DH-452784-4 AN ELECTROCARDIOGRAPHY-BASED DEEP LEARNING ALGORITHM FOR IDENTIFICATION OF INDIVIDUALS AT INCREASED RISK OF SUDDEN CARDIAC ARREST

Abstract

Sudden cardiac arrest (SCA) is a major public health problem with significant need to improve screening methods for detection of high risk. Recently developed ECG-based deep learning (DL) models have shown high accuracy for detection of selected heart disease conditions. To train and validate a 12-lead ECG-based DL algorithm for detection of increased SCA risk. Individuals who suffered out-of-hospital SCA were prospectively ascertained in the Portland, Oregon, metro area (The Oregon Sudden Unexpected Death Study, catchment population ∼1 million, 2002-onwards). A total of 1,838 12-lead ECGs from 1,796 cases obtained by health care providers prior and unrelated to the individual SCA events were digitized and employed to develop an ECG-based DL model for prediction of SCA risk (training, validation, and testing). External validation was performed in 717 ECGs from 714 SCA cases (The Ventura Prediction of Sudden Death in Multi-ethnic Communities study, catchment population ≈850,000, 2015-onwards). Two separate control group samples were obtained from 1,350 ECGs taken from 1,325 clinically matched individuals. Compared to SCA cases in the internal dataset, individuals in the external dataset were older, and more often female or Hispanic. The prevalence of prior myocardial infarction and heart failure was lower among SCA cases in the external cohort. The DL model achieved an AUC of 0.887 (95% CI 0.857-0.915) for detection of SCA cases in the internal test dataset compared to control patients in the held-out test cohort. Despite the demographic and clinical differences, the model was successfully validated among external SCA cases with an AUC of 0.821 (95% CI 0.791-0.850). The DL model performed significantly better than a previously developed and validated conventional ECG electrical risk score, which achieved an AUC of 0.705 (0.661-0.749) in the internal cohort and 0.742 (0.710-0.774) in the external cohort. An ECG-based DL model distinguished SCA cases from clinically matched control subjects with high accuracy, performing better than a conventional ECG risk model. These findings suggest that this DL model has potential for further evaluation as a screening tool for SCA risk.