M15 Dupilumab efficacy across baseline eosinophil counts in patients with chronic obstructive pulmonary disease with type 2 inflammation
Bibliographic record
Abstract
Introduction and Objectives Type 2 inflammation (indicated by elevated blood eosinophil counts [BEC]) in patients with chronic obstructive pulmonary disease (COPD) often correlates with higher exacerbation risk. In BOREAS and NOTUS, dupilumab significantly reduced exacerbation rates and improved lung function. Safety was consistent with the known dupilumab safety profile. This post hoc analysis evaluated dupilumab efficacy in patients, grouped by baseline BEC ≥150 cells/µL or ≥300 cells/µL. Methods BOREAS (NCT03930732) and NOTUS (NCT04456673), phase 3, randomized, placebo-controlled trials, enrolled 1,874 patients (40–85 years) with COPD, moderate-to-severe airflow limitation, and type 2 inflammation (screening BEC ≥300 cells/µL). Patients were randomized to dupilumab 300 mg or placebo q2w for 52 weeks. Baseline BEC for subgroups was measured at randomization. This analysis included patients from the intention-to-treat population, grouped by baseline BEC ≥150 cells/µL, or ≥300 cells/µL. Endpoints assessed included annualized moderate or severe exacerbation rate, change from baseline to Week 52 in pre-bronchodilator forced expiratory volume in 1 second (FEV1), St. George’s Respiratory Questionnaire (SGRQ), and Evaluating Respiratory Symptoms (E-RS): COPD total scores. Data are shown as least squares mean difference vs placebo (95% CI) at Week 52, unless stated otherwise. Results Of 1,874 patients, 1,703 (dupilumab n=855; placebo n=848) had baseline BEC ≥150 cells/µL, and 1,135 (dupilumab n=573; placebo n=562) had baseline BEC ≥300 cells/µL. Dupilumab reduced annualized exacerbation rates vs placebo by 36.9% (BEC ≥150 cells/µL) and 35.8% (BEC ≥300 cells/µL). Dupilumab improved pre-bronchodilator FEV1 by 76 mL (42, 110; BEC ≥150 cells/µL) and 92 mL (51, 133; BEC ≥300 cells/µL) and reduced patient-reported SGRQ scores at Week 52 by −3.1 points (−4.7, −1.4; BEC ≥150 cells/µL) and −4.0 points (−6.0, −1.9; BEC ≥300 cells/µL), as well as ERS:COPD scores by −1.0 point (−1.5, −0.4; BEC ≥150 cells/µL) and −1.0 point (−1.7, −0.4;BEC ≥300 cells/µL). Conclusions In patients with COPD and type 2 inflammation, dupilumab reduced annualized exacerbation rates and symptom burden, and improved lung function and quality of life, with greater treatment effects observed in those patients with higher baseline BEC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".