Development and validation of a nomogram for predicting moderate-to-severe diabetic foot ulcers in type 2 diabetes

Diabetic foot ulcer (DFU) has become a significant public health concern. This research aimed to develop a predictive nomogram model to assess the risk of moderate to severe DFU in patients with diabetes.

Our retrospective study included 499 hospitalized patients with Type 2 Diabetes Mellitus (T₂DM) and moderate to severe DFUs at the Second Affiliated Hospital of Fujian Medical University, from January 2021 to December 2023. Predictive factors were assessed using both univariate and multivariate logistic regression analyses, leading to the establishment of the predictive nomogram. The model's performance was evaluated through receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, and clinical impact curve.

The predictive model included several risk factors: diabetic retinopathy (DR), diabetic kidney disease (DKD), diabetic peripheral neuropathy (DPN), peripheral angiopathy (PAD), D-dimer, K-time, total cholesterol (TC), Low-Density Lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). The model demonstrated excellent discrimination, with AUC 0.977 (95% CI: 0.965-0.989) in the training cohort and 0.977 (95% CI: 0.958-0.996) in the validation cohort. Calibration results indicated strong agreement between predicted and observed outcomes. Additionally, decision curve analysis indicated that the nomogram provided clinical benefits in both the training and validation cohorts.

This nomogram, which incorporates DKD, DPN, PAD, DR, D-dimer, K-time, TC, LDL-C, and HDL-C, demonstrates strong accuracy and predictive value for assessing the risk of moderate to severe DFUs in patients with diabetes.