A multicenter chart review of patient characteristics, treatment, and outcomes in hereditary angioedema: unmet need for more effective long-term prophylaxis
Bibliographic record
Abstract
Abstract Background Hereditary angioedema (HAE) is a rare disease characterized by unpredictable, recurring subcutaneous or submucosal swelling. Without effective therapy, HAE can negatively impact patients’ quality of life. Management of HAE includes on-demand treatment of attacks and short- and long-term prophylaxis (LTP) to prevent attacks. Newer therapies may be more tolerable and effective in managing HAE; however, therapies such as androgens are still widely used in some countries owing to their relative ease of access and adequate disease control for some patients. This study evaluated the characteristics, treatment patterns, clinical outcomes, and healthcare resource utilization of a multinational cohort of patients with HAE, with a focus on understanding reasons for recommending or discontinuing available therapies. Methods A retrospective chart review was conducted at 12 centers in six countries and included data from patients with HAE type 1 or 2 who were ≥ 12 years of age at their first clinical visit. The relationship between LTP use and attack rates was evaluated using a multivariable Poisson regression model. Data were collected between March 2018 and July 2019. Results Data from 225 patients were collected (62.7% female, 86.2% White, 90.2% type 1); 64.4% of patients had their first HAE-related visit to the center prior to or during 2014. Treatment patterns varied between countries. Overall, 85.8% of patients were prescribed on-demand treatment and 53.8% were prescribed LTP, most commonly the androgen danazol (53.7% of patients who used LTP). Plasma-derived C1 inhibitor (Cinryze®) was used by 29.8% of patients for LTP. Patients who received LTP had a significantly lower rate of HAE attacks than patients who did not receive any LTP (incidence rate ratio (95% confidence interval) 0.90 (0.84–0.96)). Androgens were the most commonly discontinued therapy (51.3%), with low tolerability cited as the most frequent reason for discontinuation (50.0%). Conclusions Overall, findings from this study support the use of LTP in the prevention of HAE attacks; a lower rate of attacks was observed with LTP compared with no LTP. However, the type of LTP used varied between countries, with tolerability and accessibility to specific treatments playing important roles in management decision-making.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".