Treatment switch to nonacog beta pegol factor IX in hemophilia B: A Canadian cost-consequence analysis based on real-world factor IX consumption and clinical outcomes
Bibliographic record
Abstract
Background: The Canadian Bleeding Disorders Registry (CBDR) is a source of real-world data for Canadian patients with hemophilia B. Nonacog beta pegol (N9-GP), an extended half-life (EHL) recombinant factor IX (FIX) concentrate, was awarded a Canadian Blood Services contract in 2018 and subsequently made available across Canada (except Québec) to adult patients. For most patients already on another EHL FIX treatment, a switch to N9-GP occurred. Objectives: This study estimates the impact on treatment costs of a switch from a prior FIX to N9-GP based on annualized bleed rates and FIX consumption volumes before and after N9-GP switch from the CBDR. Methods: Real-world data from the CBDR for total FIX consumption and annualized bleed rates were used to inform a deterministic 1-year cost-consequence model. The model considered that the EHL to N9-GP switches were from eftrenonacog alfa and the standard half-life switches were from nonacog alfa. Because FIX prices are confidential in Canada, the model assumed cost parity for annual prophylaxis with each FIX based on the product monograph recommended dosing regimen to calculate an estimated price per international unit for each product. Results: The switch to N9-GP resulted in improvements in real-world annualized bleed rates and therefore reductions in annual breakthrough bleed treatment costs. Switching to N9-GP also resulted in reduced real-world annual FIX consumption for prophylaxis. Overall, annual treatment costs were 9.4% and 10.5% lower after the switch to N9-GP from nonacog alfa and eftrenonacog alfa, respectively. Conclusion: N9-GP improves clinical outcomes and may be cost-saving vs nonacog alfa and eftrenonacog alfa.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".