Real-world biomarker variability and effects of biologics on severe asthma in Alberta
Bibliographic record
Abstract
Rationale: Guidelines recommend biomarker testing to phenotype patients with severe asthma (SA), to guide treatment. However, biomarker levels fluctuate, and individual responses to biologics are not fully understood.Objectives: This study estimated the proportion of patients experiencing biomarker variability and the real-world effectiveness of biologics in SA.Methods: A population-based retrospective cohort study was conducted using administrative data from Alberta, Canada (April 1, 2010 to March 31, 2020) for patients with SA. Year-to-year variability in biomarker levels was assessed using clinical thresholds (blood eosinophil count [EOS] ≥ 300 cells/µL; immunoglobin E [IgE] ≥ 30 IU/mL) to explore category switching. Incidence rate ratios of exacerbations were estimated by biomarker level. Associations between follow-up time with biologics exposure (bio-experience), biomarker levels and exacerbation rates were modeled.Results: Up to 28% of SA patients displayed year-over-year biomarker threshold switching for EOS and up to 12% for IgE. Severe exacerbation rates were higher with blood EOS count ≥300cells/µL or IgE ≥30 IU/mL. Bio-experience was associated with lower blood EOS versus pre-bio-experience (relative mean EOS: 0.53 [0.42-0.68]), persisting >1 year following discontinuation. Bio-experience was associated with a nearly 50% reduction in exacerbation risk after initiating biologics (IRR = 0.54 [0.48, 0.62]), regardless of comorbid status.Conclusions: Higher biomarker levels were associated with higher exacerbation rates, but a proportion of patients demonstrated variability, crossing clinical thresholds. Treatments with upstream agents targeting multiple pathways of inflammation may circumvent this issue. Biologics had real-world effectiveness in reducing SA biomarkers. Their persistent temporal effect provides a groundwork for exploring cost-effective biologics use.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".