Advancing In-Hospital Mortality Prediction for Acute Myocardial Infarction: an analysis from the American Heart Association Get-With-the-Guidelines Coronary Artery Disease Registry

Abstract Background Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with acute myocardial infarction (AMI) contributing to over 100,000 deaths annually in the United States. Accurate risk stratification for in-hospital mortality is essential for guiding clinical decisions, improving outcomes, and optimizing hospital resources. However, existing models often rely on limited predictor sets, outdated data, and linear methods that may not reflect current clinical practice or complex interactions. Objective To develop and validate a contemporary in-hospital mortality risk model for AMI patients using modern statistical and machine learning approaches, incorporating clinical, demographic, and social determinants of health. Methods We utilized data from the American Heart Association (AHA) Get with The Guidelines®– Coronary Artery Disease (GWTG-CAD) Registry. Patients with AMI admitted between October 1, 2019, and December 31, 2022 (201,191 patients from 605 hospitals) were used to develop the in-hospital mortality prediction model. An independent validation cohort included 70,302 patients admitted in 2023. We incorporated 27 predictors, including demographics, comorbidities, vital signs, laboratory values, and social determinants of health. We assessed the models including a generalized linear mixed model (GLMM) and machine learning algorithms. We benchmarked against the legacy ACTION Registry–GWTG model. Subgroup and sensitivity analyses assessed model performance across sex, race/ethnicity, STEMI status, and time period. Results The LightGBM-based GWTG-CAD model achieved the highest discrimination (AUROC 0.874, 95% CI: 0.867–0.880) and demonstrated excellent calibration across all subgroups. Both GLMM and LightGBM outperformed the ACTION Registry–GWTG model, with LightGBM showing the most consistent performance in the 2023 validation cohort. Beyond traditional predictors, the inclusion of comorbidities, transportation methods, and community-level socioeconomic factors added predictive value. Conclusion The new GWTG-CAD risk model incorporates clinical, pre-hospital, and social factor predictors, along with machine learning methods, to enhance prediction of mortality in contemporary AMI patients.