Work

Explainable Machine Learning for Predicting Major Adverse Renal and Cardiovascular Events in Acute Myocardial Infarction With Chronic Kidney Disease: Algorithm Development and Validation

Year: 2026

Type: article

Abstract: Background: Major adverse renal and cardiovascular events (MARCE) have been proposed as a comprehensive end point reflecting both cardiac and renal outcomes in patients with acute myocardial infarction (AMI) and chronic kidney disease (CKD). However, existing prediction tools lack accuracy and generalizability for this high-risk population. Objective: This study aimed to develop and externally validate explainable machine learning models and a nomogram for predicting in-hospital MARCE among patients with AMI and CKD. Methods: We enrolled patients with AMI and CKD who underwent emergency treatment at the First Affiliated Hospital of Wenzhou Medical University as the discovery cohort and those at Wenzhou TCM (traditional Chinese medicine) Hospital of Zhejiang Chinese Medical University as the external validation cohort. Clinical features, including demographic details, comorbidities, laboratory results, echocardiography measurements, procedural details, and medication parameters, were collected for all participants. Important variables were selected using least absolute shrinkage and selection operator regression in the training set. We developed extreme gradient boosting (XGBoost), random forest, decision tree, and logistic regression (LR) models and evaluated their discrimination, calibration, and classification metrics in the hold-out test set and external cohort. Results: In the training set, we identified 6 important variables (Killip class, lactate, estimated glomerular filtration rate, use of diuretics, use of beta-blockers, and use of renin-angiotensin system inhibitors). In the test set, model discrimination was comparable across XGBoost (area under the receiver operating characteristic curve [AUC] 0.882, 95% CI 0.816-0.947), LR (AUC 0.875, 95% CI 0.799-0.950), and random forest (AUC 0.870, 95% CI 0.800-0.940), while decision tree showed lower performance (AUC 0.814, 95% CI 0.720-0.886). Shapley additive explanations (SHAP) values illustrated both global and individual feature contributions in the XGBoost model. A nomogram based on LR achieved an AUC of 0.873 (95% CI 0.714-0.926) in external validation. Conclusions: Our study identified 6 important predictors and provided a series of prediction models to enhance risk stratification of MARCE for patients with AMI and CKD. These models might aid in stratifying risk and choosing appropriate therapeutic strategies in the management of patients with AMI and CKD.

Source: JMIR Medical Informatics

DOI: 10.2196/96654