Exploring Definitions and Predictors of Response to Biologics for Severe Asthma
Bibliographic record
Abstract
Background Biologic effectiveness is often assessed as ‘response', a term which eludes consistent definition. Identifying those most likely to respond in real-life has proven challenging. Objective To explore definitions of biologic responders in adults with severe asthma and investigate patient characteristics associated with biologic response. Methods This was a longitudinal cohort study using data from 21 countries, which shared data with the International Severe Asthma Registry. Changes in 4 asthma outcome domains were assessed in the 1-year period pre- and post-biologic-initiation in patients with predefined level of pre-biologic impairment. Responder cut-offs were: ≥50% reduction in exacerbation rate, ≥50% reduction in long-term oral corticosteroid [LTOCS] daily dose, ≥1 category improvement in asthma control, and ≥100mL improvement in FEV1. Responders were defined using single- and multiple-domains. The association between pre-biologic characteristics and post-biologic-initiation response were examined by multivariable analysis. Results 2,210 patients were included. Responder rate ranged from 80.7% (n=566/701) for exacerbation-response to 10.6% (n=9/85) for 4-domain-response. Many responders still exhibited significant impairment post-biologic-initiation: 46.7% (n=206/441) of asthma control-responders with uncontrolled asthma pre-biologic still had incompletely-controlled disease post-biologic-initiation. Predictors of response were outcome-dependent. Lung function-responders were more likely to have higher pre-biologic FeNO (OR:1.20 for every 25ppb increase), and shorter asthma duration (OR:0.81, for every 10-year increase in duration). Higher BEC and presence of T2-related comorbidities were positively associated with higher odds of meeting LTOCS-, control- and lung function-responder criteria. Conclusion Our findings underscore the multi-modal nature of ‘response', show that many responders experience residual symptoms post-biologic-initiation, and that predictors of response vary according to outcome assessed.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".