Machine learning prediction of 30-day mortality in coronary artery disease: a retrospective multicenter study using electronic health records.

BACKGROUND: Coronary artery disease (CAD) is the leading cause of death globally and a major contributor to hospital readmission. This study aimed to predict 30-day mortality in patients hospitalized with acute and chronic CAD using a structured machine learning approach with data from multiple centers. METHODS: We conducted a retrospective cohort study using patient data from the Taipei Medical University Clinical Research Database (TMUCRD). Multiple machine learning algorithms were employed to develop predictive models for 30-day mortality. Model performance was evaluated using a stratified fivefold cross-validation approach. Key performance metrics included the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 score. RESULTS: A total of 23,267 patients (mean age 64.9 years) were included, with 1215 deaths overall (5.2%): 570 (3.7%) in the internal cohort (n = 15,510) and 645 (8.3%) in the external validation cohort (n = 7757, Shuang Ho Hospital). XGBoost achieved the best performance for the overall and acute CAD cohorts (AUROC 0.845 and 0.820, respectively), while logistic regression performed best for chronic CAD (AUROC 0.766). Key predictive features included the Charlson Comorbidity Index, hemoglobin level, emergency room admission status, age, and creatinine level. CONCLUSION: The use of a structured machine learning approach to predict 30-day mortality in patients with acute and chronic CAD demonstrated promising discriminative performance, providing valuable insights that could enhance personalized care and inform clinical decisions.