Comparative analysis of membranous and other nephropathy subtypes and establishment of a diagnostic model.
10.1007/s11684-018-0620-5
- Author:
Hanyu ZHU
1
;
Bo FU
1
;
Yong WANG
2
;
Jing GAO
3
;
Qiuxia HAN
4
;
Wenjia GENG
1
;
Xiaoli YANG
1
;
Guangyan CAI
1
;
Xiangmei CHEN
1
;
Dong ZHANG
5
Author Information
1. Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing Key Laboratory of Kidney Disease, Beijing, 100853, China.
2. Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing Key Laboratory of Kidney Disease, Beijing, 100853, China. wangyong301@263.net.
3. Department of Clinical Biochemistry, Chinese PLA General Hospital, Beijing, 100853, China.
4. Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
5. Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing Key Laboratory of Kidney Disease, Beijing, 100853, China. zhangdong301@126.com.
- Publication Type:Journal Article
- Keywords:
diagnosis;
membranous nephropathy;
model;
multiparameter analysis
- From:
Frontiers of Medicine
2019;13(5):618-625
- CountryChina
- Language:English
-
Abstract:
This study aimed to compare clinical features between membranous nephropathy (MN) and nonmembranous nephropathy (non-MN), to explore the clinically differential diagnosis of these two types, and to establish a diagnostic model of MN. After renal biopsy was obtained, 798 patients were divided into two groups based on their examination results: primary MN group (n = 248) and non-MN group (n = 550). Their data were statistically analyzed. Logistic regression analysis indicated that anti-PLA2R antibodies, IgG, and Cr were independently correlated with MN, and these three parameters were then used to establish the MN diagnostic model. A receiver operating characteristic (ROC) curve confirmed that our diagnostic model could distinguish between patients with and without MN, and their corresponding sensitivity, specificity, and AUC were 79.9%, 89.4%, and 0.917, respectively. The cutoff value for this combination in MN diagnosis was 0.34. The established diagnostic model that combined multiple factors shows a potential for broad clinical applications in differentiating primary MN from other kidney diseases and provides reliable evidence supporting the feasibility of noninvasive diagnosis of kidney diseases.
