Rechommend: An Ecg-Based Machine-Learning Approach for Identifying Patients at High-Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography

AHA Scientific Sessions 2021 Presentation
Authors Alvaro Ulloa Cerna, Linyuan Jing, John Pfeifer, Sushravya Raghunath, Jeffrey Ruhl, Daniel Rocha, Joseph Leader, Noah Zimmerman, Steven R Steinhubl, Greg Lee, Christopher Good, Christopher M Haggerty, Brandon Fornwalt, and Ruijun Chen

Introduction: Timely diagnosis of structural heart disease improves patient outcomes, yet millions remain undiagnosed. ECG-based prediction models can help identify high-risk patients for targeted screening, but existing individual disease models often have low positive predictive values (PPV) and limited clinical utility.

Hypothesis: An ECG-based composite model can predict one of multiple, actionable structural heart conditions and yield higher prevalence and PPVs than individual models.

Methods: Using 2,141,366 ECGs linked to echocardiography and EHR records from 461,466 adults from 1984-2021, we trained machine learning models to predict any of 7 echocardiography-confirmed diseases within 1 year. This composite label included: moderate or severe valvular disease (aortic stenosis or regurgitation, mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction of <50%, or interventricular septal thickness >15mm. We tested various combinations of inputs and evaluated model performance with 1) cross-validation and 2) a simulated retrospective deployment. We measured area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC), and PPV at 90% sensitivity.

Results: Our composite “rECHOmmend” model using age, sex and ECG traces had an AUROC of 0.91, AUPRC of 0.78, and PPV of 52% at 90% sensitivity and 23% disease prevalence. Individual disease models had similar AUROCs (0.88-0.93), but lower AUPRCs (0.07-0.71) and PPVs (2%-41%; Figure). Across inputs, model AUROCs ranged from 0.85 to 0.93. Our simulated deployment model classified 22% of at-risk patients in 2010 as high-risk, of whom 40% developed true, echo-confirmed disease within 1 year.

Conclusions: An ECG-based machine learning model using a composite endpoint can predict undiagnosed structural heart disease, outperforming single disease models with higher PPVs to facilitate targeted screening with echocardiography.