A real-world retrospective single-centre study of the cost-effectiveness and long-term outcomes of pegylated interferon for chronic hepatitis B
Bibliographic record
Abstract
Background: Pegylated interferon (Peg-IFN) is recommended as first-line therapy for chronic hepatitis B (CHB) but has significant side effects and is rarely used compared to oral nucleos(t)ide analogues (NA). There are limited recent clinical efficacy or economic analysis data comparing approved CHB therapy in North America. Methods: This retrospective study examined clinical outcomes, off-treatment durability, and cost-effectiveness of Peg-IFN versus NA for CHB. Demographic (age, sex, ethnicity), clinical data (i.e., liver tests, hepatitis B virus DNA, serology, transient elastography) and documented side effects were collected by retrospective chart review of patients followed in the University of Calgary Liver Unit who received Peg-IFN therapy from January 2007 to December 2020. The cost-effectiveness of Peg-IFN versus NA therapy was modelled over a 10-year time horizon. Results: Sixty-eight CHB patients were treated with Peg-IFN (median age 45.65, 74% male, 84% Asian); 50/68 (74%) completed 48 weeks of treatment with a median follow-up of 6.54 years (interquartile range 5.07). At the last known follow-up, 23/68 (34%) have not required NA treatment and one had HBsAg loss; 27 have been started on NA. Predictors of obtaining a sustained virological response included being hepatitis B e antigen-negative at treatment end and a quantitative hepatitis B surface antigen <1000 IU/mL. Economic modelling showed that finite Peg-IFN was not cost-effective versus NA at a 10-year time horizon. Conclusions: PEG-IFN remains a potential treatment for CHB although there is a significant intolerance/failure rate. Using PEG-IFN based on patient preference is reasonable and optimal patient selection may improve treatment cost-effectiveness.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".