Construction of a Nomogram Prediction Model for the Risk of Type 2 Diabetic Peripheral Neuropathy

Abstract Objective Development and validation of a nomogram to predict the risk of developing diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM).Methods A total of 706 patients with T2DM who met the criteria were included in this study. They were divided into a training group (n = 521) and a validation group (n = 185) in a ratio of 3:1. Clinical data were collected and analyzed using multivariate logistic regression to identify independent risk factors. A nomogram prediction model was then established based on the results, and its feasibility was evaluated using the validation group. The discriminative power, accuracy, and clinical utility of the predictive models were assessed using receiver operating characteristic (ROC) area under the curve (AUC), calibration curve, and decision curve analysis (DCA), respectively.Results In this study, a total of 706 patients with T2DM were found to have DPN in 414 cases, resulting in an incidence rate of 58.64%. The results of stepwise regression and multivariate logistic regression analysis revealed that age, duration of diabetes, diabetic retinopathy (DR), and body mass index (BMI) were identified as significant factors influencing the development of DPN in T2DM patients (P Conclusion This study successfully established and validated a high-precision nomogram prediction model, which can help improve the ability of early identification and screening of high-risk patients with DPN.