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Big Data Information under Proportional Hazard Mathematical Model in Analysis of Hepatitis B Virus Infection Data of Patients with Interventional Liver Cancer through Antiviral Therapy of Entecavir

The objective of this study was to analyze the application of proportional hazard mathematical model (PHMM) in Hepatitis B Virus (HBV) infection analysis of interventional liver cancer patients treated with entecavir, so as to provide data support for clinical diagnosis and treatment. Based on the s...

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Autores principales: Zhang, Yichi, Zhao, Shuai, Ding, Han, Song, Xiaoling, Miao, Huijie, Cui, Xuya, Wang, Jian, Han, Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718310/
https://www.ncbi.nlm.nih.gov/pubmed/34976330
http://dx.doi.org/10.1155/2021/6225403
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author Zhang, Yichi
Zhao, Shuai
Ding, Han
Song, Xiaoling
Miao, Huijie
Cui, Xuya
Wang, Jian
Han, Bing
author_facet Zhang, Yichi
Zhao, Shuai
Ding, Han
Song, Xiaoling
Miao, Huijie
Cui, Xuya
Wang, Jian
Han, Bing
author_sort Zhang, Yichi
collection PubMed
description The objective of this study was to analyze the application of proportional hazard mathematical model (PHMM) in Hepatitis B Virus (HBV) infection analysis of interventional liver cancer patients treated with entecavir, so as to provide data support for clinical diagnosis and treatment. Based on the survival analysis, the treatment factor x was undertaken as an independent variable to perform linear regression. The regression model took the hazard rate function as the dependent variable to establish an exponential regression equation to construct a PHMM. 136 patients with primary liver cancer receiving interventional chemoembolization combined with the drug (entecavir) were selected as the experimental group, who were in the computer gene expression omnibus (GEO). 87 patients with primary liver cancer who underwent interventional chemoembolization therapy without antiviral treatment were taken as the control group. The PHMM was adopted for comprehensive analysis. In addition, the factors affecting the virological response to antiviral therapy were analyzed using the multiple logistic regression. The results revealed that HBV deoxyribonucleic acid (DNA) negative conversion rate, Hepatitis B e-Antigen (HBeAg) negative conversion rate, and HBeAg serological conversion rate in the experimental group were much higher than those in the control group (P < 0.05). HBV DNA level and proportion of HBsAg <100 IU/mL in the experimental group were much lower than those in the control group (P < 0.05). The virological breakthrough rate and incidence of adverse events at week 24 in the experimental group were greatly lower than those in the control group (P < 0.05). The adverse virological response of patient was positively correlated with HBV DNA load and HBeAg status and negatively correlated with alanine aminotransferase (ALT) level (P < 0.05). Therefore, entecavir can significantly inhibit HBV DNA replication in patients with liver cancer, showing high antiviral effect. High baseline HBV DNA load, positive HBeAg, and low baseline alanine aminotransferase levels were independent risk factors for adverse virology response to entecavir antiviral therapy, which provided a reference for the selection of antiviral drugs for HBV infection.
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spelling pubmed-87183102021-12-31 Big Data Information under Proportional Hazard Mathematical Model in Analysis of Hepatitis B Virus Infection Data of Patients with Interventional Liver Cancer through Antiviral Therapy of Entecavir Zhang, Yichi Zhao, Shuai Ding, Han Song, Xiaoling Miao, Huijie Cui, Xuya Wang, Jian Han, Bing J Healthc Eng Research Article The objective of this study was to analyze the application of proportional hazard mathematical model (PHMM) in Hepatitis B Virus (HBV) infection analysis of interventional liver cancer patients treated with entecavir, so as to provide data support for clinical diagnosis and treatment. Based on the survival analysis, the treatment factor x was undertaken as an independent variable to perform linear regression. The regression model took the hazard rate function as the dependent variable to establish an exponential regression equation to construct a PHMM. 136 patients with primary liver cancer receiving interventional chemoembolization combined with the drug (entecavir) were selected as the experimental group, who were in the computer gene expression omnibus (GEO). 87 patients with primary liver cancer who underwent interventional chemoembolization therapy without antiviral treatment were taken as the control group. The PHMM was adopted for comprehensive analysis. In addition, the factors affecting the virological response to antiviral therapy were analyzed using the multiple logistic regression. The results revealed that HBV deoxyribonucleic acid (DNA) negative conversion rate, Hepatitis B e-Antigen (HBeAg) negative conversion rate, and HBeAg serological conversion rate in the experimental group were much higher than those in the control group (P < 0.05). HBV DNA level and proportion of HBsAg <100 IU/mL in the experimental group were much lower than those in the control group (P < 0.05). The virological breakthrough rate and incidence of adverse events at week 24 in the experimental group were greatly lower than those in the control group (P < 0.05). The adverse virological response of patient was positively correlated with HBV DNA load and HBeAg status and negatively correlated with alanine aminotransferase (ALT) level (P < 0.05). Therefore, entecavir can significantly inhibit HBV DNA replication in patients with liver cancer, showing high antiviral effect. High baseline HBV DNA load, positive HBeAg, and low baseline alanine aminotransferase levels were independent risk factors for adverse virology response to entecavir antiviral therapy, which provided a reference for the selection of antiviral drugs for HBV infection. Hindawi 2021-12-23 /pmc/articles/PMC8718310/ /pubmed/34976330 http://dx.doi.org/10.1155/2021/6225403 Text en Copyright © 2021 Yichi Zhang 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
Zhang, Yichi
Zhao, Shuai
Ding, Han
Song, Xiaoling
Miao, Huijie
Cui, Xuya
Wang, Jian
Han, Bing
Big Data Information under Proportional Hazard Mathematical Model in Analysis of Hepatitis B Virus Infection Data of Patients with Interventional Liver Cancer through Antiviral Therapy of Entecavir
title Big Data Information under Proportional Hazard Mathematical Model in Analysis of Hepatitis B Virus Infection Data of Patients with Interventional Liver Cancer through Antiviral Therapy of Entecavir
title_full Big Data Information under Proportional Hazard Mathematical Model in Analysis of Hepatitis B Virus Infection Data of Patients with Interventional Liver Cancer through Antiviral Therapy of Entecavir
title_fullStr Big Data Information under Proportional Hazard Mathematical Model in Analysis of Hepatitis B Virus Infection Data of Patients with Interventional Liver Cancer through Antiviral Therapy of Entecavir
title_full_unstemmed Big Data Information under Proportional Hazard Mathematical Model in Analysis of Hepatitis B Virus Infection Data of Patients with Interventional Liver Cancer through Antiviral Therapy of Entecavir
title_short Big Data Information under Proportional Hazard Mathematical Model in Analysis of Hepatitis B Virus Infection Data of Patients with Interventional Liver Cancer through Antiviral Therapy of Entecavir
title_sort big data information under proportional hazard mathematical model in analysis of hepatitis b virus infection data of patients with interventional liver cancer through antiviral therapy of entecavir
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718310/
https://www.ncbi.nlm.nih.gov/pubmed/34976330
http://dx.doi.org/10.1155/2021/6225403
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