A Nomogram for Predicting the Possibility of Peripheral Neuropathy in Patients with Type 2 Diabetes Mellitus

Abstract

Background and Purpose: Diabetic peripheral neuropathy (DPN) leads to ulceration, noninvasive amputation, and long-term disability. This study aimed to develop and validate a nomogram for forecasting the probability of DPN in type 2 diabetes mellitus patients. Methods: From February 2017 to May 2021, 778 patients with type 2 diabetes mellitus were included in this study. We confirmed the diagnosis of DPN according to the Toronto Expert Consensus. Patients were randomly divided into a training cohort (n = 519) and a validation cohort (n = 259). In the training cohort, univariate and multivariate logistic regression analyses were performed, and a simple nomogram was built using the stepwise method. The receiver operating characteristic (ROC), calibration curve, and decision curve analysis were computed in order to validate the discrimination and clinical value of the nomogram model. Results: About 65.7% and 72.2% of patients were diagnosed with DPN in the training and validation cohorts. We developed a novel nomogram to predict the probability of DPN based on the parameters of age, gender, duration of diabetes, body mass index, uric acid, hemoglobin A1c, and free triiodothyronine. The areas under the curves (AUCs) of the nomogram model were 0.763 in the training cohort and 0.755 in the validation cohort. The calibration plots revealed well-fitted accuracy between the predicted and actual probability in the training and validation cohorts. Decision curve analysis confirmed the clinical value of the nomogram. In subgroup analysis, the predictive ability of the nomogram model was strong. Conclusions: The nomogram of age, gender, duration of diabetes, body mass index, uric acid, hemoglobin A1c, and free triiodothyronine may assist clinicians with the early identification of DPN in patients with type 2 diabetes mellitus.

Development and validation of a risk nomogram model for predicting peripheral neuropathy in patients with type 2 diabetes mellitus

“…At the sama time, the area under the curve (AUC) of the column chart model designed by Wanli Zhang et al. was 0.763 in the training queue and 0.755 in the validation queue ( 11 ). Jing Yang et al.…”

“…The calibration curve further confirmed the model's precision in estimating the risk of DPN. At the sama time, the area under the curve (AUC) of the column chart model designed by Wanli Zhang et al was 0.763 in the training queue and 0.755 in the validation queue (11). Jing Yang et al found serum creatinine (Scr), hypertension, glycosylated hemoglobin A1c (HbA1c), blood urea nitrogen (BUN), body mass index (BMI), triglycerides (TG), and diabetic peripheral neuropathy (DPN) Can serve as a key factor for this prediction model.…”

Improving Effects of Peripheral Nerve Decompression Microsurgery of Lower Limbs in Patients with Diabetic Peripheral Neuropathy

“…DPN has long been considered “progressive and irreversible”. It is mainly manifested as pain, numbness, and paresthesia in the glove-sock-like distribution area at the end of the extremities, seriously influencing the patients’ quality of life, and results in a heavy social burden [ 5 , 6 , 7 ]. In recent years, it has generally been demonstrated to be the result of a combination of factors including long-term hyperglycemia caused by metabolic disorders, microcirculation disorders, and neuroischemia, and hypoxia [ 8 ].…”