Deep learning electrocardiogram model for risk stratification of coronary revascularization need in the emergency department

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: Test cohort AUROC 0.91 vs clinician interpretation 0.65 vs troponin T 0.71

• Primary Outcome: AUROC for coronary revascularization prediction

• Nnt Or Sensitivity: External validation AUROC 0.81 for revascularization, 0.85 for Type 1 MI

• Confidence Interval: 95% CI 0.91-0.91 for deep learning model in test cohort

Clinical Application

Requires implementation of deep learning infrastructure and ECG digitization systems in emergency departments Relevant to Australian EDs; would need TGA approval for clinical use; could complement existing ACS pathways per RACGP guidelines; potential PBS implications if reduces unnecessary investigations Emergency department patients presenting with chest pain or suspected acute coronary syndrome

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 with 0.70 (95% CI 0.57-0.83) 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 with high-sensitivity-troponin T. CONCLUSIONS: The model was able to detect revascularization and Type 1 MI with competitive performance, suggesting a potential role to complement current clinical assessment.

References

1. 1.Büscher, A., Plagwitz, L., Yildirim, K., Brix, T. J., Neuhaus, P., Bickmann, L., Menke, A. F., van Almsick, V. F., Pavenstädt, H., Kümpers, P., Heider, D., Varghese, J., & Eckardt, L. (2026). Deep learning electrocardiogram model for risk stratification of coronary revascularization need in the emergency department. European Heart Journal. https://doi.org/10.1093/eurheartj/ehaf254