Lanadelumab’s impact on hereditary angioedema control and quality of life across disease activity subgroups
Bibliographic record
Abstract
BACKGROUND: Real-world clinical data support effectiveness and safety of lanadelumab in patients with hereditary angioedema (HAE); however, disease activity between patients can vary substantially in the absence of long-term prophylactic treatment. OBJECTIVE: To assess the effectiveness of lanadelumab in patients with HAE by baseline HAE attack frequency. METHODS: Patients with HAE from the phase 4 EMPOWER (NCT03845400) and ENABLE (NCT04130191) studies with available baseline attack rate data were included in this post hoc analysis. Disease activity subgroups were defined per pre-enrollment/lanadelumab (baseline) HAE attack rate (low, <1; moderate, ≥1 to <2; high, ≥2 to <3; very high, ≥3 attacks/mo). RESULTS: The analysis included 152 patients (low disease activity, n = 29; moderate, n = 29; high, n = 15; very high, n = 79). In all 4 subgroups, mean and median HAE attack rates after lanadelumab initiation were low (0.0-0.5 attacks/mo). Clinically meaningful improvements (≥6-point decreases) in mean Angioedema Quality of Life total scores were observed regardless of pre-lanadelumab attack rates. From month 1 after lanadelumab initiation to the end of follow-up, mean Angioedema Control Test Scores were 10 or more (indicating patient perception of well-controlled disease) in all 4 subgroups. CONCLUSION: In these real-world data sets, on-treatment lanadelumab attack rates were low regardless of baseline disease activity. Patients from all 4 subgroups experienced improvements in health-related quality of life and disease control. Overall, these findings support long-term prophylaxis with lanadelumab across disease activity levels. TRIAL REGISTRATION: EMPOWER: ClinicalTrials.gov Identifier: NCT03845400; ENABLE: ClinicalTrials.gov Identifier: NCT04130191.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".