The complexities of decision-making associated with on-demand treatment of hereditary angioedema (HAE) attacks
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) is characterized by debilitating attacks of tissue swelling in various locations. While guidelines recommend the importance of early on-demand treatment, recent data indicate that many patients delay or do not treat their attacks. OBJECTIVE: This survey aimed to investigate patient behavior and evaluate the key factors that drive on-demand treatment decision-making, as reported by those living with HAE. METHODS: People living with HAE were recruited by the US Hereditary Angioedema Association (HAEA) to complete a 20-minute online survey between September 6, and October 19, 2022. RESULTS: Respondents included 107 people with HAE, 80% female, 98% adults (≥ 18 years). Attack management included on-demand therapy only (50%, n = 53) or prophylaxis with on-demand therapy (50%, n = 54). Most patients (63.6%) reported that they did not carry on-demand treatment at all times when away from home. The most common reason for not carrying on-demand treatment when away from home was 'prefer to treat at home' (72.1%). Overall, 86% of respondents reported delaying on-demand treatment, despite recognizing the initial onset of an HAE attack and despite 97% of patients agreeing that it is important to recover quickly from an HAE attack. Reasons for non-treatment or treatment delay included 'the attack is not severe enough to treat' (91.9% and 88.0%, respectively), 'cost of treatment' (31.1% and 40.2%, respectively), anxiety about refilling the prescription for on-demand treatment quickly (31.1% and 37.0%, respectively), the pain (injection or burning) associated with their on-demand treatment (18.9% and 28.3%, respectively), the lack of a suitable/private area to administer on-demand treatment (17.6% and 27.2%, respectively), lack of time to prepare on-demand treatment (16.2% and 16.3%, respectively), and a 'fear of needles' (13% and 12.2%, respectively). Survey findings from the patient perspective revealed that when on-demand treatment was delayed, 75% experienced HAE attacks that progressed in severity, and 80% reported longer attack recovery. CONCLUSIONS: Survey results highlight that decision-making regarding on-demand treatment in HAE is more complicated than expected. The burden associated with current parenteral on-demand therapies is often the cause of treatment delay, despite acknowledgment that delays may result in progression of HAE attacks and longer time to recovery.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".