Development and internal validation of a risk model for hyperuricemia in diabetic kidney disease patients - Huang, Guoqing; Li, Mingcai; Mao, Yushan; Li, Yan (Frontiers in Public Health, 2022)
Purpose: This research aimed to identify independent risk factors for hyperuricemia (HUA) in diabetic nephropathy (DN) patients and develop a HUA risk model based on a retrospective study in Ningbo, China. Patients and methods: Six hundred ten DN patients attending the two hospitals between January 2019 and December 2020 were enrolled in this research and randomized to the training and validation cohorts based on the corresponding ratio (7:3). Independent risk factors associated with HUA were identified by multivariable logistic regression analysis. The characteristic variables of the risk model were screened out by the LASSO regression model to construct the risk model in the training cohort. The C-index and receiver operating characteristic (ROC) curve, calibration plot and Hosmer-Lemeshow test, and decision curve analysis (DCA) were performed to evaluate the risk model's discriminatory power, calibration, and clinical applicability. Results: Body mass index (BMI), HbA1c, estimated glomerular filtration rate (eGFR), and hyperlipidemia were identified as independent risk factors for HUA patients in the DN population. The characteristic variables (gender, family history of T2DM, drinking history, BMI, and hyperlipidemia) were screened out by LASSO regression analysis and included as predictors in the HUA risk prediction model. In the training cohort, the HUA risk model showed good discriminatory power with a C-index of 0.761 (95% CI: 0.712-0.810) and excellent calibration (Hosmer-Lemeshow test, P > 0.05), and the results of the DCA showed that the prediction model could be beneficial for patients when the threshold probability was 9%-79%. The risk prediction model was also well-validated in the validation cohort with a C-index of 0.843 (95% CI: 0.780-0.906) and passed the Hosmer-Lemeshow test (P > 0.05), while the threshold probability of DCA was 7%-100%. Conclusion: The development of risk models contributes to the early identification and prevention of HUA in the DN population, which is vital for preventing and reducing adverse prognostic events in DN.