American heart journal · 2026
Advancing in-hospital mortality prediction for acute myocardial infarction: An analysis from the American Heart Association Get With The Guidelines-Coronary Artery Disease Registry.
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Zhao Juan, Hong Haoyun, Fanaroff Alexander C, Zhong Sophia, Bush Kelvin N V, Granger Christopher, Huber Nicole, Jackevicius Cynthia A, Kindipan Ingrid, Li Shen, Lewis Paula, Osho Asishana, Sather Jeffrey, Sigal Stephen L, Thomas Kathie, Ni Zhao, Hall Jennifer L, Jacobs Alice K, Goyal Abhinav
Аннотация
BACKGROUND: Cardiovascular disease 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 However, existing models often rely on limited predictor sets, outdated data, and linear methods that may not reflect current clinical practice.
OBJECTIVE: To develop and validate a contemporary in-hospital mortality risk model for AMI patients, incorporating clinical, demographic, and social determinants of health, and to compare performance against the legacy ACTION Registry-GWTG model.
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. A total number of 70,302 patients admitted in 2023 served as an independent validation cohort. We incorporated 27 predictors and benchmarked against the legacy ACTION Registry-GWTG model. Subgroup and sensitivity analyses assessed model performance across sex, race/ethnicity, ST-elevation myocardial infarction status, and time period.
RESULTS: The Light Gradient Boosting Machine (LightGBM)-based GWTG-CAD model achieved the highest discrimination (area under the receiver operating characteristic curve [AUROC] 0.874, 95% confidence intervals, 0.867-0.880) and superior calibration across subgroups, outperforming the ACTION Registry-GWTG model (AUROC 0.859, 95% confidence intervals, 0.852-0.867). Comorbidities, transportation method, and community-level socioeconomic factors contributed meaningful predictive value beyond traditional predictors. The generalized linear mixed model (AUROC 0.865) provided interpretable odds ratios and calibrated probability estimates suitable for risk-adjusted benchmarking and quality improvement.
CONCLUSION: The GWTG-CAD model suite advances AMI mortality prediction through 2 complementary approaches: the LightGBM model offers superior discrimination for identifying high-risk patients across diverse subgroups, while the generalized linear mixed model provides a transparent tool for institutional benchmarking and quality improvement. Broader external validation is needed before clinical deployment.
OBJECTIVE: To develop and validate a contemporary in-hospital mortality risk model for AMI patients, incorporating clinical, demographic, and social determinants of health, and to compare performance against the legacy ACTION Registry-GWTG model.
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. A total number of 70,302 patients admitted in 2023 served as an independent validation cohort. We incorporated 27 predictors and benchmarked against the legacy ACTION Registry-GWTG model. Subgroup and sensitivity analyses assessed model performance across sex, race/ethnicity, ST-elevation myocardial infarction status, and time period.
RESULTS: The Light Gradient Boosting Machine (LightGBM)-based GWTG-CAD model achieved the highest discrimination (area under the receiver operating characteristic curve [AUROC] 0.874, 95% confidence intervals, 0.867-0.880) and superior calibration across subgroups, outperforming the ACTION Registry-GWTG model (AUROC 0.859, 95% confidence intervals, 0.852-0.867). Comorbidities, transportation method, and community-level socioeconomic factors contributed meaningful predictive value beyond traditional predictors. The generalized linear mixed model (AUROC 0.865) provided interpretable odds ratios and calibrated probability estimates suitable for risk-adjusted benchmarking and quality improvement.
CONCLUSION: The GWTG-CAD model suite advances AMI mortality prediction through 2 complementary approaches: the LightGBM model offers superior discrimination for identifying high-risk patients across diverse subgroups, while the generalized linear mixed model provides a transparent tool for institutional benchmarking and quality improvement. Broader external validation is needed before clinical deployment.