Randomised controlled trial for the titration of oral corticosteroids using markers of inflammation in severe asthma
Bibliographic record
Abstract
INTRODUCTION: Biomarkers are used to select biologic therapies for patients with severe asthma, but not to regularly adjust therapy, especially oral corticosteroids (OCS). OBJECTIVE: Our goal was to test the efficacy of an algorithm to guide the titration of OCS using blood eosinophil count and fraction of exhaled nitric oxide (FeNO) levels. DESIGN, PARTICIPANTS, INTERVENTIONS AND SETTING: This proof-of-concept prospective randomised controlled trial assigned adult participants with severe uncontrolled asthma (n=32) to biomarker-based management (BBM) where OCS dose was adjusted based on a composite biomarker score comprised of blood eosinophil count and FeNO, or a standard best practice (SBP) arm. The study was conducted at the Hunter Medical Research Institute, Newcastle, Australia. Participants were recruited from the local Severe Asthma Clinic and were blinded to their study allocation. MAIN OUTCOME: The coprimary outcomes were number of severe exacerbations and time to first severe exacerbation assessed over 12 months. RESULTS: There was a longer median time to first severe exacerbation with BBM, although not significant (295 vs 123 days, Adj. HR: 0.714; 95% CI: 0.25 to 2.06; p=0.533). The relative risk of a severe exacerbation in BBM (n=17) vs SBP (n=15) was 0.88 (Adj.; 95% CI: 0.47 to 1.62; p=0.675) with a mean exacerbation rate per year of 1.2 and 2.0, respectively. There was a significant reduction in the proportion of patients requiring an emergency department (ED) visit using BBM (OR 0.09, 95% CI: 0.01 to 0.91; p=0.041). There was no difference in the cumulative OCS dose used between the two groups. CONCLUSION: A treatment algorithm to adjust OCS using blood eosinophil count and FeNO is feasible in a clinical setting and resulted in a reduced odds of an ED visit. This warrants further study to optimise the use of OCS in the future. TRIAL REGISTRATION NUMBER: This trial was registered with the Australia and New Zealand Clinical Trials Registry (ACTRN12616001015437).
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".