Evidence map›Paper›PMID 40426052›Full record

ArticleBMC gastroenterology2025

Development and validation of a new predictive model for the immune tolerance stage of chronic HBV infection based on the liver histopathological changes.

Wentao Li, Rui Huang, Jian Wang, Binhao Zhang, Qiupeng Wang, Jiang Feng, Tongjing Xing

Abstract readValidation Study
In one paragraph

Article in BMC gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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2 citing papers in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Wentao Li *Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai, Zhejiang Province, China.
Rui Huang *Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China.
Jian WangNanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu Province, China.
Binhao ZhangTaizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai, Zhejiang Province, China.
Qiupeng Wang
Jiang FengTaizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai, Zhejiang Province, China.
Tongjing XingTaizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai, Zhejiang Province, China. xingtj518@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify clinical and viral indicators for the development of a new model to accurately differentiate the stages of chronic hepatitis B virus (HBV) infection based on histopathological changes in the liver.

methodsClinical and liver pathology data from chronic hepatitis B (CHB) patients who underwent liver biopsy were retrospectively collected. The patients were allocated into test and validation groups. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated to idneitfy the optimal diagnostic value for differentiating the stages of chronic HBV infection.

resultsA total of 118 patients and 73 patients who met the diagnostic and inclusion criteria were selected as the test group and validation group, respectively. Multivariate analysis revealed that HBeAg was independently correlated with the IT and IC stages. The cutoff value of HBeAg used to quantitatively differentiate between IT and IC was 1335 S/CO. The AUC values were 0.921 (95% confidence interval (CI): 0.836-0.971) and 0.846 (95% CI: 0.726-0.967) in the test and validation groups, respectively. A new prediction model of the IT stage was established by using three indicators, namely, HBeAg, HBsAg and HBV DNA. The AUC values were 0.923 (95% CI: 0.864-0.982, p < 0.001) and 0.89 (95% CI: 0.787-0.994, p < 0.01) in the test and validation groups, respectively, when this prediction model was used. For the new model, CMA guidelines (2019 version), EASL guidelines (2017 version) and AASLD guidelines (2018 version), the error rates in the test group were 4.65%, 11.62%, 23.26%, and 46.51%, respectively, while the errors rates in the validation group were 20.0%, 25.0%, 40.0%, and 45.0%, respectively.

conclusionsHigh levels of HBeAg, rather than HBeAg positivity, may serve as a predictor of the IT stage. A predictive model for the immune tolerance stage was established by combining three indicators. Compared with the recommended standards from multiple current guidelines, the new prediction model has a significantly lower error rate.

Indexed as

Hepatitis B, ChronicImmune ToleranceLiverAdultArea Under CurveBiopsyDNA, ViralFemaleHepatitis B e AntigensHepatitis B Surface AntigensHepatitis B virusHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesDNA, ViralHepatitis B e AntigensHepatitis B Surface AntigensChronic HBV infectionImmune stageLiver histopathologyNatural history

Identifiers

PMID40426052
PMCPMC12107786

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.