Late Breaking Abstract - Dupilumab reduces the risk of severe exacerbations in patients with chronic obstructive pulmonary disease: results from BOREAS and NOTUS
Bibliographic record
Abstract
Background: In patients with COPD, severe exacerbations drive morbidity, healthcare resource utilization (HCRU), and mortality risk. Aims & objectives: This post hoc analysis assessed the time to first severe exacerbation in patients from the BOREAS ( NCT03930732 ) and NOTUS ( NCT04456673 ) trials. Methods: Patients with COPD and type 2 inflammation (screening blood eosinophil count ≥300 cells/µL) received subcutaneous dupilumab (DPL) 300 mg q2w or placebo (PBO) for 52 weeks. Kaplan-Meier estimates for the probability of ≥1 severe exacerbation were analyzed over the 52-week treatment period. Results: In the pooled analysis, 41/938 (4.4%) and 60/936 (6.4%) patients on DPL and PBO, respectively, experienced ≥1 severe exacerbation. At Week 52 in the pooled analysis, the Kaplan–Meier estimate for the probability of ≥1 severe exacerbation was 0.046 (95% CI: 0.034, 0.061) vs 0.068 (95% CI: 0.053, 0.086), for DPL vs PBO, respectively. In NOTUS, there was a nominally significant reduction in the adjusted hazard ratio (HR) for time to first severe exacerbation, with DPL reducing the risk by 49% vs PBO (HR: 0.51 [95% CI: 0.29, 0.90]; nominal P=0.0203). Overall, in the pooled analysis, DPL-treated patients had a nominally significant 39% reduction in risk vs PBO (HR: 0.61 [95% CI: 0.41, 0.91]; nominal P=0.016). Conclusions: Dupilumab treatment was associated with reduced risk of severe exacerbations over 52 weeks (nominally significant), suggesting a potential benefit in lowering disease burden and HCRU in patients with COPD. This despite COVID pandemic restrictions which may have decreased hospital admission rates compared to pre- and post-pandemic lockdowns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".