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.