Development of predictive nomograms clinical use to quantify the risk of diabetic foot in patients with type 2 diabetes mellitus.

Objective: The aim of the study was to explore the risk factors for diabetic foot disease in patients with type 2 diabetes mellitus and to establish and verify the nomogram model of DF risk in patients with T2DM. Methods: The clinical data of 705 patients with type 2 diabetes who were hospitalized in our hospital from January 2015 to December 2022 were analyzed retrospectively. According to random sampling, the patients were divided into two groups: the training set (DF = 84; simple T2DM = 410) and the verification set (DF = 41; simple T2DM = 170). Univariate and multivariate logistic regression analysis was used to screen the independent risk factors for DF in patients with T2DM in the training set. According to the independent risk factors, the nomogram risk prediction model is established and verified. Results: Logistic regression analysis showed age (OR = 1.093, 95% CI 1.062-1.124, P <0.001), smoking history (OR = 3.309, 95% CI 1.849-5.924, P <0.001), glycosylated hemoglobin (OR = 1.328, 95% CI 1.173-1.502, P <0.001), leukocyte (OR = 1.203, 95% CI 1.076-1.345, and LDL-C (OR = 2.002, 95% CI 1.463-2.740), P <0.001) was independent risk factors for T2DM complicated with DF. The area of the nomogram model based on the above indexes under the ROC curve of the training set and the verification set is 0.827 and 0.808, respectively; the correction curve shows that the model has good accuracy; and the DCA results show that when the risk threshold is between 0.10-0.85 (training set) and 0.10-0.75 (verification set), the clinical practical value of the model is higher. Conclusion: The nomogram model constructed in this study is of high value in predicting the risk of DF in patients with T2DM and is of reference value for clinicians to identify people at high risk of DF and provide them with early diagnosis and individual prevention.