QVA149 improves dyspnoea and lung function versus tiotropium in symptomatic patients using LABA/ICS preceding study enrolment: The BLAZE study
Bibliographic record
Abstract
Introduction In the BLAZE study, QVA149 showed superior improvements in dyspnoea (assessed by self-administered computerised-transition dyspnoea index [SAC-TDI]) and lung function vs placebo (PBO) and tiotropium (TIO). 1 In this post-hoc analysis, we report these outcomes from the subgroup population using LABA/ICS prior to study enrolment, primarily because of inappropriate use of ICS, despite not being recommended for patients (pts) with moderate COPD (GOLD B). Method This blinded, crossover study randomised pts with moderate-to-severe COPD (mMRC≥2) to once-daily QVA149 110/50µg, PBO or TIO 18µg. 1 Outcomes reported were SAC-TDI, FEV 1 , FEV 1 AUC 0-4 h , and rescue medication use. Results Of the 247 pts randomised, 82 pts were on prior LABA/ICS therapy. At Wk 6, the proportion of pts who achieved the SAC-TDI score (≥1unit) was higher with QVA149 (39.5%) than PBO (19.0%; odds ratio [OR] 3.16) or TIO (20.0%; OR 3.31; both p<0.01). QVA149 significantly improved mean FEV 1 up to 4h post-dose and FEV 1 AUC 0-4h (table), and reduced mean daily rescue medication use (puffs/day) by 1.53 (p<0.001) and 0.44 (p=0.128) vs PBO and TIO, respectively. LSMTD in FEV 1 and FEV 1 AUC 0-4h , mL Time-points QVA149 vs TIO QVA149 vs PBO Day 1 Wk 6 Day 1 Wk 6 FEV 1 5min 72 94 144 252 30min 72 106 196 294 2h 60 104 196 301 4h 73 84 205 284 FEV 1 AUC 0-4h 71 102 208 293 all p<0.05 Conclusion Improvements in dyspnoea and lung function, and reduction in rescue medication use with QVA149 vs PBO and TIO have been shown in overall population. Similar significant improvements were seen in subgroup population using LABA/ICS prior to study enrolment. Reference 1. Mahler et al. Eur Respir J 2013.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".