Choosing and switching biologics for patients with severe asthma – real life data from the German Asthma Net
Bibliographic record
Abstract
Severe asthma is defined as asthma which requires maximal inhaled corticosteroid dose and an additional medication or oral corticosteroids to remain controlled or is uncontrolled despite this treatment. Over the last two decades six biologics have progressively become available to treat severe asthma. Even if current guidelines do support respiratory physicians in the choice of biologic therapy, there are knowledge gaps in understanding real life practice and its effect on improving asthma control, especially since the arrival of the last biologic, tezepelumab, available in Germany since 2022. We aim to describe how patient characteristics differ depending on whether the initial biologic therapy is anti-IgE, anti-IL5/anti-IL5 receptor, anti-IL4R or anti-thymic stromal lymphopoietin (TSLP) directed, using the German Asthma Net (GAN) registry. In addition, we will analyse prescription practice over time, also depending on the availability of the respective biologics, and frequency, timing and type of switches to a different biologic therapy whenever occurring. Finally, we will describe the change in asthma control parameters (symptom control, exacerbations, lung function) and type 2 inflammation markers (blood eosinophils, exhaled nitric oxyde) depending on the switch constellation. Statistical analyses will include paired tests for each switch configuration as well as regression analyses where applicable. Our results are expected for the start of 2025 and shall add to the growing database of exact phenotyping of patients with severe asthma in order to better tailor their biologic therapy and its possible switch if the initial response remains insufficient. Publication History Article published online: 18 March 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".