# Predicting Occlusion Myocardial Infarctions in the Emergency Department Using Artificial Intelligence

## TL;DR

This study developed an AI model to predict heart attacks caused by blocked arteries in emergency patients with chest pain, showing better accuracy than current methods.

## Contribution

The novel contribution is an AI model that improves detection of occlusion myocardial infarctions in emergency departments compared to traditional criteria.

## Key findings

- The AI model achieved 95.3% AUC in predicting OMI, significantly higher than the 27% sensitivity of STEMI criteria.
- Only 5.4% of OMI cases received timely angiography, highlighting a need for better detection methods.
- The model's sensitivity was doubled compared to existing criteria at the same level of specificity.

## Abstract

The objective was to develop an artificial intelligence (AI) model for predicting acute coronary occlusion myocardial infarction (OMI) in patients with chest pain at the emergency department (ED), using information that is widely available early in the ED assessment.

In a cohort of 24,511 consecutive adult ED patients with chest pain from 5 Swedish hospitals, OMI cases were identified through register data and manual review of health records and angiographies. Ambulance patients bypassing the ED due to ST-elevation myocardial infarction (STEMI) were not included in the cohort. A deep-learning AI model was created to predict OMI using the electrocardiogram, optionally combined with other early ED data, including medical history and initial lab values. The model was internally validated on held-out data and compared with the STEMI criteria.

A total of 467 patients (1.9%) were identified as OMI, corresponding to 29% of all acute myocardial infarction cases. The 30-day mortality rate was 6.6% for OMI, compared with 3.3% for non-OMI. Only 5.4% of the OMI cases received angiography within the guideline-recommended maximum of 90 minutes after ED arrival. The AI model achieved an area under the receiver operating characteristic (AUC) of 95.3% (95% CI, 93.8%-97.3%), with a sensitivity of 62% compared with 27% for the STEMI criteria (difference 34.5%; 95% CI, 22.9%-45.2%) at the same specificity (97.4%).

Our AI model identified OMI in ED patients with chest pain with an AUC of 95%, doubling sensitivity compared with the STEMI criteria at the same specificity. Using the model could reduce time to intervention, as only about 1 in 20 OMI cases currently receive timely angiography.