Real World Biologic Use and Switch Patterns in Severe Asthma: Data from the International Severe Asthma Registry and the US CHRONICLE Study
Bibliographic record
Abstract
INTRODUCTION: International registries provide opportunities to describe use of biologics for treating severe asthma in current clinical practice. Our aims were to describe real-life global patterns of biologic use (continuation, switches, and discontinuations) for severe asthma, elucidate reasons underlying these patterns, and examine associated patient-level factors. METHODS: This was a historical cohort study including adults with severe asthma enrolled into the International Severe Asthma Registry (ISAR; http://isaregistries.org, 2015-2020) or the CHRONICLE Study (2018-2020) and treated with a biologic. Eleven countries were included (Bulgaria, Canada, Denmark, Greece, Italy, Japan, Kuwait, South Korea, Spain, UK, and USA). Biologic utilization patterns were defined: 1) continuing initial biologic; 2) stopping biologic treatment; or 3) switching to another biologic. Reasons for discontinuation/switching were recorded and comparisons drawn between groups. RESULTS: A total of 3531 patients were included. Omalizumab was the most common initial biologic in 2015 (88.2%) and benralizumab in 2019 (29.6%). Most patients (79%; 2791/3531) continued their first biologic; 10.2% (356/3531) stopped; 10.8% (384/3531) switched. The most frequent first switch was from omalizumab to an anti-IL-5/5R (49.6%; 187/377). The most common subsequent switch was from one anti-IL-5/5R to another (44.4%; 20/45). Insufficient efficacy and/or adverse effects were the most frequent reasons for stopping/switching. Patients who stopped/switched were more likely to have a higher baseline blood eosinophil count and exacerbation rate, lower lung function, and greater health care resource utilization. CONCLUSION: The description of real-life patterns of continuing, stopping, or switching biologics enhances our understanding of global biologic use. Prospective studies involving structured switching criteria could ascertain optimal strategies to identify patients who may benefit from switching.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".