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A Novel Non-invasive Model Based on GPR for the Prediction of Liver Fibrosis in Patients With Chronic Hepatitis B

Background: Some controversy remains regarding conventional serum indices for the evaluation of liver fibrosis. Therefore, we aimed to combine the existing index with other serum parameters to discriminate liver fibrosis stages in patients with chronic hepatitis B (CHB). Methods: A total of 1,622 tr...

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Autores principales: Ding, Rongrong, Lu, Wei, Zhou, Xinlan, Huang, Dan, Wang, Yanbing, Li, Xiufen, Yan, Li, Lin, Weijia, Song, Shu, Zhang, Zhanqing, Chen, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8495242/
https://www.ncbi.nlm.nih.gov/pubmed/34631748
http://dx.doi.org/10.3389/fmed.2021.727706
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author Ding, Rongrong
Lu, Wei
Zhou, Xinlan
Huang, Dan
Wang, Yanbing
Li, Xiufen
Yan, Li
Lin, Weijia
Song, Shu
Zhang, Zhanqing
Chen, Liang
author_facet Ding, Rongrong
Lu, Wei
Zhou, Xinlan
Huang, Dan
Wang, Yanbing
Li, Xiufen
Yan, Li
Lin, Weijia
Song, Shu
Zhang, Zhanqing
Chen, Liang
author_sort Ding, Rongrong
collection PubMed
description Background: Some controversy remains regarding conventional serum indices for the evaluation of liver fibrosis. Therefore, we aimed to combine the existing index with other serum parameters to discriminate liver fibrosis stages in patients with chronic hepatitis B (CHB). Methods: A total of 1,622 treatment-naïve CHB patients were divided into training (n = 1,211) and validation (n = 451) cohorts. Liver histology was assessed according to the Scheuer scoring scheme. All common demographic and clinical parameters were analyzed. Results: By utilizing the results of the logistic regression analysis, we developed a novel index, the product of GPR, international normalized ratio (INR), and type IV collagen (GIVPR), to discriminate liver fibrosis. In the training group, the areas under the ROCs (AUROCs) of GIVPR, APRI, FIB-4, and GPR for significant fibrosis were 0.81, 0.75, 0.72, and 0.77, respectively; the AUROCs of GIVPR, APRI, FIB-4, and GPR for advanced fibrosis were 0.82, 0.74, 0.74, and 0.78, respectively; and the AUROCs of GIVPR, APRI, FIB-4, and GPR for cirrhosis were 0.87, 0.78, 0.78, and 0.83, respectively. Similar results were also obtained in the validation group. Furthermore, the decision curve analysis suggested that GIVPR represented superior clinical benefits in both independent cohorts. Conclusion: The GIVPR constructed on GPR represents a superior predictive model for discriminating liver fibrosis in CHB patients.
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spelling pubmed-84952422021-10-08 A Novel Non-invasive Model Based on GPR for the Prediction of Liver Fibrosis in Patients With Chronic Hepatitis B Ding, Rongrong Lu, Wei Zhou, Xinlan Huang, Dan Wang, Yanbing Li, Xiufen Yan, Li Lin, Weijia Song, Shu Zhang, Zhanqing Chen, Liang Front Med (Lausanne) Medicine Background: Some controversy remains regarding conventional serum indices for the evaluation of liver fibrosis. Therefore, we aimed to combine the existing index with other serum parameters to discriminate liver fibrosis stages in patients with chronic hepatitis B (CHB). Methods: A total of 1,622 treatment-naïve CHB patients were divided into training (n = 1,211) and validation (n = 451) cohorts. Liver histology was assessed according to the Scheuer scoring scheme. All common demographic and clinical parameters were analyzed. Results: By utilizing the results of the logistic regression analysis, we developed a novel index, the product of GPR, international normalized ratio (INR), and type IV collagen (GIVPR), to discriminate liver fibrosis. In the training group, the areas under the ROCs (AUROCs) of GIVPR, APRI, FIB-4, and GPR for significant fibrosis were 0.81, 0.75, 0.72, and 0.77, respectively; the AUROCs of GIVPR, APRI, FIB-4, and GPR for advanced fibrosis were 0.82, 0.74, 0.74, and 0.78, respectively; and the AUROCs of GIVPR, APRI, FIB-4, and GPR for cirrhosis were 0.87, 0.78, 0.78, and 0.83, respectively. Similar results were also obtained in the validation group. Furthermore, the decision curve analysis suggested that GIVPR represented superior clinical benefits in both independent cohorts. Conclusion: The GIVPR constructed on GPR represents a superior predictive model for discriminating liver fibrosis in CHB patients. Frontiers Media S.A. 2021-09-23 /pmc/articles/PMC8495242/ /pubmed/34631748 http://dx.doi.org/10.3389/fmed.2021.727706 Text en Copyright © 2021 Ding, Lu, Zhou, Huang, Wang, Li, Yan, Lin, Song, Zhang and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Ding, Rongrong
Lu, Wei
Zhou, Xinlan
Huang, Dan
Wang, Yanbing
Li, Xiufen
Yan, Li
Lin, Weijia
Song, Shu
Zhang, Zhanqing
Chen, Liang
A Novel Non-invasive Model Based on GPR for the Prediction of Liver Fibrosis in Patients With Chronic Hepatitis B
title A Novel Non-invasive Model Based on GPR for the Prediction of Liver Fibrosis in Patients With Chronic Hepatitis B
title_full A Novel Non-invasive Model Based on GPR for the Prediction of Liver Fibrosis in Patients With Chronic Hepatitis B
title_fullStr A Novel Non-invasive Model Based on GPR for the Prediction of Liver Fibrosis in Patients With Chronic Hepatitis B
title_full_unstemmed A Novel Non-invasive Model Based on GPR for the Prediction of Liver Fibrosis in Patients With Chronic Hepatitis B
title_short A Novel Non-invasive Model Based on GPR for the Prediction of Liver Fibrosis in Patients With Chronic Hepatitis B
title_sort novel non-invasive model based on gpr for the prediction of liver fibrosis in patients with chronic hepatitis b
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8495242/
https://www.ncbi.nlm.nih.gov/pubmed/34631748
http://dx.doi.org/10.3389/fmed.2021.727706
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