〔Abstract〕 Objective To investigate independent risk factors for lower extremity amputation (LEA) in hospitalized patients with diabetic foot ulcers ( DFUs) . Methods A multicenter retrospective analysis was conducted on the clinical data of 329 DFUs hospitalized patients with diabetic foot ulcers from four general hospitals across the na⁃tion. A multivariate Logistic regression model was constructed , and prediction analysis was performed using R 4. 2. 1 . The discriminative ability of the model was assessed using receiver operating characteristic curves , while calibration accuracy and clinical applicability were evaluated via calibration curves and decision curve analysis. Re⁃ sults The study revealed that patients with higher education backgrounds showed lower disease severity (Wagnergrade) (Z = - 4. 331 , P < 0. 05) . A history of amputation , pre⁃existing lower extremity vascular disease , abnormal dorsalis pedis artery pulsation , and a history of coronary heart disease were significantly associated with the severity of DFUs , resulting in higher Wagner scores (P < 0. 05) . In the amputation prognosis analysis , prolonged duration of diabetes and elevated white blood cell count were positively correlated with amputation risk ( both P < 0. 01) .Multivariable regression identified non⁃higher education , low hemoglobin levels , decreased total cholesterol , and abnormally elevated platelet counts as independent risk factors for high Wagner grades ( ≥ grade 3 ) ( all P <0. 05) . The integrated predictive model incorporating these factors demonstrated strong discriminative performance ,with an area under curve of 0. 880 (95% CI: 0. 801 - 0. 960) . The calibration curve slope approached the ideal value , and decision curve analysis confirmed the model ′s clinical net benefit within a threshold probability range of 10% - 65% . Conclusion Lower education level , poor baseline nutritional status , infection , hypercoagulability ,and underlying vascular diseases collectively constitute key factors contributing to elevated amputation risk in DFUs patients. The developed predictive model exhibits high accuracy and may assist clinicians in formulating individual⁃ized intervention strategies.