Machine learning improves risk stratification after acute coronary syndrome
Open accessAbstract
The accurate assessment of a patient’s risk of adverse events remains a mainstay of clinical care. Commonly used risk metrics have been based on logistic regression models that incorporate aspects of the medical history, presenting signs and symptoms, and lab values. More sophisticated methods, such as Artificial Neural Networks (ANN), form an attractive platform to build risk metrics because they can easily incorporate disparate pieces of data, yielding classifiers with improved performance. Using two cohorts consisting of patients admitted with a non-ST-segment elevation acute coronary syndrome, we constructed an ANN that identifies patients at high risk of cardiovascular death (CVD). The ANN was trained and tested using patient subsets derived from a cohort containing 4395 patients (Area Under the Curve (AUC) 0.743) and validated on an independent holdout set containing 861 patients (AUC 0.767). The ANN 1-year Hazard Ratio for CVD was 3.72 (95% confidence interval 1.04–14.3) after adjusting for the TIMI Risk Score, left ventricular ejection fraction, and B-type natriuretic peptide. A unique feature of our approach is that it captures small changes in the ST segment over time that cannot be detected by visual inspection. These findings highlight the important role that ANNs can play in risk stratification.
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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 [25]
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 [25]
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 [25]
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- Authors
- Myers, P. D., Scirica, B. M., & Stultz, C. M.
- Affiliations
- Massachusetts Institute of Technology (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Stultz, Collin M.
- Venue
- Scientific Reports
- Published
- 2017-10-04
- Volume
- 7
- Issue
- 1
- Pages
- 12692-12692
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5627253
- OpenAlex
- W2759932154
- Cited by
- 75
- References
- 47
- Citations by year
- 2026: 6 · 2025: 3 · 2024: 11 · 2023: 6 · 2022: 8 · 2021: 25 · 2020: 9 · 2019: 4 · 2018: 3
- FWCI
- 6.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Nature
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Acute Myocardial Infarction Research
- Keywords
- acute coronary syndrome, risk stratification, artificial neural networks, cardiovascular mortality prediction, non-ST-elevation myocardial infarction, ST-segment changes, TIMI risk score, left ventricular ejection fraction, brain natriuretic peptide, machine learning in cardiology
- MeSH terms
- Machine Learning, Aged, Electrocardiography, Female, Humans, Male, Middle Aged, Models, Cardiovascular, Cohort Studies, Multivariate Analysis, Logistic Models, Neural Networks, Computer, Risk Assessment, Kaplan-Meier Estimate, Acute Coronary Syndrome
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