Predictive model for HBsAg clearance rate in chronic hepatitis B patients treated with pegylated interferon α-2b for 48 weeks.

Abstract

Background and aims: Chronic hepatitis B (CHB) is a major global health concern. This study aims to investigate the factors influencing hepatitis B surface antigen (HBsAg) clearance in CHB patients treated with pegylated interferon α-2b (Peg-IFNα-2b) for 48 weeks and to establish a predictive model.

Methods: This analysis is based on the "OASIS" project, a prospective real-world multicenter study in China. We included CHB patients who completed 48 weeks of Peg-IFNα-2b treatment. Patients were randomly assigned to a training set and a validation set in a ratio of approximately 4:1 by spss 26.0, and were divided into clearance and non-clearance groups based on HBsAg status at 48 weeks. Clinical data were analyzed using SPSS 26.0, employing chi-square tests for categorical data and Mann-Whitney U tests for continuous variables. Significant factors (p < 0.05) were incorporated into a binary logistic regression model to identify independent predictors of HBsAg clearance. The predictive model's performance was evaluated using ROC curve analysis.

Results: We included 868 subjects, divided into the clearance group (187 cases) and the non-clearance group (681 cases). They were randomly assigned to a training set (702 cases) and a validation set (166 cases). Key predictors included female gender (OR = 1.879), lower baseline HBsAg levels (OR = 0.371), and cirrhosis (OR = 0.438). The final predictive model was: Logit(P) = 0.92 + Gender (Female) * 0.66 - HBsAg (log) * 0.96 - Cirrhosis * 0.88. ROC analysis showed an AUC of 0.80 for the training set and 0.82 for the validation set, indicating good predictive performance.

Conclusion: Gender, baseline HBsAg levels, and cirrhosis are significant predictors of HBsAg clearance in CHB patients after 48 weeks of Peg-IFNα-2b therapy. The developed predictive model demonstrates high accuracy and potential clinical utility.