Dupilumab efficacy in patients with chronic obstructive pulmonary disease and cardiovascular or metabolic disease: BOREAS and NOTUS
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) and metabolic disease (metD) are common comorbidities in COPD and may modulate clinical outcomes and therapy response. In BOREAS/NOTUS ( NCT03930732 /NCT04456673), add-on dupilumab reduced exacerbations and improved lung function in patients with COPD. Safety was consistent with the known dupilumab safety profile. Aims & objectives: A post hoc analysis to assess dupilumab efficacy in patients with COPD with/without CVD or metD. Methods: Patients with COPD, moderate-to-severe airflow limitation, and type 2 inflammation (screening blood eosinophils ≥300 cells/µL) on triple therapy received add-on dupilumab 300 mg q2w or placebo for 52 weeks. Exacerbation rate and change from baseline in pre-bronchodilator (BD) FEV1 were assessed at Week 52 in the pooled intention-to-treat populations with/without investigator reported CVD or metD. Results: Of 1,874 enrolled patients, 1,253 (66.9%) had a history of CVD and 758 (40.4%) a history of metD. Dupilumab reduced exacerbation rates by 31–33% across all subgroups. Relative risk vs placebo (95%CI) with/without CVD: 0.69 (0.59, 0.82), P<0.001/0.67 (0.51, 0.89), P=0.005; with/without metD: 0.68 (0.54, 0.85), P<0.001/0.69 (0.57, 0.84), P<0.001. Dupilumab also improved pre-BD FEV1 across all subgroups (LS mean difference vs placebo [95%CI] with/without CVD: 55 mL [18, 92], P=0.004/104 mL [42, 166], P=0.001; with/without metD: 62 mL [12, 112], P=0.015/77 mL [35, 119], P<0.001). Conclusion: Dupilumab reduced moderate or severe exacerbation rates and improved lung function in patients with COPD and type 2 inflammation regardless of comorbid CVD or metD.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".