Breakthrough attacks in patients with hereditary angioedema receiving long-term prophylaxis are responsive to icatibant: findings from the Icatibant Outcome Survey
Bibliographic record
Abstract
BACKGROUND: Patients with hereditary angioedema (HAE) due to C1-inhibitor deficiency (C1-INH-HAE) experience recurrent attacks of cutaneous or submucosal edema that may be frequent and severe; prophylactic treatments can be prescribed to prevent attacks. However, despite the use of long-term prophylaxis (LTP), breakthrough attacks are known to occur. We used data from the Icatibant Outcome Survey (IOS) to evaluate the characteristics of breakthrough attacks and the effectiveness of icatibant as a treatment option. METHODS: Data on LTP use, attacks, and treatments were recorded. Attack characteristics, treatment characteristics, and outcomes (time to treatment, time to resolution, and duration of attack) were compared for attacks that occurred with versus without LTP. RESULTS: Data on 3228 icatibant-treated attacks from 448 patients with C1-INH-HAE were analyzed; 30.1% of attacks occurred while patients were using LTP. Attack rate, attack severity, and the distribution of attack sites were similar across all types of LTP used, and were comparable to the results found in patients who did not receive LTP. Attacks were successfully treated with icatibant; 82.5% of all breakthrough attacks were treated with a single icatibant injection without C1-INH rescue medication. Treatment outcomes were comparable for breakthrough attacks across all LTP types, and for attacks without LTP. CONCLUSIONS: 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".