Development and temporal validation of an interpretable XGBoost model for predicting cardiovascular death in patients with diabetic foot.

Abstract

Patients with diabetic foot (DF) have a high risk of cardiovascular (CV) death, yet dedicated risk-prediction tools for this population are lacking. We developed and temporally validated an interpretable machine learning (ML) model for predicting CV death in patients with DF. This single-center retrospective cohort study included 2,835 patients admitted between February 2017 and May 2025. The development cohort comprised 2,325 patients, including 748 CV deaths, and was divided into training, internal validation, and held-out test sets; an independent temporal validation cohort included 510 patients, including 220 CV deaths. Nine supervised ML algorithms were compared using the area under the receiver operating characteristic curve (AUC). Extreme gradient boosting (XGBoost) showed the best overall performance. The optimal model, incorporating demographic and diabetes-related characteristics, routine laboratory parameters, and DF-specific features, achieved AUCs of 0.829 (95% confidence interval [CI]: 0.752-0.905) in internal validation, 0.844 (95% CI: 0.806-0.881) in the held-out test set, and 0.828 (95% CI: 0.789-0.868) in temporal validation. The model demonstrated good calibration and favorable net benefit on decision curve analysis. SHapley Additive exPlanations (SHAP) identified age, serum creatinine, glycated hemoglobin, triglycerides, and body mass index as the most influential predictors of increased model-predicted risk. This interpretable XGBoost model may support early identification and individualized risk stratification of patients with DF at high risk of CV death; however, prospective multicenter validation is required before clinical implementation.