ArticleIndian heart journal
Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients.
Article in Indian heart journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prognostic utility of Killip classification in acute myocardial infarction: a retrospective cohort analysis in contemporary clinical practice.European heart journal open · 2026Article
- Machine Learning-Based Prediction of Long-Term Mortality in STEMI Patients Using Clinical, Laboratory, and Inflammatory-Metabolic Indices.Journal of clinical medicine · 2026Article
- Machine learning redevelopment of GRACE, ACEF, and TIMI scores for 6-month mortality.Frontiers in artificial intelligence · 2026Article
- Comparing Machine Learning Models for Predicting Mortality after Myocardial Infarction: A Systematic Review and Meta-analysis.Archives of academic emergency medicine · 2026Review
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15 authors.
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Abstract
backgroundRisk stratification is an integral component of ST-segment-elevation myocardial infarction (STEMI) management practices. This study aimed to derive a machine learning (ML) model for risk stratification and identification of factors associated with in-hospital and 30-day mortality in patients with STEMI and compare it with traditional TIMI score.
methodsThis was a single center prospective study wherein subjects >18 years with STEMI (n = 1700) were enrolled. Patients were divided into two groups: training (n = 1360) and validation dataset (n = 340). Six ML algorithms (Extra Tree, Random Forest, Multiple Perceptron, CatBoost, Logistic Regression and XGBoost) were used to train and tune the ML model and to determine the predictors of worse outcomes using feature selection. Additionally, the performance of ML models both for in-hospital and 30-day outcomes was compared to that of TIMI score.
resultsOf the 1700 patients, 168 (9.88 %) had in-hospital mortality while 30-day mortality was reported in 210 (12.35 %) subjects. In terms of in-hospital mortality, Random Forest ML model (sensitivity: 80 %; specificity: 74 %; AUC: 80.83 %) outperformed the TIMI score (sensitivity: 70 %; specificity: 64 %; AUC:70.7 %). Similarly, Random Forest ML model (sensitivity: 81.63 %; specificity: 78.35 %; AUC: 78.29 %) had better performance as compared to TIMI score (sensitivity: 63.26 %; specificity: 63.91 %; AUC: 63.59 %) for 30-day mortality. Key predictors for worse outcomes at 30-days included mitral regurgitation on presentation, smoking, cardiogenic shock, diabetes, ventricular septal rupture, Killip class, age, female gender, low blood pressure and low ejection fraction.
conclusionsML model outperformed the traditional regression based TIMI score as a risk stratification tool in patients with STEMI.
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