An artificial intelligence model for electrocardiogram detection of occlusion myocardial infarction: a retrospective study to reduce false-positive cath lab activations.

Aims: Existing ST-segment elevation myocardial infarction (STEMI) alert pathways that rely on traditional STEMI criteria perform suboptimally. We aimed to evaluate the diagnostic performance of an artificial intelligence (AI) model to detect acute occlusion myocardial infarction (OMI) from the routine 12-lead electrocardiogram (ECG) and, specifically, its potential to reduce false-positive activations. Methods and results: Consecutive adults managed via the STEMI pathway were included from a tertiary academic medical centre between January 2022 and December 2023. Cases without an available ECG for review, death prior to catheterization, or alternative reasons for activation (i.e. electrical instability or urgent interventions) were excluded. Pre-coronary angiography tracings were interpreted via the AI tool. Test characteristics were compared against traditional STEMI criteria. The primary outcome was the number of avoidable false-positive activations. During the 2-year study period, there were 454 activations, 150 were excluded, and 304 cases with unique ECGs were included in the study cohort. There were 118 (38.8%) false-positive activations, of which 86 (72.9%) were correctly predicted by the AI model. Its test characteristics for identifying true positives were superior compared with traditional STEMI criteria for a sensitivity of 89.2% [95% confidence interval (CI): 84.0-92.9] vs. 68.3% (95% CI: 61.3-74.5), specificity 72.9% (95% CI: 64.2-80.1) vs. 51.7% (95% CI: 42.8-60.5), and accuracy 82.9% (95% CI: 78.3-86.7) vs. 61.8 (95% CI: 56.3-67.1). Conclusion: The AI model is superior to traditional STEMI criteria for detecting OMI and has the potential to reduce false-positive catheterization lab activations. It can be a useful decision-aid for catheterization lab activation.