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Related Experiment Video

Updated: Jan 11, 2026

Humanized NOD/SCID/IL2rγnull (hu-NSG) Mouse Model for HIV Replication and Latency Studies
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Development and Validation of a Predictive Model for HBsAg Seroclearance After Peg-IFN-Based Therapy: A Multicentre

Hui-Hui Liu1, Xue-Mei Jiang2, Chao Cui3

  • 1Department of Hepatology, Qilu Hospital of Shandong University, Jinan, Shandong, People's Republic of China.

Drug Design, Development and Therapy
|November 13, 2025
PubMed
Summary

A new model predicts HBsAg seroclearance in chronic hepatitis B (CHB) patients receiving Peg-IFN therapy. Baseline factors like age, HBsAg levels, and ALT help identify patients likely to achieve HBsAg seroclearance.

Keywords:
HBsAg seroclearancechronic hepatitis Bmulticentre studypegylated interferon alfapredictive model

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Area of Science:

  • Hepatology
  • Virology
  • Clinical Prediction Modeling

Background:

  • Hepatitis B virus (HBV) infection remains a global health concern.
  • Predicting HBsAg seroclearance is crucial for optimizing treatment strategies in chronic hepatitis B (CHB).
  • Pegylated interferon (Peg-IFN)-based therapy offers a potential treatment option for CHB patients.

Purpose of the Study:

  • To develop and validate a predictive model for HBsAg seroclearance.
  • To identify baseline parameters associated with HBsAg seroclearance after Peg-IFN therapy.
  • To aid in clinical decision-making for Peg-IFN-based treatment in virally suppressed HBeAg-negative CHB patients.

Main Methods:

  • Retrospective enrollment of 377 nucleos(t)ide analogue-suppressed, HBeAg-negative CHB patients receiving 48-week Peg-IFN therapy.
  • Development of a multivariate Cox regression model in a cohort of 229 patients.
  • Validation of the model in an independent cohort of 148 patients.

Main Results:

  • HBsAg seroclearance rates were 17.9% in the development cohort and 20.27% in the validation cohort.
  • The predictive model incorporated age, baseline HBsAg, and alanine aminotransferase (ALT).
  • The model demonstrated strong predictive performance (AUC 0.842 in development, 0.852 in validation) and identified patient subgroups with varying seroclearance incidence.

Conclusions:

  • A robust predictive model for HBsAg seroclearance was successfully constructed.
  • The model utilizes readily available baseline parameters for clinical application.
  • This tool can guide the effective use of Peg-IFN-based therapy in CHB management.