Development and validation of an explainable artificial intelligence model to predict in-hospital adverse events for patients with acute myocardial infarction

In-hospital adverse events after acute myocardial infarction (AMI) can occur abruptly and adversely affect short-term prognosis. We developed and externally validated explainable machine learning models to predict a composite of in-hospital atrial fibrillation (AF), ventricular fibrillation (VF), cardiac arrest (CA), or death using routinely available admission data. We retrospectively analyzed 7,496 patients from the MIMIC-IV database as the development cohort and 2,776 patients from Xuanwu Hospital as the external validation cohort. Predictors included demographics, medical history, and admission laboratory tests. Logistic regression, random forest, extreme gradient boosting (XGBoost), multilayer perceptron, and TabNet models were evaluated using five-fold cross-validation. Performance was assessed using discrimination, classification, calibration, and decision curve analysis. Latent class analysis identified three outcome clusters with distinct event-probability profiles. XGBoost achieved the best performance for the composite outcome in the development cohort (AUROC, 0.798). Predictive performance was moderate to good for AF and in-hospital death, with AUROCs of up to 0.834 and 0.870 in the development cohort and 0.803 and 0.849 in the external validation cohort, respectively. Discrimination was lower for VF and CA. SHapley Additive exPlanations highlighted neutrophil and leukocyte counts, N-terminal pro-B-type natriuretic peptide, renal function, glucose, glycated hemoglobin A1c, and prior AF as important predictors. These explainable models may support early risk stratification and help guide monitoring intensity and timely intervention in patients with AMI.