Pain‐Related Quality of Life Outcomes in People With Haemophilia A Receiving Emicizumab: A Post Hoc Analysis of the HAVEN 1, 3 and 4 and STASEY Studies
Bibliographic record
Abstract
INTRODUCTION: People with haemophilia A (PwHA) experience acute and chronic pain associated with reduced quality of life (QoL). AIMS: This post hoc analysis of pooled data from the HAVEN 1 (NCT02622321), 3 (NCT02847637), 4 (NCT03020160) and STASEY (NCT0319179) studies assessed the impact of emicizumab prophylaxis on pain-related QoL in PwHA. METHODS: PwHA received emicizumab during the four studies. In this analysis, pain was assessed using patient-reported responses to pain-specific questions from the Haem-A-QoL/Haemo-QoL-SF and the pain/discomfort dimension of the EQ-5D-5L. Responses were recorded at baseline and at regular intervals for up to 78 weeks following treatment initiation. Additional analyses evaluated the population with target joints at baseline, and the overall population stratified by age, factor (F)VIII inhibitor status and prior treatment. RESULTS: At the data cut-off, 504 PwHA had been treated across the four studies; 464 and 470 completed the Haem-A-QoL/Haemo-QoL-SF and the EQ-5D-5L, respectively. Improvements in pain-related QoL were observed by Week 13 of emicizumab prophylaxis and maintained through Week 78. In the overall population, responses of 'never/rarely' for 'my swellings hurt' and 'pain in joints' increased from 37.0% and 30.0% at baseline to 84.0% and 61.0% by Week 13, suggesting reductions in acute and chronic pain, respectively. Similar improvements were seen in the target joint population, and across all strata. Greater improvements were observed in younger versus older PwHA. CONCLUSION: Pain-related QoL improved with emicizumab prophylaxis regardless of target joints, age, FVIII inhibitor status or prior treatment. Haemophilia-specific assessments are needed to accurately capture and characterize pain in PwHA.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".