Retrospective cohort study assesses diagnostic accuracy for Wagner Grade ≥2 DFUs in hospitalized type 2 diabetes patients.

BackgroundDiabetic foot ulcers (DFUs) represent a severe and prevalent complication of diabetes, contributing to substantial disability and elevated mortality. This study aimed to develop and validate a diagnostic nomogram for Wagner Grade ≥2 DFUs in hospitalized patients with type 2 diabetes (T2DM).MethodsThis retrospective cohort study included 510 hospitalized patients with T2DM treated at the Second Affiliated Hospital of Fujian Medical University between January 2023 and December 2025, of whom 248 had Wagner Grade ≥2 DFUs. Patients were randomly divided into a training set (n=357) and an internal validation set (n=153) in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors and construct a diagnostic nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves. External validation was performed in an independent cohort of 154 patients from Quanzhou Southeast Hospital, including 86 with Wagner Grade ≥2 DFUs.ResultsThe nomogram incorporated four independent predictors: Angle α, K time, platelet count (PLT), and lymphocyte count. The model exhibited excellent discrimination in the training set (area under the curve [AUC] = 0.940, 95% confidence interval [CI]: 0.916–0.965) and internal validation set (AUC = 0.914, 95% CI: 0.870–0.956), with modest discrimination in the external validation set (AUC = 0.690, 95% CI: 0.604–0.775). Calibration curves demonstrated strong concordance between predicted and observed probabilities. DCA and clinical impact curves confirmed substantial clinical utility across all cohorts.ConclusionThis nomogram, integrating thromboelastography parameters and hematological indicators, provides a practical tool for identifying the presence of Wagner Grade ≥2 DFUs in hospitalized patients with T2DM, supporting early risk stratification and informed clinical decision-making.