Journal Article Research Support, Non-U.S. Gov't Epublish 8 citations
PubMed: 36702844 DOI: 10.1038/s41598-022-26617-w
Prediction model for myocardial injury after non-cardiac surgery using machine learning.
Sci Rep 13(1):1475
Authors: Ah Ran Oh, Jungchan Park, Seo Jeong Shin, Byungjin Choi, Jong-Hwan Lee, Seung-Hwa Lee, Kwangmo Yang
Affiliations
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. Department of Anesthesiology and Pain Medicine, Kangwon National University Hospital, Chuncheon, Korea.
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
- Heart Vascular Stroke Institute, Rehabilitation & Prevention Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea. Division of Cardiology, Department of Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea. Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, Korea.
Abstract
Myocardial injury after non-cardiac surgery (MINS) is strongly associated with postoperative outcomes. We developed a prediction model for MINS and have provided it online. Between January 2010 and June 2019, a total of 6811 patients underwent non-cardiac surgery with normal preoperative level of cardiac troponin (cTn). We used machine learning techniques with an extreme gradient boosting algorithm to evaluate the effects of variables on MINS development. We generated two prediction models based on the top 12 and 6 variables. MINS was observed in 1499 (22.0%) patients. The top 12 variables in descending order according to the effects on MINS are preoperative cTn level, intraoperative inotropic drug infusion, operation duration, emergency operation, operation type, age, high-risk surgery, body mass index, chronic kidney disease, coronary artery disease, intraoperative red blood cell transfusion, and current alcoholic use. The prediction models are available at https://sjshin.shinyapps.io/mins_occur_prediction/ . The estimated thresholds were 0.47 in 12-variable models and 0.53 in 6-variable models. The areas under the receiver operating characteristic curves are 0.78 (95% confidence interval [CI] 0.77-0.78) and 0.77 (95% CI 0.77-0.78), respectively, with an accuracy of 0.97 for both models. Using machine learning techniques, we demonstrated prediction models for MINS. These models require further verification in other populations.
MeSH
Coronary Artery Disease, Machine Learning, Natural Science Disciplines, Learning
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- Development and Validation of an Explainable Machine Learning Model for Predicting Myocardial Injury After Noncardiac Surgery in Two Centers in China: Retrospective Study. JMIR Aging, 2024
- Prediction of perioperative myocardial infarction/injury in high-risk patients after noncardiac surgery. Eur Heart J Acute Cardiovasc Care, 2023
- Cardiac Troponin as a Prognostic Indicator for Major Adverse Cardiac Events in Non-Cardiac Surgery: A Narrative Review. Diagnostics (Basel), 2025
- Diastolic Versus Systolic or Mean Intraoperative Hypotension as Predictive of Perioperative Myocardial Injury in a White-Box Machine-Learning Model. Anesth Analg, 2025