Real-World Effectiveness of Lanadelumab in Hereditary Angioedema: Multicountry INTEGRATED Observational Study
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) is a rare genetic disease characterized by recurrent episodes of cutaneous or subcutaneous edema. There is clinical need for treatments that reduce the rate of HAE attacks in patients. OBJECTIVES: Primary objectives were to evaluate the effectiveness of lanadelumab on attack-free rate (AFR; proportion of patients who had zero HAE attacks), and of every 2-week and every 4-week adjustments on AFR. METHODS: A retrospective medical chart review study was conducted in 19 HAE centers and included data from patients with type I or II HAE treated with lanadelumab (index treatment) in Germany, France, Greece, and Austria who were aged 12 years or older (ClinicalTrials.gov identifier: NCT04861090). Data abstraction occurred September 15, 2021, to June 29, 2022. Analyses were primarily descriptive. RESULTS: Data from 198 patients were collected (61.6% female, 91.9% with type I HAE). Lanadelumab treatment patterns varied between countries. Cumulative AFR improved from 0% (preindex) to 54.4% (12 months postindex) and 39.4% (postindex; median duration, 28.8 months). Monthly AFRs varied from 16.2% to 28.3% preindex (17.7% AFR in the month before index date), and from 82.7% (month 1) to more than 95% at multiple time points between 26 and 43 months postindex. Patients with interval increases (n = 144 [72.7%]) showed improved cumulative AFR (0% preindex to 50.0% postindex). CONCLUSIONS: This real-world study demonstrates that lanadelumab long-term prophylaxis is effective in improving AFR in patients with type I/II HAE on every 2-week dosing and dose interval increases. Effectiveness with lanadelumab is rapid and was observed starting from the first month of starting therapy.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".