Feasibility of remote monitoring for fatal coronary heart disease using Apple Watch ECGs

BackgroundFatal Coronary Heart Disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic-Artificial Intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts.ObjectivesTo develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs.MethodsA FCHD single-lead ('Lead I' from 12-Lead ECGs) ECG-AI model was developed using 167,662 ECGs (50,132 patients) from the University of Tennessee Health Sciences Center. 80% of the data (five-fold cross-validation) was used for training and 20% as a holdout. Cox Proportional Hazard (CPH) models incorporating ECG-AI predictions with age, sex and race were also developed. The models were tested on paired clinical single-lead and Apple Watch ECGs from 243 St. Jude Life Cohort participants. The correlation and concordance of the predictions were assessed using Pearson's correlation(R), Spearman's correlation(ρ) and Cohen's Kappa.ResultsThe ECG-AI and CPH models resulted in AUCs=0.76 and 0.79, respectively, on the 20% holdout and AUC=0.85 and 0.87 on the AHWFB external validation data. There was moderate-strong positive correlation between predictions (R=0.74, ρ=0.67 and Kappa=0.58) when tested on the 243 paired ECGs. The clinical (Lead I) and Apple Watch predictions led to the same low/high risk FCHD classification for 99% of the participants. CPH prediction correlation resulted in a R=0.81, ρ=0.76 and Kappa=0.78.ConclusionRisk of FCHD can be predicted from single-lead ECGs obtained from wearable devices and are statistically concordant with Lead I of a 12-lead ECG.