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Toward Precise Risk Stratification after the Functional Cure of Chronic Hepatitis B.

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  • 1Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea.

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Achieving a functional cure for chronic hepatitis B (HBsAg seroclearance) still carries residual risks. Machine learning models offer improved risk stratification for personalized surveillance after viral clearance.

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Area of Science:

  • Hepatology
  • Virology
  • Machine Learning in Medicine

Background:

  • Chronic hepatitis B is a significant global health issue.
  • Hepatitis B surface antigen (HBsAg) seroclearance represents a functional cure, but residual risks persist.
  • Hepatocellular carcinoma remains a concern even after viral clearance due to host and liver factors.

Purpose of the Study:

  • To address the limitations of conventional models in predicting residual risk after hepatitis B functional cure.
  • To explore the application of machine learning for precise risk stratification in post-cure hepatitis B management.
  • To advocate for a shift towards risk-adaptive monitoring strategies.

Main Methods:

  • Review of current prediction models for residual risk after hepatitis B functional cure.
  • Analysis of recent advances in machine learning for identifying host-driven determinants of long-term outcomes.
  • Discussion of the implications for clinical management and surveillance strategies.

Main Results:

  • Conventional prediction models have limited ability to guide individualized management post-cure.
  • Machine learning approaches demonstrate potential for more accurate risk stratification.
  • These advanced methods can capture complex host factors influencing long-term outcomes.

Conclusions:

  • A functional cure for chronic hepatitis B is not the end of the disease but the start of a new phase.
  • Personalized risk assessment and surveillance are crucial for patients achieving functional cure.
  • Risk-adaptive monitoring strategies are essential to manage residual risks effectively.