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Predicting Immune Flares in Untreated Chronic Hepatitis B Patients Using Novel Risk Factors and the FLARE-B Score
Danny Con1, Daniel Clayton-Chubb2,3, Steven Tu4
1Department of Gastroenterology, Eastern Health, Box Hill Hospital, 8 Arnold Street, Box Hill, Melbourne, Victoria, 3128, Australia. dannycon302@gmail.com.
Insights
Predicting chronic hepatitis B (CHB) immune flares is crucial. New predictors like raised serum globulin and liver stiffness, along with a new score (FLARE-B), help identify high-risk patients for personalized CHB management.
Area of Science:
- Hepatology
- Virology
- Machine Learning in Medicine
Background:
- Chronic hepatitis B (CHB) immune flares are poorly understood.
- Predicting these flares is essential for managing non-cirrhotic, untreated CHB patients.
- Identifying novel risk factors can improve patient outcomes.
Purpose of the Study:
- To discover predictors of CHB immune flares in untreated, non-cirrhotic patients.
- To develop a simple risk-stratifying score (FLARE-B) for CHB flares.
- To compare the efficacy of different machine learning algorithms for flare prediction.
Main Methods:
- Retrospective cohort study of 405 untreated, non-cirrhotic CHB patients with normal baseline ALT.
- Patients were monitored for immune flares (ALT twice the upper limit of normal).
- Statistical and machine learning models were developed and internally validated using bootstrap validation.
Main Results:
- 17% of patients experienced an immune flare within 5 years (annual incidence 4.0%).
- Key predictors identified: raised serum globulin, younger age, HBeAg positive status, higher viral load, and raised liver stiffness.
- The FLARE-B score achieved optimism-adjusted 5-year AUC of 0.702; random survival forest achieved 0.725.
Conclusions:
- Raised serum globulin, raised liver stiffness, and absence of liver steatosis are novel predictors of CHB flares.
- The FLARE-B score can risk-stratify patients.
- FLARE-B may guide personalized CHB management, including monitoring and antiviral treatment decisions.
Background And Aims:
Risk factors of chronic hepatitis B (CHB) immune flares are poorly understood. The primary aim of this study was to discover predictors of the CHB flare in non-cirrhotic, untreated CHB patients and develop a simple risk-stratifying score to predict the CHB flare. The secondary aim was to compare different machine learning methods for prediction.
Methods:
A retrospective cohort of untreated, non-cirrhotic CHB patients with normal baseline ALT was followed up over time until an immune flare as defined by ALT twice the upper limit of normal. Statistical learning and machine learning algorithms were used to develop predictive models using baseline variables. Bootstrap validation was used to internally validate the models.
Results:
Of 405 patients (median age 44y; 41% male, 10% HBeAg positive), 67 (17%) experienced an immune flare by 5 years (annual incidence 4.0%). Predictors of flare included raised serum globulin, younger age, HBeAg positive status, higher viral load and raised liver stiffness. A simple predictive model "FLARE-B" had optimism-adjusted 1, 3 and 5-year AUCs of 0.813, 0.728 and 0.702, respectively. The random survival forest algorithm had the highest optimism-adjusted AUCs of 0.861, 0.766 and 0.725, respectively.
Conclusions:
New, novel predictors of the CHB flare include a raised serum globulin and possibly raised liver stiffness and the absence of liver steatosis. FLARE-B can be used to risk-stratify individuals and potentially guide personalized management strategies such as monitoring schedules and proactive antiviral treatment in high-risk patients.
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