Baseline Characteristics and ICS/LAMA/LABA Response in Asthma: Analyses From the CAPTAIN Study
Bibliographic record
Abstract
Background Findings from CAPTAIN (NCT02924688) suggest treatment response to fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) differs according to baseline type 2 (T2) inflammation markers in patients with moderate-to-severe asthma. Understanding how other patient physiologic and clinical characteristics affect response to inhaled therapies may guide physicians toward a personalized approach for asthma management. Objective To investigate, using CAPTAIN data, the predictive value of key demographic and baseline physiologic variables in patients with asthma (lung function, bronchodilator reversibility, age, age at asthma onset) on response to addition of the long-acting muscarinic antagonist UMEC to inhaled corticosteroid/long-acting β 2 -agonist combination FF/VI, or doubling FF dose. Methods Prespecified and post hoc analyses of CAPTAIN data were performed using categorical and continuous variables of key baseline characteristics to understand their influence on treatment outcomes (lung function [trough forced expiratory volume in 1 second, FEV 1 ], annualized rate of moderate/severe exacerbations, and asthma control [Asthma Control Questionnaire, ACQ]) following addition of UMEC to FF/VI or doubling FF dose in FF/VI or FF/UMEC/VI. Results Adding UMEC to FF/VI led to greater improvements in trough FEV 1 versus doubling FF dose across all baseline characteristics assessed. Doubling FF dose was generally associated with numerically greater reductions in the annualized rate of moderate/severe exacerbations compared with adding UMEC, independent of baseline characteristics. Adding UMEC and/or doubling FF dose generally led to improvements in ACQ scores irrespective of baseline characteristics. Conclusion Unlike previous findings with T2 biomarkers, lung function, bronchodilator reversibility, age and age at asthma onset do not appear to predict response to inhaled therapy.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".