Higher adherence to biologic therapies in asthma is associated with improved clinical outcomes: Retrospective analysis of claims data
Bibliographic record
Abstract
Background: Treatment adherence is a key factor impacting therapeutic effectiveness in asthma. Aims: To identify biologic treatment adherence groups in severe asthma populations and assess their association with clinical outcomes. Methods: This cross-sectional cohort study analysed US Optum Market Clarity data (2007–2023) for adults diagnosed with asthma ≤12 months before first biologic administration (index date) who had ≥12 months follow-up data. Treatment adherence groups (adherent; partially adherent; minimally adherent; treatment discontinuation) were identified based on the total number of administered doses during follow-up; their associations with exacerbations and with all-cause/asthma-related healthcare resource utilisation (HCRU) were descriptively analysed during follow-up. Results: Overall, 10,088 patients were identified. Treatment adherence across all biologics was low (adherent, 19.8% [n=2000]; partially adherent, 20.3% [n=2045]; minimally adherent, 5.2% [n=528]; treatment discontinuation, 54.7% [n=5515]). Patients with lower adherence had more exacerbations and higher HCRU versus those with higher adherence (Table). Conclusions: The findings show that higher treatment adherence was associated with fewer exacerbations and reduced HCRU, emphasising its critical role in optimising clinical outcomes for patients with severe asthma. Funding: GSK (214570). erj;66/suppl_69/PA2501/F1 F1 F1
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".