ArticleBMC medical informatics and decision making2025
Risk factor analysis and predictive nomogram development for in-hospital mortality in patients with ST-segment elevation myocardial infarction.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundIdentifying predictors of in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI) is a major concern in cardiology. The aim of this study was to identify risk factors and develop a nomogram to predict in-hospital mortality in STEMI patients. MATERIALS AND
methodsThis single-center study was a retrospective cohort analysis of all STEMI patients consecutively referred to Farshchian Hospital (Hamadan Province-Iran) from April 2021 to August 2024. Four different feature selection methods were used to select common important variables in the prediction model: Boruta, Recursive Feature Elimination (RFE), Random Forest (RF) and LASSO. The uneven distribution of the different classes 2,356 (91.7%) alive and 212 (8.3%) dead) was dealt with using the SMOTE method. After splitting the data into a training (70%) and a test (30%) dataset, a multiple logistic regression model was formulated using the significant variables identified. A nomogram predicting in-hospital mortality was then constructed and validated.
resultsThe findings indicate that age (OR = 1.05: 95% CI: 1.03–1.06), gender (female OR = 1.71: 95% CI: 1.22–2.41), length of stay (OR = 0.89: 95% CI: 0.83–0.96), blood urea nitrogen level (OR = 1.02: 95% CI: 1.00-0.03), white blood cell count (OR = 1.07: 95% CI: 1.03–1.1), Creatinine (OR = 1.74: 95% CI: 1.36–2.22), fasting blood glucose (OR = 1.007: 95% CI: 1.005–1.009), uric acid (OR = 1.07: 95% CI: 1.02–1.12), potassium (OR = 1.2: 95% CI: 0.95–1.52) and systolic blood pressure (OR = 0.98: 95% CI: 0.97–0.99) are pivotal factor in predicting in-hospital mortality in STEMI patients. The predictive model demonstrated high accuracy (84%) and excellent discriminatory ability with an AUC of 0.91. The calibration plot demonstrated the model’s strong discriminatory performance in distinguishing between the two classes.
conclusionsThe development of a nomogram for reliably predicting in-hospital mortality in STEMI patients provides clinicians with a practical visual aid for identifying high-risk patients. By enabling tailored care strategies, this tool improves therapeutic precision and ultimately leads to better clinical outcomes. CLINICAL TRIAL NUMBER: Not applicable.
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