Development and validation of a predictive model for the differential diagnosis of diabetic nephropathy and non-diabetic renal disease in patients with type 2 diabetes mellitus
10.3760/cma.j.cn441217-20200217-00145
- VernacularTitle:2型糖尿病患者糖尿病肾病及非糖尿病性肾脏疾病鉴别诊断预测模型的开发及验证
- Author:
Lulu SUN
1
;
Jin SHANG
;
Jing XIAO
;
Zhanzheng ZHAO
Author Information
1. 郑州大学第一附属医院肾脏内科，郑州 450052
- From:
Chinese Journal of Nephrology
2020;36(11):824-833
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop and validate a predictive model for the differential diagnosis of diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) in patients with type 2 diabetes mellitus.Methods:A retrospective study with patients with type 2 diabetes who underwent renal biopsy in the First Affiliated Hospital of Zhengzhou University from February 2012 to January 2015 was conducted. The dataset was randomly split into development (70.0%) and validation (30.0%) cohorts. Baseline predictors for model development was selected by using univariable and multivariable logistic regression. The model's performance in the two cohorts, including discrimination and calibration, was evaluated by the C-statistic, calibration curve and the P value of the Hosmer-Lemeshow test. Results:Among the 931 patients with type 2 diabetes, 478 cases (51.3%) diagnosed as DN alone, 214 cases (23.0%) as NDRD alone and 239 cases (25.7%) as DN plus superimposed NDRD (MIX). Among NDRD and MIX patients, membranous nephropathy was the most common pathological type, followed by IgA nephropathy. The variables selected in the final predictive model were age, duration of diabetes, diabetic retinopathy, systolic blood pressure, hemoglobin, fasting blood glucose, glycosylated hemoglobin, cystatin C. The model performed well with good discrimination and calibration. The C-statistics were 0.913(95% CI 0.892-0.935) in the derivation cohort and 0.897(95% CI 0.876-0.919) in the validation cohort. The model had the best P value of 0.934 of the Hosmer-Lemeshow test. Conclusions:A simple predictive model with high accuracy is constructed for predicting the presence of NDRD and MIX for type 2 diabetic patients. The nomogram can be used as a decision support tool to provide a non-invasive method for differential diagnosis of DN and NDRD, which may help clinicians assess the risk-benefit ratio of kidney biopsy for type 2 diabetic patients with renal impairment.
