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A diagnostic model for minimal change disease based on biological parameters

BACKGROUND: Minimal change disease (MCD) is a kind of nephrotic syndrome (NS). In this study, we aimed to establish a mathematical diagnostic model based on biological parameters to classify MCD. METHODS: A total of 798 NS patients were divided into MCD group and control group. The comparison of bio...

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Detalles Bibliográficos
Autores principales: Zhu, Hanyu, Han, Qiuxia, Zhang, Dong, Wang, Yong, Gao, Jing, Geng, Wenjia, Yang, Xiaoli, Chen, Xiangmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5768169/
https://www.ncbi.nlm.nih.gov/pubmed/29340242
http://dx.doi.org/10.7717/peerj.4237
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author Zhu, Hanyu
Han, Qiuxia
Zhang, Dong
Wang, Yong
Gao, Jing
Geng, Wenjia
Yang, Xiaoli
Chen, Xiangmei
author_facet Zhu, Hanyu
Han, Qiuxia
Zhang, Dong
Wang, Yong
Gao, Jing
Geng, Wenjia
Yang, Xiaoli
Chen, Xiangmei
author_sort Zhu, Hanyu
collection PubMed
description BACKGROUND: Minimal change disease (MCD) is a kind of nephrotic syndrome (NS). In this study, we aimed to establish a mathematical diagnostic model based on biological parameters to classify MCD. METHODS: A total of 798 NS patients were divided into MCD group and control group. The comparison of biological indicators between two groups were performed with t-tests. Logistic regression was used to establish the diagnostic model, and the diagnostic value of the model was estimated using receiver operating characteristic (ROC) analysis. RESULTS: Thirteen indicators including Anti-phospholipase A2 receptor (anti-PLA2R) (P = 0.000), Total protein (TP) (P = 0.000), Albumin (ALB) (P = 0.000), Direct bilirubin (DB) (P = 0.002), Creatinine (Cr) (P = 0.000), Total cholesterol (CH) (P = 0.000), Lactate dehydrogenase (LDH) (P = 0.007), High density lipoprotein cholesterol (HDL) (P = 0.000), Low density lipoprotein cholesterol (LDL) (P = 0.000), Thrombin time (TT) (P = 0.000), Plasma fibrinogen (FIB) (P = 0.000), Immunoglobulin A (IgA) (P = 0.008) and Complement 3 (C3) (P = 0.019) were significantly correlated with MCD. Furthermore, the area under ROC curves of CH, HDL, LDL, TT and FIB were more than 0.70. Logistic analysis demonstrated that CH and TT were risk factors for MCD. According to the ROC of “CH+TT”, the AUC was 0.827, with the sensitivity of 83.0% and the specificity of 69.8% (P = 0.000). CONCLUSION: The established diagnostic model with CH and TT could be used for classified diagnosis of MCD.
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spelling pubmed-57681692018-01-16 A diagnostic model for minimal change disease based on biological parameters Zhu, Hanyu Han, Qiuxia Zhang, Dong Wang, Yong Gao, Jing Geng, Wenjia Yang, Xiaoli Chen, Xiangmei PeerJ Bioinformatics BACKGROUND: Minimal change disease (MCD) is a kind of nephrotic syndrome (NS). In this study, we aimed to establish a mathematical diagnostic model based on biological parameters to classify MCD. METHODS: A total of 798 NS patients were divided into MCD group and control group. The comparison of biological indicators between two groups were performed with t-tests. Logistic regression was used to establish the diagnostic model, and the diagnostic value of the model was estimated using receiver operating characteristic (ROC) analysis. RESULTS: Thirteen indicators including Anti-phospholipase A2 receptor (anti-PLA2R) (P = 0.000), Total protein (TP) (P = 0.000), Albumin (ALB) (P = 0.000), Direct bilirubin (DB) (P = 0.002), Creatinine (Cr) (P = 0.000), Total cholesterol (CH) (P = 0.000), Lactate dehydrogenase (LDH) (P = 0.007), High density lipoprotein cholesterol (HDL) (P = 0.000), Low density lipoprotein cholesterol (LDL) (P = 0.000), Thrombin time (TT) (P = 0.000), Plasma fibrinogen (FIB) (P = 0.000), Immunoglobulin A (IgA) (P = 0.008) and Complement 3 (C3) (P = 0.019) were significantly correlated with MCD. Furthermore, the area under ROC curves of CH, HDL, LDL, TT and FIB were more than 0.70. Logistic analysis demonstrated that CH and TT were risk factors for MCD. According to the ROC of “CH+TT”, the AUC was 0.827, with the sensitivity of 83.0% and the specificity of 69.8% (P = 0.000). CONCLUSION: The established diagnostic model with CH and TT could be used for classified diagnosis of MCD. PeerJ Inc. 2018-01-12 /pmc/articles/PMC5768169/ /pubmed/29340242 http://dx.doi.org/10.7717/peerj.4237 Text en ©2018 Zhu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Zhu, Hanyu
Han, Qiuxia
Zhang, Dong
Wang, Yong
Gao, Jing
Geng, Wenjia
Yang, Xiaoli
Chen, Xiangmei
A diagnostic model for minimal change disease based on biological parameters
title A diagnostic model for minimal change disease based on biological parameters
title_full A diagnostic model for minimal change disease based on biological parameters
title_fullStr A diagnostic model for minimal change disease based on biological parameters
title_full_unstemmed A diagnostic model for minimal change disease based on biological parameters
title_short A diagnostic model for minimal change disease based on biological parameters
title_sort diagnostic model for minimal change disease based on biological parameters
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5768169/
https://www.ncbi.nlm.nih.gov/pubmed/29340242
http://dx.doi.org/10.7717/peerj.4237
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