Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction
Open accessAbstract
OBJECTIVE: Conventional risk stratification models for mortality of acute myocardial infarction (AMI) have potential limitations. This study aimed to develop and validate deep-learning-based risk stratification for the mortality of patients with AMI (DAMI). METHODS: The data of 22,875 AMI patients from the Korean working group of the myocardial infarction (KorMI) registry were exclusively divided into 12,152 derivation data of 36 hospitals and 10,723 validation data of 23 hospitals. The predictor variables were the initial demographic and laboratory data. The endpoints were in-hospital mortality and 12-months mortality. We compared the DAMI performance with the global registry of acute coronary event (GRACE) score, acute coronary treatment and intervention outcomes network (ACTION) score, and the thrombolysis in myocardial infarction (TIMI) score using the validation data. RESULTS: In-hospital mortality for the study subjects was 4.4% and 6-month mortality after survival upon discharge was 2.2%. The areas under the receiver operating characteristic curves (AUCs) of the DAMI were 0.905 [95% confidence interval 0.902-0.909] and 0.870 [0.865-0.876] for the ST elevation myocardial infarction (STEMI) and non ST elevation myocardial infarction (NSTEMI) patients, respectively; these results significantly outperformed those of the GRACE (0.851 [0.846-0.856], 0.810 [0.803-0.819]), ACTION (0.852 [0.847-0.857], 0.806 [0.799-0.814] and TIMI score (0.781 [0.775-0.787], 0.593[0.585-0.603]). DAMI predicted 30.9% of patients more accurately than the GRACE score. As secondary outcome, during the 6-month follow-up, the high risk group, defined by the DAMI, has a significantly higher mortality rate than the low risk group (17.1% vs. 0.5%, p < 0.001). CONCLUSIONS: The DAMI predicted in-hospital mortality and 12-month mortality of AMI patients more accurately than the existing risk scores and other machine-learning methods.
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ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.
“These ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [24]
Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated.
“Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated (16, 20–25).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [24]
Healthcare systems have increasingly sought to use ML to assist in risk stratification, and these ML models may outperform statistical models in calibration and discrimination.
“Healthcare systems have increasingly sought to use ML to assist in risk stratification, and these ML models may outperform statistical models in calibration and discrimination.”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [24]
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Accessing Artificial Intelligence for Clinical Decision-Making
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- Authors
- Kwon, J.-M., Jeon, K.-H., Kim, H. M., Kim, M. J., Lim, S., Kim, K.-H., Song, P. S., Park, J., Choi, R. K., & Oh, B.-H.
- Affiliations
- Sejong Institute (KR)
- Corresponding author
- Jeon, Ki-Hyun
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2019-10-31
- Volume
- 14
- Issue
- 10
- Pages
- e0224502
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6822714
- OpenAlex
- W2982374880
- Cited by
- 86
- References
- 52
- Citations by year
- 2026: 3 · 2025: 19 · 2024: 11 · 2023: 9 · 2022: 19 · 2021: 22 · 2020: 3
- FWCI
- 7.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Korea Meteorological Administration
- Centers for Disease Control and Prevention
- Korea Centers for Disease Control and Prevention
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Acute Myocardial Infarction Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- acute myocardial infarction, deep learning, risk stratification, mortality prediction, in-hospital mortality, 6-month mortality, GRACE risk score, TIMI score, ST-elevation myocardial infarction, non-ST-elevation myocardial infarction, risk prediction model validation, model generalization
- MeSH terms
- Machine Learning, ST Elevation Myocardial Infarction, Non-ST Elevated Myocardial Infarction, Deep Learning, Acute Disease, Aged, Female, Humans, Male, Middle Aged, Myocardial Infarction, Registries, Risk Factors, ROC Curve, Time Factors, Hospital Mortality, Risk Assessment, Area Under Curve, Republic of Korea
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