Artificial Intelligence-Based ECG Diagnosis of Myocardial Infarction in High-Risk Emergency Department Patients

Background: Myocardial infarctions are often missed in the emergency department. Deep learning models have shown promise in ECG classification in managed settings. We postulate that deep learning models can be useful for ECG myocardial infarction discrimination also in real-world emergency department patients with a high suspicion of an acute coronary syndrome. Methods: We studied emergency department patients in the Stockholm region between 2007 and 2016 that had an ECG obtained and were admitted to a coronary care unit. We developed a deep neural network based on convolutional layers similar to a residual network. Inputs were ECG, age and sex; output label was non-ST-elevation myocardial infarction (NSTEMI), ST-elevation myocardial infarction (STEMI), and control status as registered in the SWEDEHEART registry by the discharging coronary care unit physician. We used an ensemble of five models. Findings: Of the included 12,311 coronary care unit admissions, 3,993 were recorded with an NSTEMI, 1,340 a STEMI, and 6,978 had not had a myocardial infarction. In a random test set, our model could discriminate STEMIs with fair precision, with a C-statistic of 0.85 and a Brier score of 0.10. Discrimination of NSTEMIs was poorer, with a C-statistic of 0.76 and a Brier score of 0.18. The performance was similar in a temporal test set that did not overlap in time with the training set. Interpretation: We developed and validated a deep learning model with human-level performance in classifying NSTEMI and STEMI on the presenting ECG of a real-world sample of emergency department patients with a high suspicion of an acute coronary syndrome. Deep learning models for ECG decision support could be valuable in the emergency department. Funding: The Kjell and Marta Beijer Foundation, Anders Wiklof, the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and Uppsala University. Declaration of Interest: JS reports stock ownership in companies providing services to Itrim, Amgen, Janssen, Novo Nordisk, Eli Lilly, Boehringer, Bayer, Pfizer and AstraZeneca, outside the submitted work. No other authors have anything to declare. Ethical Approval: The study was approved by the Swedish Ethical Review Agency, application number 2020-01654.