Reduced mortality and cardiopulmonary risk for patients with COPD and asthma when initiating single inhaler triple therapy compared to multiple inhaler triple therapy (MITT): MAZI Asthma Study
Bibliographic record
Abstract
COPD is associated with increased risk of mortality and cardiopulmonary (CP) events. COPD patients with asthma have higher risk than with COPD alone. This study investigated all-cause mortality (ACM) and CP risks for budesonide/glycopyrrolate/formoterol fumarate (BGF) compared to MITT among patients with both COPD and asthma. This retrospective study used the Optum Clinformatics® Database. Patients were ≥40 years, ≥2 diagnoses for COPD, ≥1 baseline diagnosis for asthma, initiating BGF or MITT (10/1/2020–6/30/2023), ≥12 months baseline history prior to 1st fill of MITT or BGF (index), without prior triple therapy. Inverse propensity treatment weighting (IPTW) with Cox proportional hazards was used to assess outcomes of time to ACM and first CP event. 2755 BGF and 2732 MITT initiators were identified. After IPTW, patients were well balanced for all baseline characteristics (SMD<0.01). BGF initiators had a 23% lower risk of ACM (adjHR [95% CI]: 0.77 [0.61–0.97]) and 18% lower risk of CP events compared to MITT initiators (adjHR: 0.82 [0.73–0.93]) erj;66/suppl_69/PA1400/F1 F1 F1 . Among patients with COPD and asthma, BGF was associated with lower risk of all-cause mortality and cardiopulmonary events. This study provides real-world evidence for the reduction of these risks using BGF instead of MITT for COPD patients with asthma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".