Factors Associated with Frequent Exacerbations in the UK Severe Asthma Registry
Bibliographic record
Abstract
BACKGROUND: Frequent exacerbations are an important cause of morbidity in patients with severe asthma. OBJECTIVE: Our aim was to identify factors associated with frequent exacerbations in a large well-characterized severe asthma population and determine whether factors differed in patients treated with and without maintenance oral corticosteroids (OCS). METHODS: Adults with severe asthma from specialized asthma centers across the United Kingdom were recruited to the UK Severe Asthma Registry. Demography, comorbidities and physiological measurements were collected. We conducted univariable and multivariable logistic regression analyses to identify factors associated with frequent exacerbations, defined as 3 or more exacerbations treated with high-dose systemic corticosteroids in the past year. RESULTS: Of 1,592 patients with severe asthma from the UK Severe Asthma Registry, 1,137 (71%) were frequent exacerbators and 833 (52%) were on maintenance OCS. The frequent exacerbators were more likely to be ex-smokers, have gastroesophageal reflux disease, higher Asthma Control Questionnaire-6 (ACQ-6) score, and higher blood eosinophilia. Multivariable regression analyses showed ACQ-6 score greater than 1.5 (odds ratio [OR] 4.25; P < .001), past smoking history (OR 1.55; P = .024), and fractional exhaled nitric oxide greater than 50ppb (OR 1.54; P = .044) were independently associated with frequent exacerbations. Past smoking history correlated with frequent exacerbations only in patients on maintenance OCS (OR 2.25; P = .004), whereas ACQ-6 score greater than 1.5 was independently associated with frequent exacerbations in those treated with and without maintenance OCS (OR 2.74; P = .017 and OR 6.42; P < .001, respectively). CONCLUSIONS: Several factors were associated with frequent exacerbations in a large UK severe asthma registry population. High ACQ-6 score had the strongest association with frequent exacerbations irrespective of maintenance OCS status.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".