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Predicting Occlusion Myocardial Infarctions in the Emergency Department Using Artificial Intelligence

Nyström, Axel LU ; Björkelund, Anders LU ; Wagner, Henrik LU orcid ; Ekelund, Ulf LU orcid ; Ohlsson, Mattias LU orcid ; Björk, Jonas LU orcid ; Mokhtari, Arash LU and Lundager Forberg, Jakob LU (2026) In Journal of the American college of emergency physicians open 7(1).
Abstract
Objectives
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.
Methods
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,... (More)
Objectives
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.
Methods
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.
Results
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%).
Conclusion
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. (Less)
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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of the American college of emergency physicians open
volume
7
issue
1
article number
100299
publisher
John Wiley & Sons Inc.
ISSN
2688-1152
DOI
10.1016/j.acepjo.2025.100299
language
English
LU publication?
yes
id
f0c0dbb3-3e14-461f-8f49-ed1b556a5461
date added to LUP
2026-01-21 10:30:44
date last changed
2026-01-21 11:13:14
@article{f0c0dbb3-3e14-461f-8f49-ed1b556a5461,
  abstract     = {{Objectives<br/>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.<br/>Methods<br/>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.<br/>Results<br/>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%).<br/>Conclusion<br/>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.}},
  author       = {{Nyström, Axel and Björkelund, Anders and Wagner, Henrik and Ekelund, Ulf and Ohlsson, Mattias and Björk, Jonas and Mokhtari, Arash and Lundager Forberg, Jakob}},
  issn         = {{2688-1152}},
  language     = {{eng}},
  month        = {{01}},
  number       = {{1}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Journal of the American college of emergency physicians open}},
  title        = {{Predicting Occlusion Myocardial Infarctions in the Emergency Department Using Artificial Intelligence}},
  url          = {{http://dx.doi.org/10.1016/j.acepjo.2025.100299}},
  doi          = {{10.1016/j.acepjo.2025.100299}},
  volume       = {{7}},
  year         = {{2026}},
}