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Real-world outcomes of patients with hereditary angioedema with normal C1-inhibitor function and patients with idiopathic angioedema of unknown etiology in Canada

2024· other· en· W6958721604 on OpenAlexaffabout

Bibliographic record

VenueFigshare · 2024
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Diversity and Evolution
Canadian institutionsUniversity of TorontoMcMaster UniversityUniversity of British ColumbiaTakeda (Canada)University of ManitobaUniversity of AlbertaUniversité LavalUniversity of Calgary
Fundersnot available
KeywordsHereditary angioedemaEtiologyAngioedemaC1-inhibitorIcatibantTranexamic acidDanazol

Abstract

fetched live from OpenAlex

Abstract Background Hereditary angioedema with normal C1-inhibitor function (HAE nC1-INH) and idiopathic angioedema of unknown etiology (AE-UNK) are rare conditions that cause recurrent subcutaneous and submucosal swelling. The characteristics and clinical outcomes of patients with these conditions in Canada have not been studied. Methods The aim of this study was to extract real-world evidence from the electronic health records of patients with HAE nC1-INH or AE-UNK who were managed in selected practices of Canadian HAE-treating specialist physicians between 01-Jan-2012 and 01-Jan-2022, to examine case numbers, treatment, clinical outcomes, and healthcare utilization. Results Of 60 patients (37 with HAE nC1-INH, 23 with AE-UNK), median (range) age at symptom onset was 21.5 (5.0–57.0) and 23.0 (10.0–54.0) years, respectively. Time to diagnosis from onset of symptoms was 7.0 (0.0–43.0) and 2.0 (− 10.0 to 50.0) years. Significant differences were observed in terms of the predominant triggers for angioedema attacks between patients with HAE nC1-INH and AE-UNK: stress (65% vs. 26%, p = 0.007) and estrogen therapy (35% vs. 9%, p = 0.031). Before diagnosis, most patients received antihistamines (50% of HAE nC1-INH and 61% of AE-UNK patients). Post-diagnosis, 73% and 74% of HAE nC1-INH and AE-UNK patients received long-term prophylaxis (LTP), with the most common LTP treatments being subcutaneous pdC1-INH (43% of HAE nC1-INH patients and 39% of AE-UNK patients) and tranexamic acid (41% of HAE nC1-INH patients and 35% of AE-UNK patients). Of patients with HAE nC1-INH, and patients with AE-UNK, 22% and 13%, respectively, were taking more than one LTP treatment concurrently. Before HAE treatment initiation, significantly fewer patients with AE-UNK compared to patients with HAE nC1-INH had angioedema attacks affecting their extremities (13% vs. 38%, p = 0.045) and GI system (22% vs. 57%, p = 0.015). In the three months following treatment initiation, patients with AE-UNK experienced significantly fewer angioedema attacks compared to patients with HAE nC1-INH (median 2.0 attacks [0.0–48.0] vs. 6.0 attacks [0.0–60.0], p = 0.044). Additionally, fewer patients with AE-UNK compared to HAE nC1-INH experienced attacks affecting their GI system (26% vs. 57%, p = 0.032). Attack duration and frequency significantly decreased for patients with HAE nC1-INH from a median of 1.00 day (range: 0.00–7.00) to 0.29 day (range: 0.02–4.00; p = 0.001) and from 10.50 attacks (range: 0.00–90.00) to 6.00 attacks (range: 0.00–60.00; p = 0.004) in the three months following HAE treatment initiation. Conclusions Using Canadian real-world evidence, these data demonstrate differing clinical trajectories between patients with HAE nC1-INH and AE-UNK, including diagnostic delays, varied attack characteristics, treatment responses and healthcare utilization. Despite treatment response, many patients still experienced frequent angioedema attacks. These results suggest an unmet need for treatment guidelines and therapies specifically for patients with HAE nC1-INH and AE-UNK and better understanding of the pathophysiology accounting for the reported differences between the two.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.160
Teacher spread0.151 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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