葡萄糖变异参数构建糖尿病周围神经病变风险预测模型的探讨.

Objective: To develop and validate a risk prediction model for severe DPN in patients with type 2 diabetes using glucose variability parameters, and to provide the evidence for the prevention and treatment of diabetic chronic complications. Methods　The medical records from 323 inpatients with T2DM who met the inclusion criteria were collected in the First Affiliated Hospital of Kunming Medical University from April 2019 to May 2020. Based on the professional sensory threshold measured, patients were divided into none-sever DPN group and severe DPN group. Lasso regression model was used to select risk factors. A risk prediction models for severe DPN was established and shown as a nomogram. ROC curve, calibration curve and decision curve analysis was used to validate the model. Results: Age, smoking, dyslipidemia, HbA1c and TIR were significant predictors of severe DPN in type 2 diabetes patients, and TIR was an independent risk factor for severe DPN. An assessing model discrimination was established by using ROC curves with AUC = 0.653 (95%CI = 0.592 - 0.715, P < 0.05). The Hosmer-Lemeshow test was used to determine the model fit with P value 0.074. Conclusion: TIR can be a significant predictor of the severe DPN in patients with T2DM, and the clinical prediction model established on the basis of TIR has the fair accuracy. It is recommended to actively intervene in the patients with a risk of severe DPN greater than 20% (score > 250) evaluated by the model. [ABSTRACT FROM AUTHOR]