International Variation in Severe Exacerbation Rates in Patients With Severe Asthma
Bibliographic record
Abstract
BACKGROUND: Exacerbation frequency strongly influences treatment choices in patients with severe asthma. RESEARCH QUESTION: What is the extent of the variability of exacerbation rate across countries and its implications in disease management? STUDY DESIGN AND METHODS: We retrieved data from the International Severe Asthma Registry, an international observational cohort of patients with a clinical diagnosis of severe asthma. We identified patients aged ≥ 18 years who did not initiate any biologics prior to baseline visit. A severe exacerbation was defined as the use of oral corticosteroids for ≥ 3 days or asthma-related hospitalization/ED visit. A series of negative binomial models were applied to estimate country-specific severe exacerbation rates during 365 days of follow-up, starting from a naive model with country as the only variable to an adjusted model with country as a random-effect term and patient and disease characteristics as independent variables. RESULTS: The final sample included 7,510 patients from 17 countries (56% from the United States), contributing to 1,939 severe exacerbations (0.27/person-year). There was large between-country variation in observed severe exacerbation rate (minimum, 0.04 [Argentina]; maximum, 0.88 [Saudi Arabia]; interquartile range, 0.13-0.54), which remained substantial after adjusting for patient characteristics and sampling variability (interquartile range, 0.16-0.39). INTERPRETATION: Individuals with similar patient characteristics but coming from different jurisdictions have varied severe exacerbation risks, even after controlling for patient and disease characteristics. This suggests unknown patient factors or system-level variations at play. Disease management guidelines should recognize such between-country variability. Risk prediction models that are calibrated for each jurisdiction will be needed to optimize treatment strategies.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".