ArticleFrontiers in medicine2026
Development and validation of a risk score heatmap for in-hospital adverse events in older adults with acute myocardial infarction.
Article in Frontiers in medicine, 2026. 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
Background: Older adults with acute myocardial infarction (AMI) represent a particularly high-risk population for early in-hospital deterioration. Life-threatening complications-including sustained ventricular tachycardia or fibrillation (sVT/VF), cardiogenic shock (CS), and acute heart failure (AHF)-are major contributors to in-hospital adverse events (IAE), as these events may herald an increased risk of poor post-discharge prognosis. However, a simple and visually intuitive tool for early risk stratification in this population remains lacking. Methods: We conducted a retrospective cohort study of consecutive patients aged >60 years with AMI admitted between January 2019 and December 2024. The primary endpoint was a composite of in-hospital death, sVT/VF, CS, or AHF. After multiple imputation for missing data, a multi-stage feature selection strategy incorporating least absolute shrinkage and selection operator (LASSO) logistic regression and random forest analysis was applied. Results: Among 4,897 included patients, 434 (8.9%) experienced the composite endpoint. The final model incorporated 10 readily available clinical variables: age, atrial fibrillation (AF), pulse rate, systolic blood pressure (SBP), neutrophil-to-lymphocyte ratio (NLR), albumin, N-terminal pro-B-type natriuretic peptide (NT-proBNP), estimated glomerular filtration rate (eGFR), glucose, and alanine aminotransferase (ALT). The model demonstrated good discrimination [area under the receiver operating characteristic curve (AUC) 0.808, 95% confidence interval (CI) 0.786-0.830; Conclusion: We developed an internally validated 10-variable prediction model, visualized as a risk score heatmap, to estimate IAE in older adults with AMI. This intuitive tool may support early bedside risk stratification and personalized management in clinical practice.
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