Evidence map›Paper›PMID 41356529›Full record

ArticleWorld journal of gastroenterology2025

Retreatment hepatitis B surface antigen clearance prediction model identifies pegylated interferon alpha candidates in chronic hepatitis B.

Yan-Chao Fu, Jun Li, Jia-Yin Wang, Yi-Wen Zhang, Fei Yan, Jing Chen, Qin Du, Chao Yang, Jing Liang, Qing Ye and 1 more

Abstract read
In one paragraph

Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Yan-Chao FuDepartment of Gastroenterology and Hepatology, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China.
Jun LiThe Third Central Clinical College of Tianjin Medical University, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China.
Jia-Yin WangDepartment of Gastroenterology and Hepatology, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China.
Yi-Wen ZhangThe Third Central Clinical College of Tianjin Medical University, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China.
Fei YanThe Third Central Clinical College of Tianjin Medical University, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China.
Jing ChenThe Third Central Clinical College of Tianjin Medical University, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China.
Qin DuNankai University Affiliated Third Central Hospital, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China.
Chao YangDepartment of Gastroenterology and Hepatology, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China.
Jing LiangDepartment of Gastroenterology and Hepatology, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China.
Qing YeDepartment of Gastroenterology and Hepatology, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China.
Hui-Ling XiangDepartment of Gastroenterology and Hepatology, Tianjin University Central Hospital (Tianjin Third Central Hospital), Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin 300170, China. huilingxiang@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic hepatitis B (CHB) patients rarely achieve functional cure with initial pegylated interferon alpha-2b (Peg-IFNα-2b) therapy. Validated tools to guide retreatment candidates are lacking. We hypothesized that clinical indicators predict hepatitis B surface antigen (HBsAg) clearance during retreatment.

aimTo develop a prediction model for HBsAg clearance in Peg-IFNα-2b retreatment.

methodsIn this retrospective cohort study, we enrolled 135 CHB/compensated cirrhosis patients receiving Peg-IFNα-2b retreatment after initial non-clearance at Tianjin University Central Hospital (2017-2025). Predictors were identified through univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox regression. Model performance was assessed

resultsHBsAg clearance rate was 20.74% (28/135). Independent predictors included: Combination nucleos(t)ide analogue (NA) therapy during initial treatment [hazard ratio (HR) = 0.276, 95% confidence interval (CI): 0.092-0.833], baseline HBsAg at retreatment (HR = 0.571, 95%CI: 0.410-0.795), HBsAg decline after initial treatment (HR = 2.050, 95%CI: 1.108-3.793), and treatment interval (HR = 1.013/week, 95%CI: 1.008-1.018). The retreatment HBsAg clearance prediction score (RHCP-S) demonstrated area under the curve of 0.920 (95%CI: 0.863-0.946), sensitivity of 92.3%, specificity of 79.3%. Clearance rates differed significantly: RHCP-S challenge group (≤ 74 points): 3.45%, RHCP-S probable group (74-110 points): 29.63%, RHCP-S dominant group (≥ 110 points): 80.95% (

conclusionThe overall HBsAg clearance rate with Peg-IFNα-2b retreatment was 20.74% (28/135). The RHCP-S model identifies optimal retreatment candidates (≥ 110 points) with 80.95% clearance probability, associated with the absence of combination NA therapy during initial treatment, greater initial HBsAg decline, longer intervals, and lower retreatment HBsAg.

Indexed as

Antiviral AgentsHepatitis B, ChronicHepatitis B Surface AntigensHepatitis B virusInterferon-alphaLiver CirrhosisPolyethylene GlycolsAdultFemaleHumansInterferon alpha-2MaleMiddle AgedRecombinant ProteinsRetreatmentRetrospective StudiesAntiviral AgentsHepatitis B Surface AntigensInterferon-alphaInterferon alpha-2peginterferon alfa-2bPolyethylene GlycolsRecombinant ProteinsChronic hepatitis BHepatitis B surface antigen clearancePegylated interferon alphaPrediction modelRetreatment

Identifiers

PMID41356529
PMCPMC12678901

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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.