ArticleHepatology international2025
Machine learning prediction of rapid HBsAg seroclearance at week 24 in inactive carriers treated with pegylated interferon.
Article in Hepatology international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Beyond NIHSS and neuroimaging: an interpretable gradient boosting model for predicting in-hospital mortality in ICU patients with acute ischemic stroke.Scientific reports · 2026Article
- Hidden structure, visible effects: uneven genotype missingness and its implications for interpreting Peg-IFN efficacy in CHB trials.Hepatology international · 2026Article
- Commentary on early prediction of HBsAg seroclearance using machine learning in inactive carriers.Hepatology international · 2026Article
- Comment on "Machine learning prediction of rapid HBsAg seroclearance at week 24 in inactive carriers treated with pegylated interferon".Hepatology international · 2026Article
- Commentary on "Machine learning prediction of rapid HBsAg seroclearance at week 24 in inactive carriers treated with pegylated interferon.Hepatology international · 2026Article
- Comment on "Machine learning prediction of rapid HBsAg seroclearance at week 24 in inactive carriers treated with pegylated interferon".Hepatology international · 2026Article
- Construction and validation of a predictive model for HBsAg loss in chronic hepatitis B patients treated with Peg-IFNα-2b.Frontiers in cellular and infection microbiology · 2026Article
Corrections and comments
- Commented on by
- Commented on by
- Commented on by
- Commented on by
Authors and funding
17 authors.
Funding
Abstract
BACKGROUND AND
aimTo identify predictive factors for rapid hepatitis B surface antigen (HBsAg) seroclearance at week 24 in inactive HBsAg carriers (IHC) receiving pegylated interferon alpha-2b (Peg-IFN) therapy, and to develop a machine learning-based model to optimize individualized treatment strategies.
methodsThis retrospective analysis was based on a multicenter, prospective cohort study involving 2882 IHC patients treated with Peg-IFN and followed for at least 24 weeks. Predictive variables for week 24 HBsAg seroclearance were selected using both LASSO regression and the Boruta algorithm. Nine machine learning models were developed, including logistic regression (LR), decision tree (DT), and random forest (RF), with performance assessed via tenfold cross-validation. External validation was conducted in an independent cohort (n = 167) from three medical centers in Beijing. SHapley Additive Explanations (SHAP) were used to interpret model predictions and feature importance.
resultsThe overall HBsAg seroclearance rate at week 24 was 18.7% (541/2,882). Key predictive factors included baseline HBsAg level, ≥ 1 log IU/mL decline in HBsAg at week 12, the ratio of alanine aminotransferase (ALT) to HBsAg at week 12, the ratio of week 12 ALT to baseline HBsAg, week 12 hepatitis B virus (HBV) DNA level, and week 12 hepatitis B surface antibody (HBsAb) level. The Light Gradient Boosting Machine (Light GBM) model demonstrated the best performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.902 (95% CI 0.881-0.923) and a sensitivity of 0.889 in the training cohort, and an AUC of 0.917 (95% CI 0.850-0.983) with a sensitivity of 0.879 in the external validation cohort. SHAP analysis revealed that the week 12 ALT/ HBsAg ratio was the most impactful feature.
conclusionsWe developed a LightGBM-based machine learning model that accurately predicts rapid HBsAg seroclearance at week 24 among IHC patients receiving Peg-IFN therapy. This model offers a valuable tool for early identification of rapid responders, personalized treatment planning, and potential discontinuation strategies. The individualized stopping rules derived from model-predicted probabilities provide an evidence-based approach to precision therapy in IHC patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.