Insights From the First 820 Patients From the Brazilian Multicenter Registry of Hereditary Angioedema: The Key Role of Genetic Testing and Targeted Therapies
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) is a rare autosomal dominant disorder with a prevalence of 1:50,000 individuals. Delayed diagnosis and deaths from asphyxia still occur. OBJECTIVE: To identify knowledge and management gaps regarding clinical, genetic, and therapeutic aspects of HAE in Brazil, aiming to improve patient care and outcomes. METHODS: A Brazilian multicenter HAE registry was established, with patients' data included by treating physicians using the REDCap (Research Electronic Data Capture) platform. RESULTS: Of the 820 patients with HAE enrolled, 68.8% were female. Most (72.4%) experienced HAE due to C1 inhibitor deficiency (HAE-C1INH), whereas 19.4% had HAE with normal C1INH caused by variants in the F12 gene (HAE-FXII). Onset of symptoms occurred earlier in HAE-C1INH as compared with HAE-FXII (mean 11.2 years vs 19.4 years, respectively), and time for diagnosis was shorter in patients younger than 18 years, as compared with those 18 years and older (mean 1.8 years vs 14.5 years, respectively). Regarding treatment, 52.8% received first-line on-demand therapies (icatibant or plasma-derived C1INH [pdC1INH]). Only 4.8% used first-line options for long-term prophylaxis (LTP), such as lanadelumab or subcutaneous/intravenous pdC1INH. Attenuated androgens were used for LTP in 52% of patients, with adverse effects reported for 34.8%. CONCLUSIONS: Brazilian patients with HAE share common aspects with global patients, including predominance in women, and HAE-C1INH as the most common subtype. Available genetic testing allowed for identification of a notable proportion of HAE-FXII (19.4% of the patients). Despite recent advances, access to first-line therapies for LTP of HAE attacks remains limited.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".