ArticleCirculation. Cardiovascular quality and outcomes2025
Predicting Mortality in Patients Hospitalized With Acute Myocardial Infarction: From the National Cardiovascular Data Registry.
Article in Circulation. Cardiovascular quality and outcomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Real-world management and outcomes of myocardial infarction in a transitional country: insights from an 11-year observational study of 13,099 patients.BMC cardiovascular disorders · 2026Observational
- The impact of emergency quality improvement program on the prognosis of patients with acute ST-segment elevation myocardial infarction in Taiwan.Scientific reports · 2026Article
- Elevated Red Blood Cell Distribution Width Predicts Mortality and Major Adverse Cardiovascular Events After Acute Myocardial Infarction: A Large Propensity Score-Matched Cohort Study.Journal of clinical medicine · 2026Article
- Attenuating effects of inflammatory pathway by prolonged left ventricular unloading after myocardial infarction in male rats.PloS one · 2026Article
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15 authors.
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Abstract
backgroundIn-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.
methodsData 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.
resultsThe 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.
conclusionsThis 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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