Zhongguo quanke yixue (Sep 2023)
Development and Validation of a Risk Prediction Model for the Progression from Microalbuminuria to Macroalbuminuria in Patients with Type 2 Diabetes Mellitus
Abstract
Background The incidence of diabetic kidney disease (DKD) and the proportion of its related end-stage renal disease in dialysis patients in China are increasing. So it is urgent to take measures to prevent and control DKD. Intensified multifactorial interventions may prevent or delay the progression of DKD. Therefore, developing a personalized risk prediction model can effectively delay or even prevent the progression of DKD and be useful for the prevention and treatment of DKD. Objective The purpose of this study was to develop and validate a nomogram for the risk prediction of the progression from microalbuminuria (MAU) to macroalbuminuria (CAU) in type 2 diabetes mellitus (T2DM) patients. Methods A total of 1 263 T2DM patients with albuminuria who were hospitalized in Department of Endocrinology, the First Affiliated Hospital of Air Force Medical University from October 2016 to March 2020 were retrospectively recruited and divided into a development cohort of 906 cases and a validation cohort of 357 cases, according to the admission time. LASSO regression was used to screen the optimized variables measured at baseline for CAU. A Nomogram was constructed based on selected predictive factors identified by the multivariate logistic regression model of the development sub-cohort. The receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow (H-L) test were employed to assess the calibration and discrimination of the model. Decision curve analysis (DCA) was performed to evaluate the net clinical benefit of the Nomogram. Results The diabetes duration, systolic blood pressure (SBP), glycosylated hemoglobin A1c (HbA1c), low-density lipoprotein cholesterol (LDL-C), cystatin C (Cys-C), estimated glomerular filtration rate (eGFR), and diabetic retinopathy (DR) were screened as predictive factors for progression from MAU to CAU by LASSO penalty regression. Multivariable Logistic regression analysis using these factors indicated that seven of those potential predictors were present in the final model, diabetes duration≥10 years, SBP≥140 mmHg, HbA1c≥7.0 mmol/L, LDL-C≥1.8 mmol/L, Cys-C>1.09 mg/L, and DR were risk factors for the progression from MAU to CAU in T2DM patients (P<0.05), while eGFR showed no statistically significant association with the progression in stratified analysis (P>0.05). External and internal validations of the nomogram indicated a good predictive performance. The AUC of the model was 0.814〔95%CI (0.782, 0.846) 〕 in the development cohort, and was 0.768〔95%CI (0.713, 0.823) 〕 in the validation cohort. The model was well fit according to the calibration curve and the H-L goodness of fit test (internal validation: P=0.065; external validation: P=0.451). DCA curve showed that the Nomogram's net benefit was higher than both extreme curves when the threshold probability set between 0.08 and 0.74 in the development cohort, and between 0.14 and 0.70 in the external validation cohort, suggesting potential clinical benefits provided by this Nomogram. Conclusion This study finally constructed a prediction model with seven indicators containing diabetes duration, SBP, HbA1c, LDL-C, Cys-C, eGFR, and DR, and will be a useful clinical predictive tool for the risk of progression from MAU to CAU in T2DM patients.
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