ECG-based deep learning for detecting epicardial coronary occlusion in acute myocardial infarction

Abstract

Abstract Background One third of Non-ST-elevation myocardial infarction (NSTEMI) patients have occlusion myocardial infarction (OMI) associated with poor short- and long-term outcomes due to delayed invasive management. Purpose We sought to develop and externally validate a versatile artificial intelligence (AI)-model detecting OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art criteria. Methods An AI model was developed using 18,616 ECGs from 10,692 unique contacts (22.9% OMI) of 10,543 patients (age 66±14 years, 65.9% males) with acute coronary syndrome (ACS) originating from an international database and a tertiary care center. This AI model was tested on a holdout set of 3,254 ECGs from 2,263 unique contacts (20% OMI) of 2,222 patients (age 62±14 years, 67% males) and compared with STEMI criteria and annotations of ECG experts in detecting OMI on 12-lead ECGs using sensitivity, specificity, and predictive values. Results The OMI AI model achieved an AUROC of 0.941 (95% CI: 0.926, 0.954) in identifying the primary outcome of OMI on 12-lead ECGs in the holdout set [Figure 1A], yielding robust performance across genders and age subgroups (ranging from 0.907 to 0.951 AUROC) [Figure 1B]. Sensitivity in detecting OMI was significantly higher for OMI AI model compared to STEMI criteria (82.6% [95% CI: 78.9%, 86.1%] vs. 34.4% [95% CI: 30.0%, 38.8%]) and statistically equal compared to ECG experts 75.9% [95% CI: 71.9%, 80%] in identifying OMI [Table 1]. The OMI AI Model showed statistically superior performance compared to STEMI criteria and equal (non-inferior) performance to ECG experts when evaluated using industry-standard metrics. Conclusions This sizeable validation of an AI ECG model demonstrates the ability of AI to detect acute OMI and suggests a potential in improving ACS patient triage and need for immediate revascularization.AI Model Performance on Holdout SetBenchmark Comparison