The effect of dupilumab on lung function parameters in patients with oral corticosteroid-dependent severe asthma
Bibliographic record
Abstract
In Phase 3 LIBERTY ASTHMA VENTURE (NCT02528214), add-on dupilumab reduced oral corticosteroid (OCS) use while reducing severe exacerbations and improving pre-bronchodilator (BD) forced expiratory volume in 1 s (FEV1) in OCS-dependent severe asthma patients. This post hoc study evaluated dupilumab's efficacy based on several lung function parameters for the overall population and subgroups defined by baseline biomarkers. Lung function parameters were pre- and post-BD FEV1, pre-BD forced vital capacity (FVC), FEV1/FVC, and forced expiratory flow 25–75% (FEF25–75%). Dupilumab's steroid-sparing efficacy according to FEV1 improvement (≥ 200 mL vs < 200 mL) or in patients with 0 exacerbations was also assessed. At Week 24 in the overall population, pre- and post-BD FEV1 had improved by 0.22 L (95% CI 0.09–0.34; p = 0.0007) and 0.19 L (95% CI 0.08–0.30; p = 0.0009) vs placebo, respectively; FVC by 0.27 L (95% CI 0.13–0.41; p = 0.0003); FEV1/FVC by 1.90% (95% CI -0.26–4.07; p = 0.08); and FEF25–75% by 0.15 L/s (95% CI 0.02–0.28; p = 0.02). Improvements were early and generally sustained over 24-weeks’ treatment. Improvements were observed regardless of baseline eosinophils or FeNO and were generally greatest in patients with eosinophils ≥ 300 cells/μL. Significant steroid-sparing effects were observed with dupilumab irrespective of FEV1 improvement, and in exacerbation-free patients. Dupilumab rapidly improved various lung function measures regardless of baseline biomarkers in OCS-dependent severe asthma patients. It showed steroid-sparing effects regardless of FEV1 improvement, and in exacerbation-free patients. This population appears to be highly skewed toward type 2 inflammation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.001 | 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".