Deep Learning Electrocardiogram Model for Risk Stratification of Coronary Revascularization Need in the Emergency Department

Abstract Background and Aims Identification of patients with acute coronary syndrome requiring coronary revascularization can be challenging due to inconclusive electrocardiogram (ECG) findings or biomarker results. A deep learning model to detect ECG patterns associated with revascularization likelihood was developed, aiming to guide further assessment and reduce diagnostic uncertainty. Methods A convolutional neural network model was trained on 144,691 ED visits from a US cohort (60±19 years; 53% female; 0.6% revascularization), tested on a separate test cohort (n=35,995), and benchmarked against clinician ECG interpretation and cardiac troponin T (TnT). External validation was performed for the outcomes revascularization and type 1 myocardial infarction (MI) on 18,673 ED visits from Europe (55±21 years; 49% female; 1.5% revascularization; 1% type 1 MI). Primary performance metric was area under the receiver operating characteristic curve (AUROC). Results In the test cohort, the model achieved an AUROC of 0.91 (95% confidence interval [CI] 0.91–0.91), outperforming clinician ECG interpretation (AUROC 0.65, 95% CI 0.54–0.76) and conventional cardiac TnT (AUROC 0.71). In the external validation cohort, ECG model AUROC was 0.81 (95% CI 0.81–0.82) for revascularization, and 0.85 (95% CI 0.84–0.85) for type 1 MI, compared to 0.67 (95% CI 0.54–0.81) and 0.74 (95% CI 0.56–0.92) for clinician interpretation, and 0.85 and 0.87 for high-sensitivity (hs)-TnT, respectively. The ECG model had higher specificity but lower sensitivity compared to hs-TnT. Conclusions The model was able to detect revascularization and type 1 MI with competitive performance, suggesting a potential role to complement current clinical assessment.