A machine learning-based predictive model for 48-week hepatitis B surface antigen seroclearance in chronic hepatitis B patients treated with pegylated interferon α-2b: prediction at week 24.

ABSTRACT:

Background

Chronic hepatitis B (CHB) is an infectious disease mainly affecting the liver, caused by the hepatitis B virus (HBV). In the treatment of CHB, pegylated interferon α-2b (PEG-IFNα-2b) is one of the important therapeutic options. However, there are significant individual differences in patients' responses to this treatment and only a few patients can achieve hepatitis B surface antigen (HBsAg) seroclearance. Therefore, an effective method to identify patients with a high likelihood of favorable response at an early stage is urgently needed.

Methods

In this study, we analyzed data from CHB patients who received antiviral treatment with PEG-IFNα-2b and completed 48 weeks of follow-up in the "OASIS" Project. Patients were divided into the seroclearance group and the no

SUBMITTER: Kong N 

PROVIDER: S-EPMC12689909 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature