Hereditary Angioedema With Normal C1 Inhibitor: US Survey of Prevalence and Provider Practice Patterns
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) with normal C1-INH (HAE-nl-C1INH) is phenotypically similar to HAE resulting from C1-INH deficiency (HAE-C1INH). Confirmatory diagnostic tests for HAE-nl-C1INH are limited and few clinical study data exist regarding management of the condition. Therefore, survey studies may provide initial estimates of prevalence, diagnosis, and management patterns of this condition. OBJECTIVE: To estimate the prevalence and describe current management patterns for HAE-nl-C1INH in the United States (US). METHODS: We conducted an Internet-based survey of US physicians to estimate the prevalence of the HAE-nl-C1INH population in the United States. Potential participating physicians were identified from the US Hereditary Angioedema Association database and IQVIA Xponent prescription database. Eligible physicians were invited to complete an online survey between June and September 2021. RESULTS: A total of 113 physicians provided data for the estimation of HAE-nl-C1INH prevalence and 81 physicians treating HAE-nl-C1INH patients provided data about treatment patterns. In bias-corrected analysis, we estimated 1,230 to 1,331 HAE-nl-C1INH patients within the United States between May 2019 and April 2020. Mean time to diagnosis for HAE-nl-C1INH was approximately 6 years (range, 2.4-13.5 years). Response to medication was commonly used to inform diagnosis (antihistamine response or nonresponse used by 73% of physician respondents, corticosteroids by 57%, or HAE-specific medications by 74%), and Factor XII genetic testing was used by 43%. CONCLUSIONS: These survey data provide estimates of HAE-nl-C1INH prevalence in the United States as well as current diagnosis and management strategies. Results may be useful for developing studies to assess treatment efficacy and safety, and potentially improve the diagnosis for and management of this patient population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".