{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T08:35:52Z","timestamp":1783586152668,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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\u2014including infection biomarkers, vascular parameters, and nutritional indices\u2014were 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 &lt; 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\u2013Lemeshow \u03c72 = 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 in internal validation, confirming its predictive robustness and potential for clinical application. Conclusions: C-reactive protein and Wagner grade are key determinants of amputation risk in diabetic foot ulcer patients. The support vector machine-based prediction model exhibits strong accuracy, clinical interpretability, and personalized interventions.<\/jats:p>","DOI":"10.3390\/diagnostics15243142","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T14:22:40Z","timestamp":1765376560000},"page":"3142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Machine-Learning-Based Clinical Decision Model for Predicting Amputation Risk in Patients with Diabetic Foot Ulcers: Diagnostic Performance and Practical Implications"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1009-9529","authenticated-orcid":false,"given":"Lei","family":"Gao","sequence":"first","affiliation":[{"name":"Orthopedic Department, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Orthopedic Department, Beijing Shijingshan Hospital, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyang","family":"Han","sequence":"additional","affiliation":[{"name":"Orthopedic Department, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7201-8688","authenticated-orcid":false,"given":"Jiangning","family":"Wang","sequence":"additional","affiliation":[{"name":"Orthopedic Department, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.jvs.2025.03.198","article-title":"Predicting mortality risk following major lower extremity amputation using machine learning","volume":"82","author":"Li","year":"2025","journal-title":"J. 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