Key result
10-variable risk score predicts major in-hospital events in older AMI patients with AUC ~0.81.
Why the study?
Older adults with AMI are at high risk for early in-hospital deterioration, but a simple and visually intuitive tool for early risk stratification remains lacking.
Cohort (n=4,897)
No
Effect estimate: AUC 0.808 (95% CI 0.786-0.830)
p-value: p=<0.001
A novel, internally validated 10-variable risk score heatmap provides good discrimination (AUC 0.808) for predicting early in-hospital adverse events in older adults presenting with acute myocardial infarction.
Warrants intensified monitoring for decompensation in at-risk MI patients; confirms event rate in large RCT and extends evidence for predictive modeling.
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; p < 0.001] and satisfactory calibration. The risk score heatmap allows intuitive estimation of individual risk based on cumulative point scores. 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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Lian et al. (2026) conducted a cohort in Acute myocardial infarction (n=4,897). 10-variable risk score heatmap was evaluated on Composite of in-hospital death, sVT/VF, CS, or AHF (AUC 0.808, 95% CI 0.786-0.830, p=<0.001). A 10-variable risk score heatmap demonstrated good discrimination (AUC 0.808) for predicting a composite of in-hospital adverse events in older adults with acute myocardial infarction.
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