Validation of an artificial intelligence model for occlusion myocardial infarction identification: initial findings from a Portuguese cohort

Abstract Background Around 15 to 30% of patients presenting without significant ST-segment elevation have an acutely occluded coronary artery. These patients have a worse prognosis, likely related to delayed revascularization. Purpose We aimed to test a novel artificial intelligence (AI) model designed to enhance the detection of these cases based on admission 12-lead electrocardiograms (ECGs). Methods A total of 658 ECGs from 398 patients admitted to the emergency department with suspected acute coronary syndrome (ACS) were retrospectively analyzed via the OMI AI ECG Model. The primary endpoint was the detection of occlusion myocardial infarction (OMI), defined as angiographic evidence of an acute culprit lesion with either 0-2 TIMI flow and positive troponin or TIMI 3 flow and significant troponin elevation (i.e. high-sensitivity troponin I ≥ 5000 ng/L). The model’s performance was compared with the current gold standard. Results In this initial test set, we identified 147 (36.9%) OMI cases. The OMI AI ECG Model achieved 72% accuracy (95% confidence interval (CI): 67.4–76.5), 44.2% sensitivity (95% CI: 37.2–51.6), 91.9% specificity (95% CI: 88.7-94.8), 79.6% PPV (95% CI: 71.9-86.6), NPV 69.8% (95% CI: 63.9-75.4), and a 0.422 Mathew’s correlation coefficient (MCC; 95% CI: 0.341-0.503), whereas the ST-segment elevation myocardial infarction (STEMI) criteria had 66.3% accuracy (95% CI: 61.0-71.3), 28.5% sensitivity (95% CI: 22.3-35.3), 93.2% specificity (95% CI: 90.1-96.1), 75.0% PPV (95% CI: 64.8-84.5), 64.6% NPV (95% CI: 58.6-70.4), and a 0.293 MCC (95% CI: 0.206-0.38)]. Demographic parameters, such as age and sex, did not impact model performance. Notably, within the patient group who underwent coronary angiography within 2 hours of admission, the model’s sensitivity increased to 81.2% (CI: 73.1-88.5), reflecting good model performance in acute/active case detection. Conclusion In this challenging all-comer suspect ACS cohort, the OMI AI ECG Model outperformed the STEMI criteria in active OMI detection, with about 1.5 times higher sensitivity, without compromising specificity. This tool may contribute to better patient triage and timely revascularization.