S101 Impact of dupilumab in patients with chronic obstructive pulmonary disease with cardiovascular or metabolic comorbidities: BOREAS and NOTUS trials
Bibliographic record
Abstract
Introduction and Objectives Cardiovascular disease (CVD) and metabolic disease (metD) are common comorbidities in chronic obstructive pulmonary disease (COPD) and may modulate clinical outcomes and therapy response. In BOREAS and NOTUS, add-on dupilumab reduced exacerbations and improved lung function in patients with COPD and type 2 inflammation. Safety was consistent with the known dupilumab safety profile. This post hoc analysis assessed dupilumab efficacy in patients with COPD with/without CVD or metD. Methods BOREAS (NCT03930732) and NOTUS (NCT04456673), phase 3 RCTs, enrolled patients (40– 85 years) with COPD, moderate-to-severe airflow limitation, and type 2 inflammation (screening blood eosinophils ≥300 cells/µL) on triple therapy. Patients received dupilumab 300 mg or placebo q2w for 52 weeks. Endpoints: annualized moderate or severe exacerbation rates, and change from baseline at Week 52 in pre-bronchodilator forced expiratory volume in 1 second (FEV1) and St. George’s Respiratory Questionnaire (SGRQ) total scores (range: 0–100 points; lower scores indicating better quality of life) in the pooled intention-to-treat population with/without investigator-reported CVD or metD. Results Of 1,874 patients, 1,253 (66.9%) had a history of CVD and 758 (40.4%) of metD. Dupilumab reduced exacerbation rates by 31–33%, with relative risk vs placebo (95%CI) of 0.69 (0.59, 0.82; P<0.001) with CVD, 0.67 (0.51, 0.89; P=0.005) without CVD, and 0.68 (0.54, 0.85; P<0.001) with metD, 0.69 (0.57, 0.84; P<0.001) without metD. Dupilumab improved Week 52 pre-bronchodilator FEV1 across all subgroups by LS mean difference vs placebo (95%CI) of 55 mL (18, 92; P=0.004) with CVD, 104 mL (42, 166; P=0.001) without CVD, and 62 mL (12, 112; P=0.015) with metD, 77 mL (35, 119; P<0.001) without metD. Dupilumab also reduced Week 52 SGRQ total scores by LS mean difference vs placebo (95%CI) of −3.1 points (−4.9, −1.2; P=0.001) with CVD, −3.3 points (−6.1, −0.4; P=0.026) without CVD, and −2.5 points (−4.8, −0.1; P=0.040) with metD, −3.9 points (−6.0, −1.9; P<0.001) without metD. All interaction P values for data presented were >0.05. Conclusion Dupilumab reduced moderate or severe exacerbation rates, and improved lung function and quality of life 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".