Pharmacotherapies for chronic obstructive pulmonary disease: a multiple treatment comparison meta-analysis
Bibliographic record
Abstract
Edward J Mills1, Eric Druyts1, Isabella Ghement2, Milo A Puhan31Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada; 2Ghement Statistical Consulting Company, Richmond, British Columbia, Canada; 3Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USABackground: Most patients with moderate and severe chronic obstructive pulmonary disease (COPD) receive long-acting bronchodilators (LABA) for symptom control. It is, however, unclear if and what drug treatments should be added to LABAs to reduce exacerbations, which is an important goal of COPD management. Since current guidelines cannot make strong recommendations yet, our aim was to determine the relative efficacy of existing treatments and combinations to reduce the risk for COPD exacerbations.Methods: We included randomized clinical trials (RCTs) evaluating long-acting ß2 agonists (LABA), long-acting muscarinic antagonists (LAMA), inhaled glucocorticosterioids (ICS), and the phosphodiesterase-4 (PDE4) inhibitor roflumilast, and combinations of these interventions in moderate to severe COPD populations. Our primary outcome was the event rate of exacerbations. We conducted a random-effects Bayesian mixed-treatment comparison (MTC) and applied several sensitivity analyses. In particular, we confirmed our findings using a binomial MTC analysis examining whether a patient experienced at least one exacerbation event or not during the trial. We also used an additive assumption to calculate the combined effects of treatments that were not included in the systematic review.Results: Twenty-six studies provided data on the total number of exacerbations and/or the mean annual rate of exacerbations among a combined 36,312 patients. There were a total of 10 treatment combinations in the MTC and 15 in the additive analysis. Compared with all other treatments, the combination of roflumilast plus LAMA exhibited the largest treatment effects, and had the highest probability (45%) of being the best first-line treatment. This was consistent whether applying the incidence rate analysis or the binomial analysis. When applying the additive assumption, most point estimates suggested that roflumilast may provide additional benefit by further reducing exacerbations.Conclusions: Using various meta-analytic approaches, our study demonstrates that depending on the choice of drug, combined treatments offer a therapeutic advantage.Keywords: exacerbations, MTC analysis, clinical trials, roflumilast
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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 | 0.033 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".