Predicting Mortality in Patients Hospitalized With Acute Myocardial Infarction: From the National Cardiovascular Data Registry.

Summary

Using 313,825 AMI hospitalizations across 784 U.S. sites, the authors developed and validated a 14-variable in-hospital mortality model (C-statistic 0.868) and a 0–25 bedside score. Out-of-hospital cardiac arrest, cardiogenic shock, and STEMI were the strongest predictors, and performance was consistent across subgroups and pandemic periods.

Key Findings

• A 14-variable model achieved excellent discrimination (C-statistic 0.868) for AMI in-hospital mortality with good calibration.
• Strongest predictors were out-of-hospital cardiac arrest, cardiogenic shock, and STEMI; a 0–25 point bedside score mapped to 0.3%–49.4% mortality risk.
• Model performance was stable across MI type, hospital volume, and pre-/during COVID-19 periods.

Clinical Implications

Supports hospital benchmarking, triage, and shared decision-making; the simplified score facilitates quick risk assessment and resource allocation.

Limitations

• Registry-based observational data; potential residual confounding and coding variability
• In-hospital outcome only; does not capture post-discharge events

Future Directions

Integration into EHRs for real-time risk dashboards; evaluation of impact on care pathways and outcomes; extension to post-discharge risk.