Once-daily QVA149 improves dyspnoea, lung function and reduces rescue medication use in symptomatic patients with COPD using LAMA as prior medication: The BLAZE study
Bibliographic record
Abstract
Introduction The BLAZE study reported the superiority of an approved dual bronchodilator, QVA149 (GOLD group B-D), in terms of improvement in self-administered computerised version of transitional dyspnoea index (SAC-TDI) total score and lung function vs placebo (PBO) and tiotropium (TIO) in patients (pts) with COPD. 1 Here, we present improvements in dyspnoea, lung function, and rescue medication use in the subgroup of pts on prior LAMA therapy. Methods In this blinded, double-dummy, 3-period crossover study, pts (with mMRC≥2) with moderate-to-severe COPD were randomised to once-daily QVA149 110/50µg, PBO or TIO 18µg. Results Of the 247 pts randomised, 115 were on prior LAMA therapy. Of these 115 pts, a higher proportion of pts on QVA149 reported ≥1unit improvement in the SAC-TDI total score (36.5%) vs PBO (16.4%; odds ratio [OR] 3.71; p<0.001) and TIO (20.2%; OR 2.51; p=0.007). At Day 1 and Wk 6, QVA149 provided significant improvements in mean FEV 1 at all assessed time-points and FEV 1 AUC 0–4h vs both PBO and TIO (table). QVA149 significantly reduced (p<0.001) mean daily rescue medication use (puffs/day) by 1.55 vs PBO and by 0.68 vs TIO. Least squares mean treatment difference values of FEV 1 and FEV 1 AUC 0-4h (mL) QVA149 vs PBO QVA149 vs TIO Day 1 Wk 6 Day 1 Wk 6 FEV 1 5min 126 271 65 97 30min 164 301 59 100 2h 206 310 59 112 4h 229 281 74 100 FEV 1 AUC 0-4h 205 305 63 111 All p<0.001 Conclusion In the subgroup of pts on prior LAMA therapy, QVA149 significantly improved SAC-TDI total score and lung function while also reducing rescue medication usage vs PBO and TIO. Reference : 1. Mahler et al. Eur Respir J 2013 Oct 31 (in press).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".