Biologic use and outcomes among adults with severe asthma treated by US subspecialists
Bibliographic record
Abstract
BACKGROUND: Multiple biologics are now available for severe asthma (SA) treatment and can improve outcomes for patients. However, few available data describe the real-world use and effectiveness of multiple approved biologics, including biologic switching, among subspecialists in the United States. OBJECTIVE: To evaluate biologic use and associated exacerbation outcomes in a large cohort of subspecialist-treated US adults with SA. METHODS: CHRONICLE is an ongoing, noninterventional study of subspecialist-treated US adults with SA receiving biologics, maintenance systemic corticosteroids, or those persistently uncontrolled by high-dose inhaled corticosteroids with additional controllers. For enrolled patients, sites report asthma exacerbations and medication use starting 12 months before enrollment. For patients enrolled between February 2018 and February 2021, biologic use and exacerbation outcomes before and after biologic initiation are described. RESULTS: Among 2793 enrolled patients, 66% (n = 1832) were receiving biologics. The most used biologic (> 1 biologic use per patient allowed) was omalizumab (47%), followed by benralizumab (27%), mepolizumab (26%), dupilumab (18%), and reslizumab (3%). Overall, 16% of patients had biologic switches, 13% had stops, and 89% had ongoing biologic use. Patients starting and switching biologics experienced a 58% (1.80 vs 0.76 per patient-year) and 49% (1.47 vs 0.75 per patient-year) reduction in exacerbations, respectively (both P < .001), with a numerically greater reduction observed among those starting non-anti-immunoglobulin E biologics compared with anti-immunoglobulin E. CONCLUSION: Real-world starting and switching of biologic therapies for SA were associated with meaningful reductions in exacerbations. With increasing biologic options available, individualized approaches to therapy may improve patient outcomes. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03373045.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".