Development and Validation of a Nomogram for Predicting Diabetic Foot Ulcer Risk in Patients with Type 2 Diabetes Mellitus

Xiuli Feng¹Renhao Zhao²Teng Yang³Na Wang¹Guofeng Wang^1*

• ¹Department of Endocrinology, Lianyungang Clinical College of Nanjing Medical University, Lianyungang, China
• ²Department of Endocrinology, The First People’s Hospital of Yancheng, Yancheng, China
• ³Department of Endocrinology, Postgraduate Training base of Lianyungang First People’s hospital of Jinzhou Medical University, Lianyungang, China

Objective: To identify the risk factors of diabetic foot ulcer (DFU) in patients with type 2 diabetes mellitus (T2DM) and to develop and validate a nomogram prediction model for DFU occurrence in primary care setting. Methods: We conducted a single-center retrospective study enrolling 547 T2DM patients hospitalized at The First People's Hospital of Lianyungang from January 2019 to April 2022. Patients were randomly divided (3:1) into modeling (n = 411) and validation (n = 136) cohorts, and further stratified by DFU status. Thirty-four clinical variables were extracted for analysis. LASSO regression with tenfold cross-validation identified key features, followed by multivariate logistic regression to determine independent DFU risk factors. A nomogram model was developed using R software, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, the Hosmer–Lemeshow goodness-of-fit test, and decision curve analysis (DCA).Results: Among 547 T2DM patients, 150 (27.4%) developed DFU. Multivariate analysis identified seven independent risk factors: age (odds ratio [OR] = 1.032, 95% confidence interval [CI]: 1.005–1.062, P = 0.021), white blood cell (WBC) (OR = 1.127, 95% CI: 1.006–1.270, P = 0.043), ankle-brachial index (ABI) (OR = 5.447, 95% CI: 2.186–14.340, P < 0.001), urine albumin-to-creatinine ratio (UACR) (OR = 2.049, 95% CI: 1.062–3.936, P = 0.031), family history of diabetes (OR = 3.405, 95% CI: 1.666–7.039, P < 0.001), diabetic peripheral neuropathy (DPN) (OR = 5.084, 95% CI: 2.673–9.805, P < 0.001), and albumin (ALB) (OR = 0.850, 95% CI: 0.786–0.915, P < 0.001). The developed nomogram demonstrated excellent discrimination (AUC = 0.917 and 0.956 for modeling and validation cohorts). Internal validation confirmed good model reliability (C-index = 0.917). Calibration curves showed strong agreement between predicted and observed outcomes (Hosmer–Lemeshow P = 0.649 and 0.345). DCA indicated a consistently higher net benefit across threshold probabilities of 0 to 0.8, underscoring the model’s potential clinical utility.Conclusions: The nomogram prediction model developed in this study demonstrates excellent performance and strong clinical applicability. It provides an effective tool to identify high-risk T2DM patients for DFU and guide early preventive interventions.