Patient characteristics, biomarkers and exacerbation risk in severe, uncontrolled asthma
Bibliographic record
Abstract
Background Greater precision in asthma exacerbation risk prediction may improve outcomes. We sought to identify clinical characteristics and biomarkers associated with elevated exacerbation risk in patients with severe, uncontrolled asthma. Methods Data were pooled from seven similarly designed phase II and III randomised controlled clinical trials of biologic therapies for the treatment of severe, uncontrolled asthma that enrolled comparable patient populations. Annualised asthma exacerbation rates (AAERs) for patients randomised to placebo were assessed by baseline clinical characteristics, and by biomarker concentrations at baseline and over the study duration. Results The AAER for the 2016 patients in the combined placebo group was 0.91 (95% CI 0.84‒0.98). Baseline characteristics associated with greater AAER were frequent or severe exacerbations within the prior 12 months, nasal polyposis, maintenance oral corticosteroid use, Asian race and Asian or Western European region. AAER increased with baseline blood eosinophil counts and exhaled nitric oxide fraction ( F ENO ) concentration, with the greatest AAER occurring for patients with eosinophils ≥300 cells·μL −1 and F ENO ≥50 ppb. No relationship was observed between baseline serum IgE concentration and AAER. Combining type 2 inflammation criteria for eosinophils and F ENO had greater prognostic value than either biomarker alone. Persistent eosinophil and F ENO elevations throughout the study period were associated with greater AAER. Conclusions Exacerbation history, maintenance corticosteroid use, nasal polyposis, Asian race, geographic region, and elevations in blood eosinophil counts and F ENO concentrations (particularly when combined and/or persistently achieving type 2 inflammation criteria) were associated with increased exacerbation risk in patients with severe, uncontrolled asthma.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".