Combination therapy with long-acting bronchodilators and the risk of major adverse cardiovascular events in patients with COPD: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Accumulated high-quality data from randomised controlled trials (RCTs) indicate that long-acting muscarinic antagonist (LAMA)/long-acting β2 agonist (LABA) combination therapy significantly improves clinical symptoms and health status in patients with chronic obstructive pulmonary disease (COPD) and reduces exacerbation risk. However, there is a growing concern that LAMA/LABA therapy may increase the risk of cardiovascular disease in patients with COPD. The aim of this paper is to determine whether the use of LAMA/LABA combination therapy modifies the risk of cardiovascular disease in patients with COPD. METHODS: Two reviewers independently searched Embase, PubMed and Cochrane Library to identify relevant RCTs of LAMA/LABA or LABA/LAMA/inhaled corticosteroids (ICS) for the management of patients with COPD that reported on cardiovascular end-points. The primary outcome was major adverse cardiovascular events (MACE), which was a composite of cardiovascular death, myocardial infarction or stroke. RESULTS: 1.4%; relative risk 1.29, 95% CI 1.03-1.61) significantly increased the risk of MACE compared with ICS/LABA. The excess risk was most evident in RCTs in which the average underlying baseline risk for MACE was >1% per year. Compared with LAMA only, LABA only or placebo, dual LAMA/LABA therapy did not significantly increase the risk of MACE, though these comparisons may have lacked sufficient statistical power. CONCLUSION: Compared with ICS/LABA, dual LAMA/LABA or triple therapy increases cardiovascular risk in patients with COPD. This should be considered in the context of the incremental benefits of these therapies for symptoms and exacerbation rates in patients with COPD, especially in those with a MACE risk of >1% per year.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".