Impact of Biologics Initiation on Oral Corticosteroid Use in the International Severe Asthma Registry and the Optimum Patient Care Research Database: A Pooled Analysis of Real-World Data
Bibliographic record
Abstract
BACKGROUND: For severe asthma (SA) management, real-world evidence on the effects of biologic therapies in reducing the burden of oral corticosteroid (OCS) use is limited. OBJECTIVE: To estimate the efficacy of biologic initiation on total OCS (TOCS) exposure in patients with SA from real-world specialist and primary care settings. METHODS: From the International Severe Asthma Registry (ISAR, specialist care) and the Optimum Patient Care Research Database (OPCRD, primary care, United Kingdom), adult biologic initiators were identified and propensity score-matched with non-initiators (ISAR, 1:1; OPCRD, 1:2). The impact of biologic initiation on TOCS (including bursts for exacerbations) daily dose in the first- and second-year follow-up period was estimated using multivariable generalized linear models. RESULTS: Among 5,663 patients (ISAR 48%, OPCRD 52%), the odds ratios (ORs) of biologic initiators achieving TOCS cessation in the first and second years of follow-up were 2.38 (95% CI, 1.87-3.04) and 2.11 (95% CI, 1.65-2.70), whereas the ORs of low (0- to 5-mg) TOCS intake were 1.62 (95% CI, 1.40-1.86) and 1.40 (95% CI, 1.21-1.61), respectively. Compared with non-initiators, biologic initiators had a substantially higher chance of achieving greater than 75% reduction from baseline (OR [95% CI] = 2.35 [2.06-2.68] and 1.53 [1.35-1.73] in first and second years, respectively). These findings remained persistent and robust when analyses were repeated with one country setting removed at a time. CONCLUSIONS: Biologic initiation in patients with SA led to substantial reduction in TOCS exposure, particularly in the first year. Future analyses will explore the impact on OCS-related adverse health events.
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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 | 0.042 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.021 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".