Impact of pre-biologic impairment on meeting domain-specific biologic responder definitions in patients with severe asthma
Bibliographic record
Abstract
Background There is little agreement on clinically useful criteria for identifying real-world responders to biologic treatments for asthma. Objective To investigate the impact of pre-biologic impairment on meeting domain-specific biologic responder definitions in adults with severe asthma. Methods This was a longitudinal, cohort study across 22 countries participating in the International Severe Asthma Registry (https://isaregistries.org/) from May 2017 to January 2023. Change in four asthma domains (exacerbation rate, asthma control, long-term oral corticosteroid [LTOCS] dose, and lung function) was assessed from biologic initiation to one year post-treatment (minimum 24 weeks). Pre- to post-biologic changes for responders and non-responders were described along a categorical gradient for each domain derived from pre-biologic distributions (exacerbation rate: 0 to 6+/year; asthma control: well-controlled to uncontrolled; LTOCS: 0 to >30 mg/day; ppFEV1: <50 to ≥80%). Results Percentage of biologic responders (i.e., those with a category improvement pre- to post-biologic) varied by domain and increased with greater pre-biologic impairment, increasing from 70.2 to 90.0% for exacerbation rate, 46.3 to 52.3% for asthma control, 31.1 to 58.5% for LTOCS daily dose, and 35.8 to 50.6% for ppFEV1. The proportion of patients showing improvement post-biologic tended to be greater for anti–IL-5/5R compared to anti-IgE for exacerbation, asthma control, and ppFEV1 domains, irrespective of pre-biologic impairment. Conclusion Our results provide realistic outcome-specific post-biologic expectations for both physicians and patients, will be foundational to inform future work on a multi-dimensional approach to define and assess biologic responders and response, and may enhance appropriate patient selection for biologic therapies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".