Transient early blood eosinophil increases do not affect dupilumab’s long-term efficacy in patients with moderate-to-severe asthma
Bibliographic record
Abstract
Background Transient increases in blood eosinophil count (BEC) have been observed in dupilumab clinical trials but are rarely associated with clinical symptoms. Objective To assess the effect of early increases in BEC on long-term treatment outcomes. Methods Patients aged ≥12 years with moderate-to-severe type 2 asthma from the phase 3 QUEST study (NCT02414854; 52 weeks) who enrolled in the TRAVERSE open-label extension study (NCT02134028; 96 weeks) were stratified by BEC: with/without ≥2-fold BEC increase any time by week 12 of QUEST or presence/absence of increased BEC any time during QUEST (defined as <500 cells/μL at baseline but ≥500 cells/μL at any time point during QUEST). Endpoints included annualized exacerbation rate (AER) and change from parent study baseline (PSBL) in pre-bronchodilator forced expiratory volume in 1 second (FEV 1 ), 5-item Asthma Control Questionnaire (ACQ-5), and type 2 inflammatory biomarkers. Results 36.6% of dupilumab-treated patients versus 21.7% of placebo-receiving patients experienced a ≥2-fold BEC change by week 12, while 31.3% versus 28.0% experienced increased BEC any time during QUEST. Dupilumab versus placebo reduced AER, improved pre-bronchodilator FEV 1 and ACQ-5 scores, and reduced biomarkers across subgroups at week 52 of QUEST. Improvements were maintained in all subgroups through week 96 of TRAVERSE. Conclusions Dupilumab reduced asthma exacerbations and improved lung function and asthma control up to 148 weeks in patients with uncontrolled moderate-to-severe type 2 asthma irrespective of early transient increases in BEC. Overall safety was consistent with the known dupilumab safety profile.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".