Machine learning prediction of functional cure to pegylated interferon-alpha therapy in two HBV populations: Advantaged populations and HBeAg-negative patients.

ABSTRACT: Despite advances in antiviral therapy, the rate of functional cure for chronic hepatitis B (CHB) remains unsatisfactory, and developing an applicable prediction model is pivotal to improving it. Thus, we aimed to identify key predictive factors and develop prognostic models for functional cure in HBeAg-negative patients and the advantaged populations. This retrospective study included 202 HBeAg-negative CHB patients (114 classified as advantaged populations) receiving pegylated interferon-alpha (PEG-IFNα) therapy for model derivation and internal validation, and 183 HBeAg-negative CHB patients (117 classified as advantaged populations) for model external validation. Using 48 routinely collected clinical indicators, we constructed prediction models through LASSO regression followed by multi

SUBMITTER: Li J 

PROVIDER: S-EPMC12915860 | biostudies-literature | 2026 Dec

REPOSITORIES: biostudies-literature