Biological Anti-IL-5/IL-5R Therapeutics for Chronic Obstructive Pulmonary Disease (COPD) with Specific Treatable Traits: A Real-World Retrospective Analysis
Bibliographic record
Abstract
Introduction: We describe the use of anti-IL-5 monoclonal antibodies from a COPD clinic, a source other than traditional clinical trials. The objectives were to characterize the patient subgroup prescribed anti-IL-5 monoclonal antibodies and to report potential benefits. Methods: This is a retrospective case series study of 17 patients treated in a COPD subspecialty clinic. All patients had a diagnosis of COPD (post-bronchodilator FEV1/FVC <0.7) and had been prescribed an anti-IL-5 biologic for at least 8 months. Acute exacerbations of COPD (AECOPDs) were collected as reported in electronic medical records. Results: All patients (17) enrolled were treated with biologics for ≥8 months, and 13 (76%) for ≥1 year. Patients were characterized by severe disease traits, FEV1 <50% predicted, recurrent exacerbations (3.5 moderate-to-severe AECOPDs in the year before treatment), high peripheral blood eosinophil counts (≥250 cells/μL in the previous year), all on inhaled triple therapy, and only 1 patient with a diagnosis of asthma prior to smoking. There was a statistically significant decrease in the exacerbation rate compared with baseline after 8 and 12 months of anti-IL-5 treatment, respectively, yielding the equivalent of a 2-3x reduction in exacerbation rate. Absolute FEV1 decreased, and the decline in FEV1 % of predicted reached statistical significance (p<0.05); CAT score improved (p<0.05). Discussion: This real-world evidence data aligns with existing studies suggesting the potential benefit of anti-IL-5 treatment for specific patients with COPD and therefore advocates for further investigation of RCTs on the use of anti-IL-5 biologics for well-characterized patients with COPD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".