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Hepatology
Original research
High accuracy model for HBsAg loss based on longitudinal trajectories of serum qHBsAg throughout long-term antiviral therapy
  1. Rong Fan1,
  2. Siru Zhao1,
  3. Junqi Niu2,
  4. Hong Ma3,
  5. Qing Xie4,
  6. Song Yang5,
  7. Jianping Xie6,
  8. Xiaoguang Dou7,
  9. Jia Shang8,
  10. Huiying Rao9,
  11. Qi Xia10,
  12. Yali Liu11,
  13. Yongfeng Yang12,
  14. Hongbo Gao13,
  15. Aimin Sun14,
  16. Xieer Liang1,
  17. Xueru Yin1,
  18. Yongfang Jiang15,
  19. Yanyan Yu16,
  20. Jian Sun1,
  21. Nikolai V Naoumov17,
  22. Jinlin Hou1
  23. Chronic Hepatitis B Study Consortium
    1. 1 Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Southern Medical University Nanfang Hospital, Guangzhou, China
    2. 2 Hepatology Unit, No. 1 Hospital affiliated to Jilin University, Changchun, China
    3. 3 Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing, China
    4. 4 Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    5. 5 Beijing Ditan Hospital, Capital Medical University, Beijing, China
    6. 6 Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, China
    7. 7 Department of Infectious Diseases, Shengjing Hospital of China Medical University, Shenyang, China
    8. 8 Henan Provincial People's Hospital, Zhengzhou, China
    9. 9 Peking University Hepatology Institute, Peking University People's Hospital, Beijing, China
    10. 10 State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
    11. 11 Beijing Youan Hospital, Capital Medical University, Beijing, China
    12. 12 The Second Hospital of Nanjing, Nanjing, China
    13. 13 8th People's Hospital, Guangzhou, China
    14. 14 Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
    15. 15 Liver Disease Research Center, the Second Xiangya Hospital, Central South University, Changsha, China
    16. 16 Department of Infectious Diseases, First Hospital of Peking University, Beijing, China
    17. 17 Royal College of Physicians, London, UK
    1. Correspondence to Professor Jinlin Hou, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China; jlhousmu{at}163.com; Professor Rong Fan, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China; rongfansmu{at}163.com

    Abstract

    Objective Hepatitis B surface antigen (HBsAg) loss is the optimal outcome for patients with chronic hepatitis B (CHB) but this rarely occurs with currently approved therapies. We aimed to develop and validate a prognostic model for HBsAg loss on treatment using longitudinal data from a large, prospectively followed, nationwide cohort.

    Design CHB patients receiving nucleos(t)ide analogues as antiviral treatment were enrolled from 50 centres in China. Quantitative HBsAg (qHBsAg) testing was prospectively performed biannually per protocol. Longitudinal discriminant analysis algorithm was used to estimate the incidence of HBsAg loss, by integrating clinical data of each patient collected during follow-up.

    Results In total, 6792 CHB patients who had initiated antiviral treatment 41.3 (IQR 7.6–107.6) months before enrolment and had median qHBsAg 2.9 (IQR 2.3–3.3) log10IU/mL at entry were analysed. With a median follow-up of 65.6 (IQR 51.5–84.7) months, the 5-year cumulative incidence of HBsAg loss was 2.4%. A prediction model integrating all qHBsAg values of each patient during follow-up, designated GOLDEN model, was developed and validated. The AUCs of GOLDEN model were 0.981 (95% CI 0.974 to 0.987) and 0.979 (95% CI 0.974 to 0.983) in the training and external validation sets, respectively, and were significantly better than those of a single qHBsAg measurement. GOLDEN model identified 8.5%–10.4% of patients with a high probability of HBsAg loss (5-year cumulative incidence: 17.0%–29.1%) and was able to exclude 89.6%–91.5% of patients whose incidence of HBsAg loss is 0. Moreover, the GOLDEN model consistently showed excellent performance among various subgroups.

    Conclusion The novel GOLDEN model, based on longitudinal qHBsAg data, accurately predicts HBsAg clearance, provides reliable estimates of functional hepatitis B virus (HBV) cure and may have the potential to stratify different subsets of patients for novel anti-HBV therapies.

    • CHRONIC HEPATITIS
    • HEPATITIS B

    Data availability statement

    Data are available on reasonable request. The data that support the findings of this study are available from the corresponding authors on reasonable request.

    https://doi.org/10.1136/gutjnl-2024-332182

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      Data availability statement

      Data are available on reasonable request. The data that support the findings of this study are available from the corresponding authors on reasonable request.

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      Footnotes

      • RF and SZ are joint first authors.

      • Correction notice This article has been corrected since it published Online First. The author affiliation details have been updated.

      • Collaborators Chronic Hepatitis B Study Consortium for detailed list, please see online supplemental material.

      • Contributors JH, RF and NVN contributed to the conception and design of the study. JH and RF coordinated the study. RF, JN, HM, QXie, SY, JX, XD, JShang, HR, QXia, YL, YYang, HG, AS, XL, XY, YJ, YYu, JSun and the other members from chronic Hepatitis B Study Consortium (For detailed list, please see online supplemental material) acquired the data. JH, RF and SZ did the statistical analysis, interpreted the data and verified the underlying data. JH, RF, NVN and SZ prepared the manuscript. All authors contributed to the discissions and interpretation of study results, approved the final manuscript and had final responsibility for the decision to submit for publication. JH and RF are the guarantors.

      • Funding This work was supported by National Key Research and Development Program of China (2022YFC2303600, 2022YFC2304800), the National Natural Science Foundation of China (82170610) and GuangDong Basic and Applied Basic Research Foundation (2023A1515011211).

      • Competing interests JH received consulting fees from GlaxoSmithKline, Gilead Sciences and a Grant from Roche. NVN is an independent advisor to HistoIndex and a member of the scientific advisory board for InSphero. The other authors declare no conflicts of interest that pertain to this work.

      • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

      • Provenance and peer review Not commissioned; externally peer reviewed.

      • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.