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Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B

BACKGROUND AND AIMS: Noninvasive models have been developed for fibrosis assessment in patients with chronic hepatitis B. However, the sensitivity, specificity and diagnostic accuracy in evaluating liver fibrosis of these methods have not been validated and compared in the same group of patients. Th...

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Autores principales: Cheng, Jieyao, Hou, Jinlin, Ding, Huiguo, Chen, Guofeng, Xie, Qing, Wang, Yuming, Zeng, Minde, Ou, Xiaojuan, Ma, Hong, Jia, Jidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4692502/
https://www.ncbi.nlm.nih.gov/pubmed/26709706
http://dx.doi.org/10.1371/journal.pone.0144425
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author Cheng, Jieyao
Hou, Jinlin
Ding, Huiguo
Chen, Guofeng
Xie, Qing
Wang, Yuming
Zeng, Minde
Ou, Xiaojuan
Ma, Hong
Jia, Jidong
author_facet Cheng, Jieyao
Hou, Jinlin
Ding, Huiguo
Chen, Guofeng
Xie, Qing
Wang, Yuming
Zeng, Minde
Ou, Xiaojuan
Ma, Hong
Jia, Jidong
author_sort Cheng, Jieyao
collection PubMed
description BACKGROUND AND AIMS: Noninvasive models have been developed for fibrosis assessment in patients with chronic hepatitis B. However, the sensitivity, specificity and diagnostic accuracy in evaluating liver fibrosis of these methods have not been validated and compared in the same group of patients. The aim of this study was to verify the diagnostic performance and reproducibility of ten reported noninvasive models in a large cohort of Asian CHB patients. METHODS: The diagnostic performance of ten noninvasive models (HALF index, FibroScan, S index, Zeng model, Youyi model, Hui model, APAG, APRI, FIB-4 and FibroTest) was assessed against the liver histology by ROC curve analysis in CHB patients. The reproducibility of the ten models were evaluated by recalculating the diagnostic values at the given cut-off values defined by the original studies. RESULTS: Six models (HALF index, FibroScan, Zeng model, Youyi model, S index and FibroTest) had AUROCs higher than 0.70 in predicting any fibrosis stage and 2 of them had best diagnostic performance with AUROCs to predict F≥2, F≥3 and F4 being 0.83, 0.89 and 0.89 for HALF index, 0.82, 0.87 and 0.87 for FibroScan, respectively. Four models (HALF index, FibroScan, Zeng model and Youyi model) showed good diagnostic values at given cut-offs. CONCLUSIONS: HALF index, FibroScan, Zeng model, Youyi model, S index and FibroTest show a good diagnostic performance and all of them, except S index and FibroTest, have good reproducibility for evaluating liver fibrosis in CHB patients. REGISTRATION NUMBER: ChiCTR-DCS-07000039.
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spelling pubmed-46925022016-01-12 Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B Cheng, Jieyao Hou, Jinlin Ding, Huiguo Chen, Guofeng Xie, Qing Wang, Yuming Zeng, Minde Ou, Xiaojuan Ma, Hong Jia, Jidong PLoS One Research Article BACKGROUND AND AIMS: Noninvasive models have been developed for fibrosis assessment in patients with chronic hepatitis B. However, the sensitivity, specificity and diagnostic accuracy in evaluating liver fibrosis of these methods have not been validated and compared in the same group of patients. The aim of this study was to verify the diagnostic performance and reproducibility of ten reported noninvasive models in a large cohort of Asian CHB patients. METHODS: The diagnostic performance of ten noninvasive models (HALF index, FibroScan, S index, Zeng model, Youyi model, Hui model, APAG, APRI, FIB-4 and FibroTest) was assessed against the liver histology by ROC curve analysis in CHB patients. The reproducibility of the ten models were evaluated by recalculating the diagnostic values at the given cut-off values defined by the original studies. RESULTS: Six models (HALF index, FibroScan, Zeng model, Youyi model, S index and FibroTest) had AUROCs higher than 0.70 in predicting any fibrosis stage and 2 of them had best diagnostic performance with AUROCs to predict F≥2, F≥3 and F4 being 0.83, 0.89 and 0.89 for HALF index, 0.82, 0.87 and 0.87 for FibroScan, respectively. Four models (HALF index, FibroScan, Zeng model and Youyi model) showed good diagnostic values at given cut-offs. CONCLUSIONS: HALF index, FibroScan, Zeng model, Youyi model, S index and FibroTest show a good diagnostic performance and all of them, except S index and FibroTest, have good reproducibility for evaluating liver fibrosis in CHB patients. REGISTRATION NUMBER: ChiCTR-DCS-07000039. Public Library of Science 2015-12-28 /pmc/articles/PMC4692502/ /pubmed/26709706 http://dx.doi.org/10.1371/journal.pone.0144425 Text en © 2015 Cheng 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Cheng, Jieyao
Hou, Jinlin
Ding, Huiguo
Chen, Guofeng
Xie, Qing
Wang, Yuming
Zeng, Minde
Ou, Xiaojuan
Ma, Hong
Jia, Jidong
Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B
title Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B
title_full Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B
title_fullStr Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B
title_full_unstemmed Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B
title_short Validation of Ten Noninvasive Diagnostic Models for Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B
title_sort validation of ten noninvasive diagnostic models for prediction of liver fibrosis in patients with chronic hepatitis b
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4692502/
https://www.ncbi.nlm.nih.gov/pubmed/26709706
http://dx.doi.org/10.1371/journal.pone.0144425
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