Cargando…
Development and Validation of a Novel Model to Predict Liver Histopathology in Patients with Chronic Hepatitis B
It is still vague for chronic hepatitis B (CHB) patients with normal or mildly increasing alanine aminotransferase (ALT) level to undergo antiviral treatment or not. The purpose of our study was to establish a noninvasive model based on routine blood test to predict liver histopathology for antivira...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6415284/ https://www.ncbi.nlm.nih.gov/pubmed/30937309 http://dx.doi.org/10.1155/2019/1621627 |
_version_ | 1783403155589955584 |
---|---|
author | Guo, Hongying Zhu, Beidi Li, Shu Li, Jing Shen, Zhiqing Zheng, Yijuan Zhao, Weidong Tan, Dan Wu, Jingwen Zhang, Xueyun Jiang, Qirong Qi, Xun Mao, Richeng Yu, Xueping Su, Zhijun Zhang, Jiming |
author_facet | Guo, Hongying Zhu, Beidi Li, Shu Li, Jing Shen, Zhiqing Zheng, Yijuan Zhao, Weidong Tan, Dan Wu, Jingwen Zhang, Xueyun Jiang, Qirong Qi, Xun Mao, Richeng Yu, Xueping Su, Zhijun Zhang, Jiming |
author_sort | Guo, Hongying |
collection | PubMed |
description | It is still vague for chronic hepatitis B (CHB) patients with normal or mildly increasing alanine aminotransferase (ALT) level to undergo antiviral treatment or not. The purpose of our study was to establish a noninvasive model based on routine blood test to predict liver histopathology for antiviral therapy. This retrospective study enrolled 258 CHB patients with liver biopsy from the First Hospital of Quanzhou (training cohort, n=126) and Huashan Hospital (validation cohort, n=132). Histologic grading of necroinflammation (G) and liver fibrosis (S) was performed according to the Scheuer scoring system. A novel model, ATPI, including aspartate aminotransferase (AST), total bilirubin (TBil), and platelets (PLT), was developed in training cohort. The area under ROC curves (AUC) of ATPI for predicting antiviral therapy indication was 0.83 in training cohort and was 0.88 in the validation cohort, respectively. Similarly, ATPI also displayed the highest AUC in predicting antiviral therapy indication in CHB patients with normal or mildly increasing ALT level. In conclusion, ATPI is a novel independent model to predict liver histopathology for antiviral therapy in CHB patients with normal and mildly increased ALT levels. |
format | Online Article Text |
id | pubmed-6415284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-64152842019-04-01 Development and Validation of a Novel Model to Predict Liver Histopathology in Patients with Chronic Hepatitis B Guo, Hongying Zhu, Beidi Li, Shu Li, Jing Shen, Zhiqing Zheng, Yijuan Zhao, Weidong Tan, Dan Wu, Jingwen Zhang, Xueyun Jiang, Qirong Qi, Xun Mao, Richeng Yu, Xueping Su, Zhijun Zhang, Jiming Biomed Res Int Research Article It is still vague for chronic hepatitis B (CHB) patients with normal or mildly increasing alanine aminotransferase (ALT) level to undergo antiviral treatment or not. The purpose of our study was to establish a noninvasive model based on routine blood test to predict liver histopathology for antiviral therapy. This retrospective study enrolled 258 CHB patients with liver biopsy from the First Hospital of Quanzhou (training cohort, n=126) and Huashan Hospital (validation cohort, n=132). Histologic grading of necroinflammation (G) and liver fibrosis (S) was performed according to the Scheuer scoring system. A novel model, ATPI, including aspartate aminotransferase (AST), total bilirubin (TBil), and platelets (PLT), was developed in training cohort. The area under ROC curves (AUC) of ATPI for predicting antiviral therapy indication was 0.83 in training cohort and was 0.88 in the validation cohort, respectively. Similarly, ATPI also displayed the highest AUC in predicting antiviral therapy indication in CHB patients with normal or mildly increasing ALT level. In conclusion, ATPI is a novel independent model to predict liver histopathology for antiviral therapy in CHB patients with normal and mildly increased ALT levels. Hindawi 2019-02-27 /pmc/articles/PMC6415284/ /pubmed/30937309 http://dx.doi.org/10.1155/2019/1621627 Text en Copyright © 2019 Hongying Guo et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Guo, Hongying Zhu, Beidi Li, Shu Li, Jing Shen, Zhiqing Zheng, Yijuan Zhao, Weidong Tan, Dan Wu, Jingwen Zhang, Xueyun Jiang, Qirong Qi, Xun Mao, Richeng Yu, Xueping Su, Zhijun Zhang, Jiming Development and Validation of a Novel Model to Predict Liver Histopathology in Patients with Chronic Hepatitis B |
title | Development and Validation of a Novel Model to Predict Liver Histopathology in Patients with Chronic Hepatitis B |
title_full | Development and Validation of a Novel Model to Predict Liver Histopathology in Patients with Chronic Hepatitis B |
title_fullStr | Development and Validation of a Novel Model to Predict Liver Histopathology in Patients with Chronic Hepatitis B |
title_full_unstemmed | Development and Validation of a Novel Model to Predict Liver Histopathology in Patients with Chronic Hepatitis B |
title_short | Development and Validation of a Novel Model to Predict Liver Histopathology in Patients with Chronic Hepatitis B |
title_sort | development and validation of a novel model to predict liver histopathology in patients with chronic hepatitis b |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6415284/ https://www.ncbi.nlm.nih.gov/pubmed/30937309 http://dx.doi.org/10.1155/2019/1621627 |
work_keys_str_mv | AT guohongying developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT zhubeidi developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT lishu developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT lijing developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT shenzhiqing developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT zhengyijuan developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT zhaoweidong developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT tandan developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT wujingwen developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT zhangxueyun developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT jiangqirong developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT qixun developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT maoricheng developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT yuxueping developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT suzhijun developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb AT zhangjiming developmentandvalidationofanovelmodeltopredictliverhistopathologyinpatientswithchronichepatitisb |