Deep learning on electrocardiogram waveforms to stratify risk of obstructive stable coronary artery disease.

Aims: Coronary artery disease (CAD) incidence continues to rise with an increasing burden of chronic coronary disease (CCD). Current probability-based risk assessment for obstructive CAD (oCAD) lacks sufficient diagnostic accuracy. We aimed to develop and validate a deep learning (DL) algorithm utilizing electrocardiogram (ECG) waveforms and clinical features to predict oCAD in patients with suspected CCD. Methods and results: The study includes subjects undergoing invasive angiography for evaluation of CCD over a 4-year period at a quaternary care centre. oCAD was defined as performance of percutaneous coronary intervention (PCI) based on assessment by interventional cardiologists during elective angiography. DL models were developed for ECG waveforms alone (DL-ECG), clinical features from standard risk scores (DL-Clinical), and the combination of ECG waveforms and clinical features (DL-MM); a commonly used pre-test probability estimation tool from the CAD Consortium study was used for comparison (CAD2) [3]. The CAD2 model [AUC 0.733 (0.717-0.750)] had similar performance as the DL-Clinical model [AUC 0.762 (0.746-0.778)]. The DL-ECG model [AUC 0.741 (0.726-0.758)] had similar performance as both the clinical feature models. The DL-MM model [AUC 0.807 (0.793-0.822)] had a superior performance. Validation in an external cohort demonstrated similar performance in the DL-MM [AUC 0.716 (0.707-0.726)] and CAD2 risk score [AUC 0.715 (0.705-0.724)]. Conclusion: A multi-modality DL model utilizing ECG waveforms and clinical risk factors can improve prediction of oCAD in CCD compared with risk-factor based models. Prospective research is warranted to determine whether incorporating DL methods in ECG analysis improves diagnosis of oCAD and outcomes in CCD.