Evidence map›Paper›PMID 39867915›Full record

ArticleFrontiers in immunology2024

Exploring using HBsAg to predict interferon treatment course to achieve clinical cure in chronic hepatitis B patients: a clinical study.

Fei Yan, Fei Tang, Jing Chen, YiCheng Lin, XinYu Chen, Qin Du, WeiLi Yin, Jing Liang, Lei Liu, Fang Wang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. [Chinese expert consensus on clinical practice of chronic hepatitis B functional (clinical) cure].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025
    Guideline
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Fei Yan *The Third Central Clinical College of Tianjin Medical University, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Fei Tang *Department of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Jing Chen *The Third Central Clinical College of Tianjin Medical University, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
YiCheng Lin *The Third Central Clinical College of Tianjin Medical University, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
XinYu Chen *The Third Central Clinical College of Tianjin Medical University, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Qin DuNankai University Affiliated Third Center Hospital, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
WeiLi YinDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Jing LiangDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Lei LiuDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Fang WangDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
BaiGuo XuDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Qing YeDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
HuiLing XiangDepartment of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Although pegylated interferon α-2b (PEG-IFN α-2b) therapy for chronic hepatitis B has received increasing attention, determining the optimal treatment course remains challenging. This research aimed to develop an efficient model for predicting interferon (IFN) treatment course. Methods: Patients with chronic hepatitis B, undergoing PEG-IFN α-2b monotherapy or combined with NAs (Nucleoside Analogs), were recruited from January 2018 to December 2023 at Tianjin Third Central Hospital. All patients achieved hepatitis B surface antigen (HBsAg) clearance post-treatment. Result: The study enrolled 176 patients with chronic hepatitis B, with the median IFN treatment course of 35.23 ± 25.22 weeks. They were randomly divided into two cohorts in a ratio of 7:3. And there were 123 patients in the training cohort and 53 patients in the validation cohort. Univariable and multivariable analyses demonstrated that baseline HBsAg, 12 weeks HBsAg and the presence of cirrhosis significantly influenced IFN treatment course, and both are risk factors (β=7.27,4.27,10.91; p<0.05). After adjusting for confounding factors, HBsAg remained a significant predictor (β=6.99, 95%CI: 3.59,10.40; p<0.05), which was finally included to establish the model. The actual and predicted values in the validation cohort were highly matched, meanwhile the mean absolute percentage error (MAPE), root mean square error (RMSE) and accuracy (ACC) of the validation cohort were calculated. External validation also suggests that the model can be used as a tool for initial assessment. Conclusion: Baseline HBsAg in chronic hepatitis B patients were a risk factor for prolonged IFN treatment course with a positive correlation. Ultimately, a personalized model based on baseline HBsAg levels can be established to roughly estimate the duration of interferon therapy prior to treatment initiation, thereby guiding clinical decision-making.

Indexed as

Antiviral AgentsHepatitis B, ChronicHepatitis B Surface AntigensHepatitis B virusInterferon-alphaInterferon alpha-2Polyethylene GlycolsAdultFemaleHumansMaleMiddle AgedRecombinant ProteinsTreatment OutcomeAntiviral AgentsHepatitis B Surface AntigensInterferon-alphaInterferon alpha-2peginterferon alfa-2bPolyethylene GlycolsRecombinant Proteinschronic hepatitis B (CHB)clinical curehepatitis b surface antigen (HBsAg)modelPEG-IFN α

Identifiers

PMID39867915
PMCPMC11758162

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Registered trials

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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 OpenQuestion graph.