Obstructive Spirometric Index Predicts Lung Function Response to LAMA in asthma: A Post-Hoc Analysis of the CAPTAIN Trial
Bibliographic record
Abstract
Rationale: The Obstructive Spirometric Index (OSI) integrates the forced expiratory volume in 1 second (FEV₁) and FEV₁/forced vital capacity (FEV₁/FVC) to better capture the risk associated with airflow limitation compared to FEV1 and FEV₁/FVC alone. However, its theragnostic value is unclear. Aim: To assess OSI’s predictive value for response to the long-acting muscarinic antagonist (LAMA) umeclidinium (UMEC). Methods: We conducted a post-hoc analysis of CAPTAIN, a double-blind, 24–52-week randomized controlled trial in uncontrolled moderate-to-severe asthma. Participants were factorially randomised to ICS/LABA inhalers with/without UMEC. The interaction between baseline lung function (FEV₁% or OSI) and UMEC (yes/no) was assessed using multivariable models: negative binomial for asthma attacks and linear for ΔFEV₁ at 52 weeks. Results: Among 2,436 patients, neither FEV₁ nor OSI interacted with UMEC for asthma attack risk. In 543 patients with available FEV₁ at 52 weeks, OSI significantly interacted with UMEC for lung function improvement (p=0.01), while FEV₁ did not. Higher OSI was associated with greater lung function improvement following UMEC (Figure). Conclusions: The OSI, which integrates FEV1 and FEV1/FVC, predicts lung function improvement with UMEC, whereas FEV₁ alone does not. OSI may identify treatment opportunities. Funding: GSK/APQ/FRQS/NIHR ( NCT02924688 ). erj;66/suppl_69/PA5764/F1 F1 F1
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".