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.

Keywords: 12-lead ECG Analysis; Clinical Outcome Prediction; Coronary Revascularization; Machine Learning; Risk Stratification.