ArticleBMC infectious diseases2026
Noninvasive models for patient selection and Early response monitoring of interferon therapy for HBsAg clearance in patients with chronic hepatitis B.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundHepatitis B surface antigen (HBsAg) clearance is a critical step toward functional cure and is associated with improved virological control, as well as a reduced risk of cirrhosis and hepatocellular carcinoma (HCC). Pegylated interferon-α (Peg-IFN-α) has shown promise in achieving HBsAg clearance in selected patient subsets. Precise identification of these "advantaged populations" and early assessment of treatment response are critical for optimizing personalized therapy.
methodsWe retrospectively analyzed 239 chronic hepatitis B (CHB) patients, including 175 patients with baseline HBsAg levels < 1500 IU/mL, who received Peg-IFN-α therapy. Parameters were compared between HBsAg clearance and non-clearance groups. We developed/validated Model 1 (gender, age, baseline HBsAg) to identify treatment-advantaged patients. Model 2 incorporated week-12 dynamics: platelet count change (ΔPLT), alanine aminotransferase-to-aspartate aminotransferase ratio (ALT/AST) rates of change, and HBsAg level. The generalizability of Model 1 and Model 2 was further validated in an independent external cohort (n = 92).
resultsModel 1 demonstrated superior performance over individual predictors and existing models (ALT/qHBsAg and Zhang's model), with validation in an independent cohort. Decision curve analysis (DCA) further confirmed Model 1's clinical utility advantage over existing models (ALT/qHBsAg and Zhang's model). Additionally, through receiver operating characteristic (ROC) curve analysis, DCA, and Cox regression, Model 2 outperformed both week-12 HBsAg level and ALT/qHBsAg in predicting HBsAg clearance, providing a robust early on-treatment response indicator.
conclusionsWe propose two clinically applicable models: Model 1 identifies advantaged patients suitable for Peg-IFN-α therapy based on baseline characteristics, while Model 2 predicts treatment response using week-12 dynamics. These tools provide a framework for precision medicine approaches to achieve HBsAg clearance in CHB.
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