Cargando…

A novel predictive model using routinely clinical parameters to predict liver fibrosis in patients with chronic hepatitis B

OBJECTIVES: Noninvasive models have been established for the assessment of liver fibrosis in patients with chronic hepatitis B(CHB). However, the predictive performance of these established models remains inconclusive. We aimed to develop a novel predictive model for liver fibrosis in CHB based on r...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Jian, Yan, Xiaomin, Yang, Yue, Chang, Haiyan, Jia, Bei, Zhao, Xiang-An, Chen, Guangmei, Xia, Juan, Liu, Yong, Chen, Yuxin, Wang, Guiyang, Wang, Li, Zhang, Zhaoping, Ding, Weimao, Huang, Rui, Wu, Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5601730/
https://www.ncbi.nlm.nih.gov/pubmed/28938634
http://dx.doi.org/10.18632/oncotarget.19501
_version_ 1783264443514224640
author Wang, Jian
Yan, Xiaomin
Yang, Yue
Chang, Haiyan
Jia, Bei
Zhao, Xiang-An
Chen, Guangmei
Xia, Juan
Liu, Yong
Chen, Yuxin
Wang, Guiyang
Wang, Li
Zhang, Zhaoping
Ding, Weimao
Huang, Rui
Wu, Chao
author_facet Wang, Jian
Yan, Xiaomin
Yang, Yue
Chang, Haiyan
Jia, Bei
Zhao, Xiang-An
Chen, Guangmei
Xia, Juan
Liu, Yong
Chen, Yuxin
Wang, Guiyang
Wang, Li
Zhang, Zhaoping
Ding, Weimao
Huang, Rui
Wu, Chao
author_sort Wang, Jian
collection PubMed
description OBJECTIVES: Noninvasive models have been established for the assessment of liver fibrosis in patients with chronic hepatitis B(CHB). However, the predictive performance of these established models remains inconclusive. We aimed to develop a novel predictive model for liver fibrosis in CHB based on routinely clinical parameters. RESULTS: Platelets(PLT), the standard deviation of red blood cell distribution width(RDW-SD), alkaline phosphatase(ALP) and globulin were independent predictors of significant fibrosis by multivariable analysis. Based on these parameters, a new predictive model namely APRG(ALP/PLT/RDW-SD/globulin) was proposed. The areas under the receiver-operating characteristic curves(AUROCs) of APRG index in predicting significant fibrosis(≥F2), advanced fibrosis(≥F3) and liver cirrhosis(≥F4) were 0.757(95%CI 0.699 to 0.816), 0.763(95%CI 0.711 to 0.816) and 0.781(95%CI 0.728 to 0.835), respectively. The AUROCs of the APRG were significantly higher than that of aspartate transaminase(AST) to PLT ratio index(APRI), RDW to PLT ratio(RPR) and AST to alanine aminotransferase ratio(AAR) to predict significant fibrosis, advanced fibrosis and cirrhosis. The AUROCs of the APRG were also significantly higher than fibrosis-4 score (FIB-4) (0.723, 95%CI 0.663 to 0.783) for cirrhosis(P=0.034) and better than gamma-glutamyl transpeptidase(GGT) to PLT ratio(GPR) (0.657, 95%CI 0.590 to 0.724) for significant fibrosis(P=0.001). MATERIALS AND METHODS: 308 CHB patients who underwent liver biopsy were enrolled. The diagnostic values of the APRG for liver fibrosis with other noninvasive models were compared. CONCLUSIONS: The APRG has a better diagnostic value than conventionally predictive models to assess liver fibrosis in CHB patients. The application of APRG may reduce the need for liver biopsy in CHB patients in clinical practice.
