Burden of Treatment on People with Hemophilia: Global Real-World Data
Bibliographic record
Abstract
Background: Prophylaxis lowers bleed risk and facilitates individualized treatment for people with hemophilia (PWH). Prior real-world data from the United States (US) show that treatment burden persists, although is lower with newer therapies (Garcia VC et al. Haemophilia. 2024;30(2):375-387). Aims: The study investigated the remaining clinical needs of people with hemophilia A (HA) and B (HB) receiving prophylaxis, from a global perspective. The current analysis assessed the burden of treatment reported by PWH. Methods: This cross-sectional real-world survey captured patient-reported outcomes, experiences, and clinical data from PWH and parents/guardians of PWH (PPWH) in eight countries (Canada, France, Germany, India, Italy, Saudi Arabia, Spain and the US). Participants were recruited via interaction with treating healthcare professionals, social media, online panels and patient groups. Eligible participants were ≥1 year of age, with HA and ≥6 months of prophylaxis, or HB who were on prophylactic and/or on-demand treatment. Exclusions included mental incapacity, language barriers, clinical trial participation, or receipt of investigative/compassionate treatments. Between December 2023 and March 2024, participants completed a 30-minute online survey. Treatment burden was measured using the adult Hemophilia Treatment Experience Measure (Hemo-TEM), a validated questionnaire covering five domains (injection difficulties, physical impact, treatment bother, interference, and emotional impact). Scores range from 0 to 100 with higher scores indicating greater burden. Here we report treatment burden for people with severe hemophilia at diagnosis without inhibitors (woI) by treatment class: standard half-life (SHL) and extended half-life (EHL) for factor VIII in HA or factor IX in HB, and non-factor therapy (NFT) in HA. Results: Of 495 PWH/PPWH who completed the survey, most were self-completing PWH (n=323, 65%). Mean (standard deviation) age of PWH was 27.1 (17.1) years. Of the 229 people with severe HAwoI 26% (n=59) received SHL, 28% (n=64) EHL, 40% (n=91) NFT and 6% (n=15) unknown. Of the 46 people with severe HBwoI 20% (n=9) received SHL, 78% EHL (n=36) and 2% (n=1) unknown. Across the five Hemo-TEM domains, people with severe HAwoI (n=144) experienced similar degrees of treatment burden regardless of therapy class, with a trend towards reduced burden in PWH from SHL to EHL to NFT. Median scores (interquartile range [IQR]) by treatment class, SHL (n=46)/EHL (n=36)/NFT (n=62) were 17 (0-35)/8 (0-25)/4 (0-17) for injection difficulties, 29 (4-42)/17 (4-35)/8 (0-18) for physical impact, 29 (14-44)/21 (7-36)/13 (4-29) for treatment bother, 44 (13-63)/25 (2-44)/9 (0-25) for interference, and 38 (21-50)/29 (2-46)/21 (7-38) for emotional impact. For people with severe HBwoI (n=26), Hemo-TEM results indicated comparable treatment burden across the five domains. Median scores (IQR) by treatment class, SHL (n=5)/EHL (n=21), were 8 (0-38)/17 (0-42) for injection difficulties, 17 (6-38)/8 (0-31) for physical impact, 14 (9-39/21 (7-39) for treatment bother, 50 (28-66)/19 (6-28) for interference, and 17(13-58)/17 (8-33) for emotional impact. In the Hemo-TEM physical impact domain, of people with severe HAwoI on SHL/EHL/NFT, 43%/22%/19% experienced soreness and 35%/33%/18% reported pain due to their treatment (while injecting or after) at least sometimes (sometimes/often/always). Responses for people with severe HBwoI on SHL/EHL were 40%/19% for soreness and 20%/14% for pain. In the treatment bother domain 33%/11%/6% of people with severe HAwoI reported at least some level of bother (somewhat/very/extremely bothered) for time taken to prepare and administer treatment, 30%/14%/26% for the need to store medication and supplies and 35%/3%/6% for the number of steps it takes to administer treatment. Responses for people with severe HBwoI were 20%/33% for time taken to prepare and administer treatment, 20%/24% for the need to store medication and supplies and 20%/14% for the number of steps it takes to administer treatment. Conclusion: Although current therapies can improve bleed prevention outcomes for PWH, treatment burden remains, manifesting as pain due to therapies and administrative aspects that may negatively affect patient experience. Assessing treatment burden using standard tools is an important part of comprehensive care to individually optimize care and outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".