Development and validation of a machine learning-based model for predicting amputation risk in patients with lower extremity arterial disease and diabetes

Background Patients with lower extremity arterial disease (LEAD) complicated by diabetes face substantial amputation risk. Pre-amputation death acts as a critical competing event that distorts risk estimation. Validated personalized prediction tools for this population remain scarce and hinder precise clinical interventions. This study aimed to develop and validate two complementary interpretable models for 12-month amputation risk in patients with LEAD and diabetes to support clinical decision-making. Methods This retrospective multicenter study enrolled 1,919 patients with LEAD and diabetes from four Chinese hospitals (January 2018-September 2024), with follow-up ending in September 2025. The primary outcome was 12-month amputation (major or minor). Prior to model construction, candidate predictors were screened via LASSO regression, random forest classifier and Boruta algorithm. Ten machine learning algorithms were then trained and evaluated by discrimination and calibration metrics. SHapley Additive Explanations (SHAP) was applied for interpretability. A Fine-Gray competing risk regression model was built using the same screened predictor set. Results Patients were split into training ( n = 967), internal validation ( n = 414), and external validation ( n = 538) cohorts. The 12-month amputation rate was 14.0%. Median follow-up was 12 months; mean age was 72.7 ± 9.5 years and 65.7% were male. The optimal GraBoost model achieved internal validation AUC = 0.860 and external validation AUC = 0.828, incorporating 14 clinical predictors, including: Rutherford classification, financial burden, age, Barthel Index, diabetic foot, statin, fibrinogen (FIB), neutrophil (NEUT), residence, prealbumin (PALB), C-reactive protein (CRP), activated partial thromboplastin time (APTT), body mass index (BMI) classification and wound. SHAP revealed higher grade Rutherford classification, lower Barthel Index, younger age, wounds, diabetic foot, and elevated FIB, APTT, CRP, NEUT correlated with higher amputation risk. Urban residence, normal BMI classification, lower financial burden, statin use and higher PALB were protective. Shared predictors showed consistent directional effects across GraBoost and Fine-Gray models. A dual-model web-based calculator was constructed for risk assessment. Conclusion We developed and validated complementary interpretable GraBoost machine learning and Fine-Gray competing risk models for 12-month amputation risk. The dual-model web calculator may help clinicians identify high-risk patients and guide clinical management.