Emicizumab prophylaxis in pups and mtps with severe haemophilia: The pednet real world experience in 80 infants
Bibliographic record
Abstract
Abstract Emicizumab (Hemlibra®, Roche) is a humanized bispecific antibody used for subcutaneous prophylaxis in children of any age and adults with haemophilia A (HA). While the HAVEN-7 trial provided primary data on 55 infants with severe HA without FVIII inhibitors, real-world data regarding this age group remains limited. To address this, we analysed prospectively collected data on inhibitor development and bleeding in emicizumab-treated previously untreated patients (PUPs) and minimally treated patients (MTPs; i.e. <6 exposure days (EDs) to FVIII) at PedNet Centres. Data were extracted from the PedNet Registry (a prospective, observational multicentre study collecting data from 34 haemophilia treatment centres in 19 countries), as of January 1, 2025, focusing on children with severe HA without inhibitors who started emicizumab as either PUPs or MTPs, and had received emicizumab prophylaxis for ≥12 weeks. Children included in the HAVEN-7 trial were excluded. Ethical approval was obtained from the parents/guardians of all participants. Key endpoints were inhibitor development, and bleeding rates, including the proportion of children with zero treated bleeds. We analysed 80 infants (39/80 were PUP) with a median follow-up of 84.5 weeks and a median age at emicizumab initiation of 8.6 months (P25-P75 6.7-11.3; range 0.2-26.4). During follow-up, 46/80 infants received FVIII for bleeding episodes (median 3 (P25-P75 1-6; range 1-108 lifetime EDs)) and/or concomitant prophylaxis (n=10). The model-based annualized bleeding rate was 0.6 (95%CI 0.4 – 1.1) with 56% experiencing zero treated bleeds, and no serious adverse events or thromboses were reported. Five infants (2 MTPs) developed FVIII inhibitors (3 high responders (>5.0 BU/mL)) after 4-18 exposure days to FVIII. These data support the safety and efficacy of early emicizumab prophylaxis. However, long-term effects on FVIII inhibitor development require further investigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".