Addition of PEG-interferon to long-term nucleos(t)ide analogue therapy enhances HBsAg decline and clearance in HBeAg-negative chronic hepatitis B: Multicentre Randomized Trial (PAS Study)
Bibliographic record
Abstract
We studied whether 48 weeks of PEG-IFN alfa-2a add-on increases HBsAg-decline and clearance in HBeAg-negative patients on long-term nucleo(s)tide analogue (NA) therapy. In this investigator-initiated, randomized, controlled trial conducted in Europe and Canada, HBeAg-negative patients treated with NA > 12 months, with HBVDNA < 200 IU/mL, were enrolled. Patients were randomized 2:1 to 48 weeks of PEG-IFN alfa-2a add-on (180 μg per week) or continued NA-monotherapy with subsequent follow-up to Week 72. Endpoints were HBsAg decline (≥1 log10 IU/mL) and HBsAg clearance at Week 48. Of the 86 patients in the modified-intention-to-treat analysis, 58 patients received PEG-IFN add-on, and 28 continued NA monotherapy. At Week 48, 16(28%) patients achieved HBsAg decline ≥1 log10 in the add-on arm versus none on NA-monotherapy (p <.001), and HBsAg clearance was observed in 6 (10%) PEG-IFN add-on patients versus 0% NA-monotherapy (p =.01). HBVRNA was only detected in 2% after PEG-IFN treatment versus 19% in NA-monotherapy (p =.002) at Week 48. PEG-IFN add-on therapy was well tolerated in majority of patients. Low baseline HBsAg levels (<10 IU/mL) identified patients most likely to achieve HBsAg loss with PEG-IFN add-on, whereas an HBsAg level > 200 IU/mL at on-treatment Week 12 was highly predictive of non-response (NPV = 100%). Addition of PEG-IFN to long-term NA enhanced HBsAg decline and increased the chance of HBsAg clearance in HBeAg-negative patients on long-term NA. On-treatment HBsAg levels >200 IU/mL identify patients unlikely to benefit from PEG-IFN add-on and could be used as a potential stopping-rule for PEG-IFN therapy. Our findings support further exploration of immune modulation add-on to antiviral therapy, preferably using response-guided strategies, to increase functional cure rates in patients with CHB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".