ArticleScientific reports2023
Prediction model for myocardial injury after non-cardiac surgery using machine learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Article
- Update in Perioperative Ischemic Workup: Integrating 2024 AHA/ACC Guidelines and Contemporary Evidence.Journal of cardiovascular development and disease · 2026Review
- Interpretable machine learning models for predicting perioperative myocardial injury in non-cardiac surgery.European heart journal. Digital health · 2026Article
- Congestive Heart Failure: Perioperative Risk Assessment and Therapeutic Consequences.Deutsches Arzteblatt international · 2026Review
- Interpretable artificial intelligence for predicting blood transfusion after surgery for femoral shaft fractures: A retrospective analysis.Indian journal of anaesthesia · 2026Article
- Article
- Cardiac Troponin as a Prognostic Indicator for Major Adverse Cardiac Events in Non-Cardiac Surgery: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Perioperative myocardial injury and infarction after noncardiac surgery: a review of pathophysiology, diagnosis, and management.Frontiers in cardiovascular medicine · 2024Review
- Prediction of perioperative myocardial infarction/injury in high-risk patients after noncardiac surgery.European heart journal. Acute cardiovascular care · 2023Article
- Age-Related Association between Sex and Postoperative Atrial Fibrillation in Non-Cardiac Surgery Patients: Observational Cohort Study.Reviews in cardiovascular medicine · 2023Article
- The relationships between body mass index, reciprocal ponderal index, waist-to-height ratio, and fitness in young adult males.Frontiers in psychology · 2023Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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