Real-world outcomes in hereditary angioedema: first experience from the Icatibant Outcome Survey in the United Kingdom
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) is a potentially life-threatening, bradykinin-mediated disease, often misdiagnosed and under-treated, with long diagnostic delays. There are limited real-world data on best-practice management of HAE in the UK. OBJECTIVES: To characterize the clinical profile, management and outcomes of patients with HAE type I and II from three specialist centres in the UK using data from the Icatibant Outcome Survey (IOS; Shire, Zug, Switzerland), an international observational study monitoring safety and effectiveness of icatibant, a selective bradykinin B2 receptor antagonist. METHODS: We performed retrospective analyses of IOS data for patients with HAE type I and II from three centres in the UK and compared UK data with pooled IOS data from 10 countries (48 centres). RESULTS: Analyses included 73 UK and 579 non-UK patients with HAE type I or II. Median diagnostic delay was 6.2 and 5.9 years, respectively. Analysis of data collected from February 2008 to July 2016 included 286 icatibant-treated attacks in 58 UK patients and 2553 icatibant-treated attacks in 436 non-UK patients (median of 3.0 attacks per patient in both groups). More attacks were treated by icatibant self-administration in UK patients (95.8%) than in non-UK patients (86.8%, p < 0.001). Time to icatibant treatment, time to resolution and attack duration were not significantly different in the UK versus non-UK patients. CONCLUSION: ClinicalTrials.gov NCT01034969.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".