format Online
Article
Text
id pubmed-5601730
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Impact Journals LLC
record_format MEDLINE/PubMed
spelling pubmed-56017302017-09-21 A novel predictive model using routinely clinical parameters to predict liver fibrosis in patients with chronic hepatitis B Wang, Jian Yan, Xiaomin Yang, Yue Chang, Haiyan Jia, Bei Zhao, Xiang-An Chen, Guangmei Xia, Juan Liu, Yong Chen, Yuxin Wang, Guiyang Wang, Li Zhang, Zhaoping Ding, Weimao Huang, Rui Wu, Chao Oncotarget Research Paper OBJECTIVES: Noninvasive models have been established for the assessment of liver fibrosis in patients with chronic hepatitis B(CHB). However, the predictive performance of these established models remains inconclusive. We aimed to develop a novel predictive model for liver fibrosis in CHB based on routinely clinical parameters. RESULTS: Platelets(PLT), the standard deviation of red blood cell distribution width(RDW-SD), alkaline phosphatase(ALP) and globulin were independent predictors of significant fibrosis by multivariable analysis. Based on these parameters, a new predictive model namely APRG(ALP/PLT/RDW-SD/globulin) was proposed. The areas under the receiver-operating characteristic curves(AUROCs) of APRG index in predicting significant fibrosis(≥F2), advanced fibrosis(≥F3) and liver cirrhosis(≥F4) were 0.757(95%CI 0.699 to 0.816), 0.763(95%CI 0.711 to 0.816) and 0.781(95%CI 0.728 to 0.835), respectively. The AUROCs of the APRG were significantly higher than that of aspartate transaminase(AST) to PLT ratio index(APRI), RDW to PLT ratio(RPR) and AST to alanine aminotransferase ratio(AAR) to predict significant fibrosis, advanced fibrosis and cirrhosis. The AUROCs of the APRG were also significantly higher than fibrosis-4 score (FIB-4) (0.723, 95%CI 0.663 to 0.783) for cirrhosis(P=0.034) and better than gamma-glutamyl transpeptidase(GGT) to PLT ratio(GPR) (0.657, 95%CI 0.590 to 0.724) for significant fibrosis(P=0.001). MATERIALS AND METHODS: 308 CHB patients who underwent liver biopsy were enrolled. The diagnostic values of the APRG for liver fibrosis with other noninvasive models were compared. CONCLUSIONS: The APRG has a better diagnostic value than conventionally predictive models to assess liver fibrosis in CHB patients. The application of APRG may reduce the need for liver biopsy in CHB patients in clinical practice. Impact Journals LLC 2017-07-22 /pmc/articles/PMC5601730/ /pubmed/28938634 http://dx.doi.org/10.18632/oncotarget.19501 Text en Copyright: © 2017 Wang et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (http://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Wang, Jian
Yan, Xiaomin
Yang, Yue
Chang, Haiyan
Jia, Bei
Zhao, Xiang-An
Chen, Guangmei
Xia, Juan
Liu, Yong
Chen, Yuxin
Wang, Guiyang
Wang, Li
Zhang, Zhaoping
Ding, Weimao
Huang, Rui
Wu, Chao
A novel predictive model using routinely clinical parameters to predict liver fibrosis in patients with chronic hepatitis B
title A novel predictive model using routinely clinical parameters to predict liver fibrosis in patients with chronic hepatitis B
title_full A novel predictive model using routinely clinical parameters to predict liver fibrosis in patients with chronic hepatitis B
title_fullStr A novel predictive model using routinely clinical parameters to predict liver fibrosis in patients with chronic hepatitis B
title_full_unstemmed A novel predictive model using routinely clinical parameters to predict liver fibrosis in patients with chronic hepatitis B
title_short A novel predictive model using routinely clinical parameters to predict liver fibrosis in patients with chronic hepatitis B
title_sort novel predictive model using routinely clinical parameters to predict liver fibrosis in patients with chronic hepatitis b
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5601730/
https://www.ncbi.nlm.nih.gov/pubmed/28938634
http://dx.doi.org/10.18632/oncotarget.19501
work_keys_str_mv AT wangjian anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT yanxiaomin anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT yangyue anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT changhaiyan anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT jiabei anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT zhaoxiangan anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT chenguangmei anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT xiajuan anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT liuyong anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT chenyuxin anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT wangguiyang anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT wangli anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT zhangzhaoping anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT dingweimao anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT huangrui anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT wuchao anovelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT wangjian novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT yanxiaomin novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT yangyue novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT changhaiyan novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT jiabei novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT zhaoxiangan novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT chenguangmei novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT xiajuan novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT liuyong novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT chenyuxin novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT wangguiyang novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT wangli novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT zhangzhaoping novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT dingweimao novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT huangrui novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb
AT wuchao novelpredictivemodelusingroutinelyclinicalparameterstopredictliverfibrosisinpatientswithchronichepatitisb