National survey on clinical and genetic characteristics of patients with hereditary angioedema in Latvia
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) is a rare and life-threatening inborn error of immunity. HAE is mostly caused by pathogenic variations in the serine protease inhibitor gene 1 (SERPING1), leading to deficient or dysfunctional C1-inhibitor (C1-INH), overproduction of bradykinin, and development of recurrent subcutaneous and/or submucosal oedema. The prevalence of HAE is 1 in 50,000 - 100000 people worldwide. We aimed to describe the clinical features and genetic spectrum of hereditary angioedema with C1-INH deficiency (C1-INH-HAE) in Latvia. METHODS: All patients from Latvia diagnosed with HAE (types I/II) from 2006 to March 2022 were included in the study. Laboratory tests and clinical data were analysed, and genetic tests with Sanger sequencing and whole genome sequencing were performed. RESULTS: The study identified 10 C1-INH-HAE patients (nine females, one male) from eight families. The point prevalence of HAE in Latvia is 0.53 per 100 000 inhabitants. Of all patients, seven (70%) had HAE type I and three (30%) had HAE type II. The median age of patients was 54 years and the median age at onset of symptoms was 15 years. A significant delay (median 20.5 years) until diagnosis was observed, and 60% of patients had a positive family history of angioedema. All HAE patients have been hospitalised a median two times during their lifetime. Skin (100%), abdominal (80%), and airway (80%) oedema were the most frequent symptoms. Triggering factors (60%) and prodromal symptoms (90%) were referred. Attacks were severe in 50% of patients, moderate in 10%, and mild in 40%. Pathogenic variations of SERPING1 were identified in eight patients (six families), confirming the diagnosis molecularly. In two patients (two families), no pathogenic variations in the genes were found even after whole genome sequencing. CONCLUSIONS: Current data shows a significant delay and clear underdiagnosis of HAE in Latvia. Higher awareness and better information and communication between doctors would improve the diagnosis and management of HAE; as would screening of family members, patients with recurrent angioedema unresponsive to antihistamines and glucocorticoids, and patients with recurrent episodes of severe, unexplained abdominal pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".