Publication date: Jan 13, 2025

In-hospital mortality risk prediction is an important tool for benchmarking quality and patient prognostication. Given changes in patient characteristics and treatments over time, a contemporary risk model for patients with acute myocardial infarction (MI) is needed. Data from 313 825 acute MI hospitalizations between January 2019 and December 2020 for adults aged ≥18 years at 784 sites in the National Cardiovascular Data Registry Chest Pain-MI Registry were used to develop a risk-standardized model to predict in-hospital mortality. The sample was randomly divided into 70% development (n=220 014) and 30% validation (n=93 811) samples, and 23 separate registry-based patient characteristics at presentation were considered for model inclusion using stepwise logistic regression with 1000 bootstrapped samples. A simplified risk score was also developed for individual risk stratification. The mean age of the study cohort was 65. 3 (SD 13. 1) years, and 33. 6% were women. The overall in-hospital mortality rate was 5. 0% (n=15 822 deaths). The final model included 14 variables, with out-of-hospital cardiac arrest, cardiogenic shock, and ST-segment elevation MI as the strongest independent predictors of mortality. The model also included age, comorbidities (dyslipidemia, diabetes, prior percutaneous coronary intervention, cerebrovascular disease, and peripheral artery disease), heart failure on admission, heart rate, systolic blood pressure, glomerular filtration rate, and hemoglobin. The model demonstrated excellent discrimination (C-statistic, 0. 868 [95% CI 0. 865-0. 871]) and good calibration, with similar performance across subgroups based on MI type, periods before and during the COVID-19 pandemic, and hospital volume. The simplified risk score included values from 0 to 25, with mortality risk ranging from 0. 3% with a score of 0 to 1 up to 49. 4% with a score >11. This contemporary risk model accurately predicts in-hospital mortality for patients with acute MI and can be used for risk standardization across hospitals and at the bedside for patient prognostication.

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