ArticleClinical cardiology2025
Predicting In-Hospital Mortality in Patients With Acute Myocardial Infarction: A Comparison of Machine Learning Approaches.
Article in Clinical cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Fufangteng Yixin Formula alleviates myocardial ischemia-reperfusion injury by modulating gut microbiota and resultant metabolites in rats.Journal of natural medicines · 2026Article
- Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.Journal of cardiovascular translational research · 2026Article
- A machine learning model for early risk stratification of 28-day mortality after myocardial infarction.European heart journal. Digital health · 2026Article
- Machine learning-based prediction of mortality risk from air pollution-induced acute coronary syndrome in the Western Pacific region.Scientific reports · 2026Article
- Development and validation of an interpretable machine learning model for predicting 5-year recurrence in breast cancer.Frontiers in medicine · 2026Article
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12 authors.
Funding
Abstract
backgroundAcute myocardial infarction (AMI) remains a leading global cause of mortality. This study explores predictors of in-hospital mortality among AMI patients using advanced machine learning (ML) techniques.
methodsData from 7422 AMI patients treated with percutaneous coronary intervention (PCI) at Tehran Heart Center (2015-2021) were analyzed. Fifty-eight clinical, demographic, and laboratory variables were evaluated. Seven ML algorithms, including Random Forest (RF), logistic regression with LASSO, and XGBoost, were implemented. The data set was divided into training (70%) and testing (30%) subsets, with fivefold cross-validation. The class imbalance was addressed using the synthetic minority oversampling technique (SMOTE). Model predictions were interpreted using SHapley Additive exPlanations (SHAP).
resultsIn-hospital mortality occurred in 129 patients (1.74%). RF achieved the highest predictive performance, with an area under the curve (AUC) of 0.924 (95% CI 0.893-0.954), followed by XGBoost (AUC 0.905) and logistic regression with LASSO (AUC 0.893). Sensitivity analysis in STEMI patients confirmed RF's robust performance (AUC 0.900). SHAP analysis identified key predictors, including lower left ventricular ejection fraction (LVEF; 33.24% vs. 43.46% in survivors, p < 0.001), higher fasting blood glucose (190.38 vs. 132.29 mg/dL, p < 0.001), elevated serum creatinine, advanced age (70.92 vs. 61.88 years, p < 0.001), and lower LDL-C levels. Conversely, BMI showed no significant association (p = 0.456).
conclusionML algorithms, particularly RF, effectively predict in-hospital mortality in AMI patients, highlighting critical predictors such as LVEF and biochemical markers. These insights offer valuable tools for enhancing clinical decision-making and improving patient outcomes.
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