Single-Inhaler Triple vs Long-Acting Beta2-Agonist-Inhaled Corticosteroid Therapy for COPD
Bibliographic record
Abstract
BACKGROUND: -agonist (LABA) and inhaled corticosteroid (ICS) combination with single-inhaler triple therapy that adds a long-acting muscarinic antagonist (LAMA). However, the corresponding trials reported numerically higher incidences of cardiovascular adverse events with triple therapy compared with LABA-ICS. RESEARCH QUESTION: Does single-inhaler triple therapy increase the incidence of major adverse cardiovascular events, compared with LABA-ICS, in a real-world clinical practice setting? STUDY DESIGN AND METHODS: We identified a cohort of patients with COPD aged ≥ 40 years treated during 2017-2021 from the UK's Clinical Practice Research Datalink. Among LAMA-naive patients, initiators of single-inhaler triple therapy were matched 1:1 to LABA-ICS users on time-conditional propensity scores. They were compared on the incidence of major adverse cardiovascular events (MACEs), defined as hospitalization for myocardial infarction or stroke, or all-cause-mortality, over 1 year. RESULTS: The cohort included 10,255 initiators of triple therapy and 10,255 matched users of LABA-ICS. The incidence rate of MACEs was 11.3 per 100 per year with triple therapy compared with 8.8 per 100 per year for LABA-ICS. The corresponding adjusted hazard ratio (HR) of MACEs with triple therapy was 1.28 (95% CI, 1.05-1.55), relative to LABA-ICS; however, the increase was mainly in the first 4 months (HR, 1.41; 95% CI, 1.14-1.74). The HR of all-cause death was 1.31 (95% CI, 1.06-1.62), whereas for acute myocardial infarction and stroke hospitalization it was 1.00 (95% CI, 0.56-1.79) and 1.06 (95% CI, 0.48-2.36), respectively, with triple therapy, relative to LABA-ICS. INTERPRETATION: In a real-world setting of COPD treatment, patients who initiated single-inhaler triple therapy had an increased incidence of MACEs compared with similar patients treated with an LABA-ICS inhaler. This small increase was due to the all-cause mortality component, occurring mainly in the first 4 months after treatment initiation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".