Baseline FeNO Independently Predicts the Dupilumab Response in Patients With Moderate-to-Severe Asthma
Bibliographic record
Abstract
Background FeNO may have a role as both a prognostic and predictive biomarker in combination with eosinophils for assessing responsiveness to some biological therapies. Objective We evaluated the value of baseline FeNO, adjusted for baseline blood eosinophil levels and other clinical characteristics, as an independent predictor of treatment response to dupilumab in patients with uncontrolled moderate-to-severe asthma. Methods We performed a post hoc analysis of LIBERTY ASTHMA QUEST (NCT02414854), a phase 3, double-blind study in patients aged 12 years and older with uncontrolled moderate-to-severe asthma, who received dupilumab 200 or 300 mg, or placebo every 2 weeks up to 52 weeks. We assessed the annualized event rate of severe exacerbations and least-squares mean change from baseline in prebronchodilator FEV 1 at weeks 12 and 52 in relationship to baseline FeNO, adjusted for eosinophils and other clinical characteristics. Results The annualized event rate increased with increasing baseline FeNO in placebo and decreased in dupilumab groups. The relative risk of severe exacerbations was 22·7%, 58·3%, and 69·3% lower for dupilumab versus placebo for the FeNO less than 25, 25 to less than 50, and 50 and greater parts per billion subgroups. The magnitude of FEV 1 improvement increased with higher baseline FeNO for dupilumab and was consistent across the continuum of FeNO levels in placebo. Both findings were independent of blood eosinophil levels. Significant differences were observed between FeNO subgroups. Conclusions Increased baseline FeNO was associated with greater clinical effects in dupilumab versus placebo independently of eosinophil levels and other clinical characteristics.
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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 | 0.001 | 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".