Interpretable machine learning for predicting major amputation risk in hospitalized diabetic foot ulcer patients: a single-center study with temporal external validation.

Background: Diabetic foot ulcers are a leading cause of non-traumatic lower-limb amputation, but early identification of inpatients at high risk of major amputation remains challenging. Methods: We retrospectively reviewed consecutive admissions for diabetic foot ulcers at a single center, developing models in a 2019-2020 cohort and temporally validating them in a later 2024 cohort. The outcome was in-hospital major lower-extremity amputation above the ankle. Candidate predictors were routinely available admission variables within 24 hours, including comorbidities, bedside limb/ulcer assessment, and standard laboratory tests. We compared logistic regression, elastic net, random forest, and extreme gradient boosting models and used Shapley additive explanations to provide patient-level interpretability. Results: The random forest model showed the best overall discrimination, with an area under the receiver operating characteristic curve of 0.977 in internal testing and 0.984 in temporal validation, and acceptable calibration. The most influential predictors reflected limb perfusion and infection severity and included perfusion grade, ankle-brachial index, maintenance dialysis, white blood cell count, C-reactive protein, and prior minor amputation. Conclusions: An explainable admission-data model can support early inpatient risk stratification for major amputation in diabetic foot ulcer patients and may help prioritize timely multidisciplinary care.