Impact of lanadelumab in hereditary angioedema: a case series of 12 patients in Canada
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) is a rare autosomal dominant disease resulting in recurring episodes of swelling, leading to considerable patient morbidity and mortality. Lanadelumab is a plasma kallikrein inhibitor that is approved as 1st line therapy in Canada for long term prophylaxis of HAE attacks. OBJECTIVE: To describe our clinical findings from a case series of adult patients with HAE type 1/2 who have been initiated on lanadelumab. METHODS: A chart review of HAE type 1/2 patients at three academic centers in Canada was undertaken with demographic and clinical data extracted. Patients were included if they had been receiving lanadelumab for at least 6 months. Patients with other causes of angioedema were excluded. RESULTS: 12 patients meeting enrollment criteria were identified. Compared to pre-lanadelumab, patients had mean reductions of 72% and 62% in attack rate and treated attack rate respectively. 3 patients reported complete remission from attacks after starting lanadelumab. Most patients had significant improvements in HAE impact on social outings. CONCLUSION: Our case series findings support the 2019 International/Canadian HAE guideline that lanadelumab is an effective therapy for long term prophylaxis. In our patient population, initiation of lanadelumab improved disease control, minimized the burden of treatment and improved HAE impact on social outings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".