Dynamic predicting hepatitis B surface antigen decline rate during treatment for patients with chronic hepatitis B.

Prediction of hepatitis B surface antigen (HBsAg) decline rates during treatment is crucial for achieving a higher proportion of functional cure outcomes in patients with chronic hepatitis B (CHB), and so is the identification of favorable patients. A total of 371 patients who received pegylated interferon alpha monotherapy or sequential/combined nucleos(t)ide analogues therapy between May 2018 and July 2024 were included for follow-up analysis. The patients were divided into a training set, a validation set and a test set via time series partitioning and random partitioning methods. The primary outcome was the prediction of HBsAg decline rate at each medical visit via linear mixed effects model. Patient stratification was secondary outcomes assessed using group-based trajectory model. The cumulative number of functional cures among 371 patients was 76 (20%, 95% CI: 16%-25%). Three groups, namely rapid high-clearance, delayed high-clearance, and slow low-clearance, were identified by the group trajectory model. The overall accuracy of the time-plus-group dual-effect prediction model was 84% (95% CI: 81%-87%), which was approximately 10% higher than that of the time-effect prediction model after 24 weeks of treatment. When the computational cost was combined, a pragmatic prediction strategy with robust individual prediction performance was obtained. The constructed group trajectory model and prediction strategy may have the potential to dynamically identify favorable patients and dynamically predict the HBsAg decline rate, thereby improving the functional cure rate in clinical practice.