Real-life efficacy of type-2 targeting biologicals in the withdrawal from oral corticosteroid maintenance therapy in severe asthma: A retrospective cohort study
Bibliographic record
Abstract
Introduction Severe corticosteroid-dependent asthma is associated with significant morbidity, partly due to oral corticosteroids (OCS). Biologicals targeting type-2 inflammation have shown efficacy in reducing maintenance OCS while preserving disease control. Whether the 50% reduction in OCS dose observed in phase 3 randomized controlled trials (RCT) is reproducible in the real-life setting is unclear.Methodology We aimed to determine the corticosteroid-sparing effect of mepolizumab, benralizumab and dupilumab after 24 wk in a real-world setting. We conducted a single-center retrospective cohort study comparing liberal weaning from OCS in Sherbrooke with results from protocolized single agent randomized controlled trials in the literature. We selected all adult patients with severe OCS-dependant type-2 asthma treated with biologicals between January 1, 2012 and March 1, 2022.Results Seventy-three patients meeting the inclusion criteria were selected, of which 33 (45%) had asthma-chronic obstructive pulmonary disease overlap. Thirty-nine (53%) received mepolizumab, 30 (41%) on benralizumab and 4 (5%) on dupilumab. In the combined analysis, the median OCS dose reduction at 24 wk was 50% (0–100) (p < 0.0001), and 75% (23–100) (p < 0.0001) at 52 wk; not statistically different from the phase 3 trial result of 50% median reduction. In the per biological agent analysis, the percentages reduction at 24 wk were similar: 33% [0–88] for mepolizumab, 50% (18–100) for benralizumab and 50% (33–100) for dupilumab.Conclusion The efficacy of biologicals to withdraw maintenance OCS in our real-world population was similar to results of dedicated phase 3 RCTs. Our findings imply that the potency of mepolizumab, benralizumab, and dupilumab is evident outside of protocolized weaning protocols.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.001 | 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".