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Background <p>Hepatitis 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.</p> Methods <p>We retrospectively analyzed 239 chronic hepatitis B (CHB) patients, including 175 patients with baseline HBsAg levels &lt; 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 (<i>n</i> = 92).</p> Results <p>Model 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.</p> Conclusions <p>We 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.</p>

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Noninvasive models for patient selection and Early response monitoring of interferon therapy for HBsAg clearance in patients with chronic hepatitis B

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • Zhaopei Guo,
  • Yan Pan,
  • Lin Lin,
  • Xiangjun Tang,
  • Weiquan You,
  • Fengling Fang,
  • Hongyan Guo,
  • Yiming Zhong,
  • Dina Haishaer,
  • Lu Lai,
  • Yue Shi,
  • Yutong Li,
  • Dong Huang,
  • Kaixin Chen,
  • Yongjie Xu,
  • Ni Lin,
  • Tianbin Chen,
  • Yongbin Zeng,
  • Qishui Ou,
  • Ya Fu

摘要

Background

Hepatitis 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.

Methods

We 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).

Results

Model 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.

Conclusions

We 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.