A Machine-Learning-Based Clinical Decision Model for Predicting Amputation Risk in Patients with Diabetic Foot Ulcers: Diagnostic Performance and Practical Implications

作者：Lei Gao, Zixuan Liu, Siyang Han, J.R. Wang · 发表于：Diagnostics · 年份：2025 · DOI：10.3390/diagnostics15243142 · 被引用次数：2 · 研究领域：Diabetic Foot Ulcer Assessment and Management、Peripheral Artery Disease Management、Wound Healing and Treatments

Objective: To establish a reliable machine-learning-based model for predicting the risk of lower limb amputation in patients with diabetic foot ulcers and to provide quantitative evidence for clinical decision-making and individualized prevention strategies. Methods: This retrospective study analyzed data from 149 hospitalized diabetic foot ulcer patients treated at Beijing Shijitan Hospital between January 2019 and December 2022. Patients were divided into amputation and non-amputation groups according to clinical outcomes. Candidate predictors—including infection biomarkers, vascular parameters, and nutritional indices—were first screened using the least absolute shrinkage and selection operator algorithm. Subsequently, a support vector machine model was trained and internally validated via five-fold cross-validation to estimate amputation risk. Model performance was evaluated by discrimination, calibration, and clinical utility analysis. Results: Among all enrolled variables, C-reactive protein and Wagner grade were identified as independent predictors of amputation (p < 0.05). The optimized support vector machine model achieved excellent discrimination, with an area under the Receiver Operating Characteristic curve of 0.89, and demonstrated a moderate level of calibration (Hosmer–Lemeshow χ2 = 19.614, p = 0.012). Decision curve analysis showed a net clinical benefit of 0.351 when the threshold probability was set at 0.30. The model correctly classified 82.4% of cases i...