Impact of injectable HAE on-demand treatments on health-related quality of life: a patient and caregiver interview study
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) attacks can be unpredictable, painful, and debilitating. Although studies described the burden of prophylactic HAE treatments on patient and caregiver health-related quality of life (HRQoL), few have explored the effects of injectable on-demand treatments on HRQoL. OBJECTIVE: To understand the impact of injectable on-demand HAE treatments on HRQoL. METHODS: Patients (aged ≥ 12 years) with ≥ 1 HAE attack in the prior 6 months and adult caregivers (aged ≥ 18 years) of patients of any age with HAE were recruited between July and October 2024 to complete a qualitative interview. Questions concerned on-demand injectable treatment use, impacts, and burden. Thematic analysis was used to identify key themes across responses. RESULTS: The 25 study participants from the US and UK (17 patients; 8 caregivers), who completed the interview, highlighted emotional and logistical reasons for delaying or forgoing injectable on-demand treatment, including fear of needles, portability, and complexity of administration. All participants described at least one negative impact on HRQoL, including anxiety and pain associated with treatment administration, disruption of daily activities or work/school days, and impacts on personal relationships. Adolescent patients reported greater impacts than adult patients. Although indicated for self-administration, some adult and all adolescent patients reported needing assistance with administration of their injectable on-demand treatment. All participants expressed interest in an oral on-demand treatment for reasons including portability, pain-free administration, and ability to treat attacks earlier. CONCLUSION: This study highlights the unmet need for an on-demand treatment that allows for earlier, pain-free administration, ultimately increasing patient independence and improving HRQoL for both patients and caregivers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".