Abstract 4119992: 12-lead electrocardiograms predict adverse cardiovascular outcomes of emergency department patients

Introduction: Chest pain is a common cause for presentation to the emergency department (ED). Identifying patients at increased risk of adverse cardiovascular outcomes may help guide testing. Aims: We sought to determine if deep learning enabled 12-lead electrocardiogram (ECG) analysis may improve prediction of adverse cardiovascular outcomes of ED patients presenting with chest pain compared to conventional approaches. Methods: A pretrained convolutional neural network was applied to derive numerical representations of the index ECG waveforms from patients presenting to the Brigham and Women’s Hospital (BWH) ED with chest pain. We trained a neural network model (‘CP-AI’) to predict 30-day major adverse cardiovascular events (MACE) defined as myocardial infarction (MI), pulmonary embolism (PE), aortic dissection (AD), or mortality using age, sex, cardiac biomarkers (troponin and d-dimer), and the numerical ECG representations as inputs ( Figure 1 ). In a holdout sample of Massachusetts General Hospital (MGH) ED patients, we compared the performance of the CP-AI model to alternative models fit on (1) demographics (‘Age and Sex’) (2) demographics + cardiac biomarkers (‘Biomarker Model’), and (3) demographics + numerical ECG representations (‘ECG Model’). Results: The BWH derivation sample included 18,811 individuals (51% female, mean age 65 ± 16 years). The MGH validation sample included 14,476 individuals (45% female, mean age 65 ± 15 years). The CP-AI model significantly outperformed the comparison models for prediction of 30-day MACE with an area under the receiver operating characteristic curve (AUROC) of 0.82 [95% CI 0.81-0.83] ( Figure 1 ). CP-AI also significantly outperformed the comparison models for classification of the 30-day MACE components including MI (AUROC 0.91 [95% CI 0.90-0.92]), PE (AUROC 0.74 [95% CI 0.72-0.76]), and mortality (AUROC 0.86 [95% CI 0.85-0.87]), but not aortic dissection (0.71 [95% CI 0.68-0.75]). Conclusions: The integration of deep learning ECG analysis with conventional assessment improved prediction of adverse cardiovascular of ED patients presenting with chest pain.