Open Access2型糖尿病患者中糖尿病肾病和非糖尿病肾病鉴别诊断评分模型的建立与验证
Author(s) -
Li Li,
Yang Yuan,
Zhu Xuejing,
Xiong Xiaofen,
Zeng Lingfeng,
Xiong Shan,
Jiang Na,
Li Chenrui,
Yuan Shuguang,
Xu Hui,
Liu Fuyou,
Sun Lin
Publication year - 2020
Publication title -
journal of diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.949
H-Index - 43
eISSN - 1753-0407
pISSN - 1753-0393
DOI - 10.1111/1753-0407.12994
Subject(s) - medicine , diabetic nephropathy , logistic regression , type 2 diabetes , diabetes mellitus , cohort , gastroenterology , kidney , endocrinology
Background We aim to design a scoring model for differential diagnosis between diabetic nephropathy (DN) and nondiabetic renal disease (NDRD) in type 2 diabetic patients through a combination of clinical variables. Methods A total of 170 patients with type 2 diabetes who underwent kidney biopsies were included and divided into three groups according to pathological findings: DN group (n = 46), MIX group (DN + NDRD, n = 54), NDRD group (n = 70). Clinical characteristics and laboratory data were collected and compared among groups. Variables with a significant statistical difference between DN and NDRD patients were analyzed by logistic regression to predict the presence of NDRD; then a scoring model was established based on the regression coefficient and further validated in an independent cohort of 67 patients prospectively. Results On biopsy, 72.9% of patients had NDRD, and the most common pathological type was membranous nephropathy. The established scoring model for predicting NDRD included five predictors: age, systolic blood pressure, hemoglobin, duration of diabetes, and absence of diabetic retinopathy. The model demonstrated good discrimination and calibration (area under curve [AUC] 0.863, 95% CI, 0.800‐0.925; Hosmer‐Lemeshow [H‐L] P = .062). Furthermore, high prediction accuracy (AUC = 0.900; 95% CI, 0.815‐0.985) in the validation cohort proved the stability of the model. Conclusions We present a simple, robust scoring model for predicting the presence of NDRD with high accuracy (0.85) for the first time. This decision support tool provides a noninvasive 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.