Exhaled nitric oxide (FeNO) predicts response to prednisolone for asthma attacks in patients on anti-IL5/5Rα therapy
Bibliographic record
Abstract
Introduction: Prednisolone is guideline recommended treatment for asthma attacks but has an uncertain role in patients already treated with anti-IL5/5Rα biologics. We tested the hypothesis that a higher exhaled nitric oxide (FeNO) identifies patients with a better response to prednisolone. Methods: BOOST was a prospective observational study of adults with severe eosinophilic asthma established on anti-IL5/5Rα therapy presenting with an asthma attack. All participants received 7 days 40mg oral prednisolone. Study visits also included stable state, 7 and 28 days after attack. Pre-specified comparison of all outcomes was between FeNO at attack <25 ppb or ≥25ppb. The primary outcome was the proportion of treatment failure after attack (unscheduled healthcare visit or acute treatment for asthma). The secondary outcomes were changes in FEV1, ACQ-5, and VAS symptoms across visits. Exploratory outcomes included sputum type-2 cytokine concentrations. Results: We recruited 60 asthma attacks. 64% were female. 56% were on anti-IL5 and 44% on anti-IL5R therapy. After prednisolone treatment, patients with FeNO-low attacks (n=21) had a higher proportion of treatment failure at day 14 (38% vs 10%; OR 5.21; 95% CI 1.16 to 27.9, p=0.02). FeNO-high participants had greater improvements in FEV1 (mean difference 370ml; 95% CI 113 to 628mL, p=0.006), ACQ-5 (mean difference -1.41; 95% CI -0.70 to -2.11, p<0.001), and significant reductions in sputum IL4 (p=0.03), IL13 (p=0.04), and IL5 (p=0.02) compared to FeNO-low participants. Conclusion: FeNO testing at attack can identify the patients on anti-IL5/IL5Rα treatment who have the most clinical and anti-inflammatory benefit from prednisolone.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".