-diagnostic features are unavailable for model use. The publicly available algorithm demonstrated a similar trend when tested on uncensored data (AUROC: 0.67±0.03) as compared to an appropriately censored feature set (AUROC: 0.54±0.04). Conclusions: EHR algorithms can be trained to find patients with high risk of undiagnosed cardiac amyloidosis. These models should be evaluated on temporally censored data so that post-diagnostic features do not artificially inflate performance estimates and negatively impact real-world deployment.

BACKGROUND:Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values to facilitate meaningful recommendations for echocardiography. METHODS:Using 2 232 130 ECGs linked to electronic health records and echocardiography reports from 484 765 adults between 1984 to 2021, we trained machine learning models to predict the presence or absence of any of 7 echocardiography-confirmed diseases within 1 year. This composite label included the following: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction <50%, or interventricular septal thickness >15 mm. We tested various combinations of input features (demographics, laboratory values, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multisite validation trained on 1 site and tested on 10 independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010. RESULTS:Our composite rECHOmmend model used age, sex, and ECG traces and had a 0.91 area under the receiver operating characteristic curve and a 42% positive predictive value at 90% sensitivity, with a composite label prevalence of 17.9%. Individual disease models had area under the receiver operating characteristic curves from 0.86 to 0.93 and lower positive predictive values from 1% to 31%. Area under the receiver operating characteristic curves for models using different input features ranged from 0.80 to 0.93, increasing with additional features. Multisite validation showed similar results to cross-validation, with an aggregate area under the receiver operating characteristic curve of 0.91 across our independent test set of 10 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without preexisting structural heart disease in the year 2010, 11% were classified as high risk and 41% (4.5% of total patients) developed true echocardiography-confirmed disease within 1 year. CONCLUSIONS:An ECG-based machine learning model using a composite end point can identify a high-risk population for having undiagnosed, clinically significant structural heart disease while outperforming single-disease models and improving practical utility with higher positive predictive values. This approach can facilitate targeted screening with echocardiography to improve underdiagnosis of structural heart disease.