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Volume 39 Issue 8
Aug.  2023
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Article Navigation >  Journal of Clinical Hepatology  > 2023  >  39(8): 1832-1840

Predictive value of anti-HBs+anti-HBe for seroconversion of HBeAg in patients with positive HBsAg/HBeAg/anti-HBc

DOI: 10.3969/j.issn.1001-5256.2023.08.011
  • a.

    Department of Clinical Laboratory, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei 434000, China

  • b.

    Department of Gynecology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei 434000, China

Research funding:

Jingzhou Science and Technology Plan Project (2022HC68)

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  • Corresponding author: JIANG Tao, jiangtao00358@126.com (ORCID: 0000-0002-0852-9190)
  • Received Date: 2022-12-02
  • Accepted Date: 2023-01-09
  • Published Date: 2023-08-20
    Abstract
  •   Objective  To establish a predictive model for HBeAg seroconversion in patients with positive HBsAg/HBeAg/anti- HBc and anti-HBe, and to investigate the predictive value of this model.  Methods  A total of 6 055 patients with hepatitis B who received the quantification of the serum markers for hepatitis B and HBV DNA in Jingzhou Hospital Affiliated to Yangtze University from April 1, 2018 to August 1, 2022 were enrolled, and according to the pattern of serological markers for hepatitis B, they were divided into negative HBsAg group, positive HBsAg/anti-HBe/anti-HBc group, positive HBsAg/HBeAg/anti-HBc+positive anti-HBe group, positive HBsAg/HBeAg/anti- HBc+positive anti-HBs group, and positive HBsAg/HBeAg/anti-HBc group. The characteristics of different patterns of serological markers were analyzed, and the level of HBeAg was compared between groups. The Kruskal-Wallis H test was used for comparison of non-normally distributed continuous variables between multiple groups; the chi-square test was used for comparison of categorical data between groups. The linear regression analysis was used to analyze the difference in HBeAg content between different groups; the logistic regression analysis was used to screen for independent influencing factors and obtain the optimal predictive factors. The receiver operating characteristic (ROC) curve analysis was used to validate the predictive performance of the model. R Studio4.2.1 was used to establish and validate the predictive model.  Results  Compared with the positive HBsAg/anti-HBe/anti-HBc group, the positive HBsAg/HBeAg/anti-HBc+positive anti-HBe group and the positive HBsAg/HBeAg/anti-HBc+positive anti-HBs group had a significant reduction in the level of HBeAg (both P < 0.01). The multivariate logistic regression analysis showed that anti-HBe was an independent influencing factor for HBeAg seroconversion (P=0.014), and the Lasso regression analysis showed that anti-HBe+anti-HBs was the optimal predictive factor for HBeAg seroconversion in the patients with positive HBsAg/HBeAg/anti- HBc and positive anti-HBe. The ROC curve analysis showed that anti-HBe+ anti-HBs had an area under the ROC curve (AUC) of 0.733 (95% confidence interval: 0.588-0.878, P=0.004 8). Anti-HBe+anti-HBs was included to establish a predictive model, which had good discriminatory ability (AUC=0.733), accuracy (C=0.733, B=0.20, P=0.946), predictive performance, and stability (C=0.726 based on the enhanced Bootstrap test).  Conclusion  The presence of anti-HBs or anti-HBe in patients with hepatitis B promotes the reduction in HBeAg level, and anti-HBe had a stronger ability than anti-HBs in promoting such reduction. Anti-HBe+anti-HBs can be used to predict HBeAg seroconversion in patients with positive HBsAg/HBeAg/anti-HBc+positive anti-HBe.

     

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