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Volume 42 Issue 5
May  2026
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Article Navigation >  Journal of Clinical Hepatology  > 2026  >  42(5): 1038-1047

Construction of a nomogram model for clinical cure of chronic hepatitis B with a low level of hepatitis B surface antigen treated with pegylated interferon α-2b

DOI: 10.12449/JCH260508
  • 1.

    Department of Immunopathy and Hepatology，Yunnan Provincial Clinical Medical Center for Infectious Diseases，The Third People’s Hospital of Kunming，Kunming 650041，China

  • 2.

    Department for Prevention and Control of Endemic and Chronic Non-communicable Diseases，Wangcang County Center for Disease Control and Prevention，Wangcang，Sichuan 628200，China

  • 3.

    Department of Urology，Kunming Children’s Hospital，Kunming 650103，China

Research funding:

Yunnan Provincial Department of Science and Technology Kunming Medical Joint Special Project-General Project (202301AY070001-108);

Yunnan Provincial Science and Technology Planning Project (2017FH001-088);

Kunming Municipal Health Commission Health Research Project (2025-03-08-008)

More Information
  • Corresponding author: ZHANG Huangchenghao， zhanghuangchenghao@163.com （ORCID： 0000-0001-6421-767X）; MOU Chunyan， 784652835@qq.com （ORCID： 0009-0000-7301-3302）; LIU Li， liuli197210@163.com （ORCID： 0000-0001-7712-4931）
  • Received Date: 2025-12-02
  • Accepted Date: 2026-03-03
  • Published Date: 2026-05-25
    Abstract
  •   Objective  To investigate the predictive factors for HBsAg clearance in chronic hepatitis B （CHB） patients with a low level of hepatitis B surface antigen （HBsAg） treated with pegylated interferon α-2b （PEG-IFN-α-2b）， to establish a combined predictive model and a nomogram based on multiple factors， and to provide a reference for formulating individualized treatment regimens and predicting treatment outcome in clinical practice.  Methods  A retrospective analysis was performed for 167 CHB patients with HBsAg <1 500 IU/mL who attended The Third People’s Hospital of Kunming from January 2022 to January 2024 and were treated with PEG-IFN-α-2b. According to whether clinical cure was achieved， the patients were divided into HBsAg clearance group and HBsAg non-clearance group. Related data were collected， including general information and serological/biochemical/virological indicators at different time points during treatment. The independent samples t-test was used for comparison of normally distributed continuous data， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data； the chi-square test was used for comparison of categorical data. The multivariate logistic regression analysis was used to identify independent influencing factors. The receiver operating characteristic （ROC） curve was used to assess the value of indicators used alone or in combination in predicting clinical cure， and calibration curves were plotted to assess the risk prediction model.  Results  The univariate analysis showed that there were significant differences between the two groups in age （t=-6.839， P<0.05）， history of nucleos（t）ide analogue treatment for over 1 year （χ2=59.339， P<0.05）， genotype （χ2=4.610， P<0.05）， nonalcoholic fatty liver disease （χ2=5.319， P<0.05）， hepatitis B virus DNA status before treatment （χ2=60.861， P <0.05）， compensated liver cirrhosis （χ2=10.960， P<0.05）， HBeAg status before treatment （χ2=19.060， P<0.05）， a history of interferon treatment （χ2=8.162， P<0.05）， presence of interferon antibodies after treatment （χ2=12.858， P<0.05）， HBsAg level before treatment （Z=-7.412， P<0.05）， alanine aminotransaminase （ALT） level at baseline （Z=-6.117， P<0.05）， ALT level at 12 weeks of treatment （Z=-7.171， P<0.05）， platelet count （PLT） at 24 weeks of treatment （Z=-3.622， P<0.05）， and thyroid stimulating hormone （TSH） level at 24 weeks of treatment （Z=-2.830， P<0.05）. The multivariate logistic regression analysis showed that age （odds ratio ［OR］=1.230， P=0.007）， history of nucleos（t）ide analogue treatment for over 1 year （OR=0.008， P=0.011）， HBeAg status before treatment （OR=0.003， P=0.012）， HBsAg level before treatment （OR=1.005， P=0.014）， ALT level at baseline （OR=0.949， P=0.014）， ALT level at 12 weeks of treatment （OR=0.969， P=0.016）， PLT at 24 weeks of treatment （OR=0.969， P=0.022）， and TSH level at 24 weeks of treatment （OR=3.608， P=0.045） were independent influencing factors for HBsAg clearance at 48 weeks of treatment in CHB patients with HBsAg <1 500 IU/mL. The Hosmer-Lemeshow goodness-of-fit test yielded χ2=1.398， P=0.994， indicating that the model had good fitting. The Bootstrap method was used to perform internal validation of the nomogram model， and there was a good degree of fitting between the calibration curve and the ideal curve， with a mean absolute error of 0.029. The ROC curve analysis showed that the combination of predictive factors had an area under the ROC curve of 0.982 （95% confidence interval： 0.961 — 0.999）， with a sensitivity of 94.10% and a specificity of 93.10%， suggesting that the nomogram model had a good discriminatory ability. For the CHB patients with HBsAg <1 500 IU/mL and different features， further analysis of HBsAg clearance rate at 48 weeks of treatment showed an HBsAg clearance rate of 69.60% for those with HBsAg ≤67.65 IU/mL before treatment， 58.30% for those with a baseline ALT level of ≥62.50 U/L， 68.30% for those with an ALT level of ≥92.50 U/L at 12 weeks of treatment， 42.40% for those with PLT ≥104×109/L at 24 weeks of treatment， and 48.30% for those with a TSH level of ≤1.38 μIU/mL at 24 weeks of treatment， with significant differences between the two groups （all P<0.001）.  Conclusion  Age， history of nucleos（t）ide analogue treatment for over 1 year， HBeAg status before treatment， HBsAg level before treatment， baseline ALT level， ALT level at 12 weeks of treatment， PLT level at 24 weeks， and TSH level at 24 weeks of treatment are independent predictive factors. The combined prediction nomogram model constructed in this study has a relatively high value in predicting clinical cure at 48 weeks of PEG-IFN-α-2b treatment in CHB patients with HBsAg<1 500 IU/mL， thereby providing a reference for selecting suitable treatment population and predicting clinical cure.

     

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