Optimizing pegylated interferon α‐2b consolidation duration and developing a recurrence prediction model after functional cure based on updated definition in chronic hepatitis B

Despite achieving functional cure (FC), some chronic hepatitis B (CHB) patients experience HBsAg recurrence. This study aimed to identify the optimal Pegylated Interferon α‐2b (PEG-IFN) consolidation duration and predictors of recurrence based on the updated FC definition.

This study enrolled 443 CHB patients who were on guideline-recommended continuous antiviral therapy and initiated PEG-IFN-based treatment at the First Affiliated Hospital of Nanchang University between November 2020 and May 2023. A total of 113 patients achieved FC, the patients were divided into a training cohort and a validation cohort via time series partitioning method. Patients in each cohort were grouped by recurrence status. Clinical characteristics and longitudinal changes in HBV DNA and HBsAg levels were compared. Independent predictors were identified using multivariate logistic regression. Cutoff values were determined using receiver operating characteristic curve analysis, and the cumulative recurrence rate was estimated by Kaplan–Meier survival analysis. A nomogram was constructed based on independent predictors.

Consolidation therapy duration, PEG-IFN monotherapy, and end-of-treatment (EOT) HBsAb level were independently associated with recurrence (all P < 0.05). The combined model showed high predictive accuracy (AUC = 0.913, 95% CI 0.830–0.964). The optimal cutoff value for consolidation therapy duration was 13.50 weeks. For EOT HBsAb, the cutoff value was 2.03 log₁₀ mIU/mL. The nomogram model demonstrated good predictive performance.

A consolidation duration of ≥ 13.5 weeks, antiviral therapy with PEG-IFN in combination with nucleos(t)ide analogues, and an EOT HBsAb level 2.03 log₁₀ mIU/mL were associated with sustained FC. Post-treatment virological monitoring remains essential to allow early detection of HBsAg reappearance and timely intervention. The nomogram model may provide a reference for clinical decision-making and individualized risk assessment.