Indirect treatment comparisons of the gene therapy etranacogene dezaparvovec versus extended half‐life factor IX therapies for severe or moderately severe haemophilia B
Bibliographic record
Abstract
INTRODUCTION: Etranacogene dezaparvovec gene therapy for haemophilia B demonstrated superior efficacy at 24 months in reducing bleeds versus a ≥6-month lead-in period of prophylaxis with FIX products in the phase 3 trial, HOPE-B. In the absence of head-to-head comparisons of etranacogene dezaparvovec versus FIX products, indirect treatment comparisons (ITC) can be used. AIM: To compare the efficacy of etranacogene dezaparvovec versus rIX-FP, rFIXFc and N9-GP using ITC, and support HOPE-B results. METHODS: Data were leveraged from Phase 3 pivotal trials: HOPE-B, PROLONG-9FP, B-LONG and Paradigm 2. Annualised bleeding rates (ABR), spontaneous (AsBR) and joint (AjBR) bleeding rates, percentage of patients with no bleeds, and FIX consumption were assessed using inverse probability of treatment weighting and matching adjusted indirect comparisons. RESULTS: Etranacogene dezaparvovec demonstrated statistically significantly lower bleeding rates versus all comparators. Rate ratios for ABR, AsBR and AjBR versus rIX-FP were 0.19 (p < .0001), 0.08 (p < .0001) and 0.09 (p < .0001), respectively. Rate ratios for ABR, AsBR and AjBR versus rFIXFc were 0.14 (p < .0001), 0.13 (p = .0083) and 0.15 (p = .0111), respectively. Rate ratios for ABR and AsBR, versus N9-GP were 0.24 (p = .0231) and 0.13 (p = .0071), respectively. Etranacogene dezaparvovec demonstrated significantly higher percentage of patients with no bleeds versus rIX-FP and rFIXFc; odds ratios: 17.60 (p < .0001) and 5.65 (p = .0037), respectively. Etranacogene dezaparvovec resulted in significantly lower FIX consumption than all comparators. CONCLUSIONS: ITC suggests that etranacogene dezaparvovec offers patients with haemophilia B (≤2% of normal FIX expression) a single dose treatment that can significantly reduce bleeding rates and eliminate routine infusions associated with FIX therapies.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".