S162 Emerging biologic use may reduce the need for corticosteroid treatment and surgery for chronic rhinosinusitis with nasal polyps (CRSwNP) in France, Germany, Italy, Spain and the UK
Bibliographic record
Abstract
Introduction and objectives CRSwNP patients often face a prolonged and challenging treatment journey, influenced by regional variations in healthcare systems and treatment practices. This study characterised the clinical management and treatment patterns in CRSwNP patients in routine clinical practice in France, Germany, Italy and Spain (EU4); and the UK. Method This study utilised data from the Adelphi Real World CRS Disease Specific ProgrammeTM, a cross-sectional survey, with retrospective data collection, conducted in the EU4 and UK (Mar-Sep 2023). In total, 176 physicians completed a physician survey and patient record forms for 1050 patients with moderate-to-severe CRSwNP (random group), recurrent CRSwNP with ≥1 previous surgery for polyp removal (surgical group, SG) or severe CRSwNP prescribed biologics (biologic group, BG). Patients voluntarily completed self-reported surveys assessing CRSwNP impact using validated tools (e.g. Sino-Nasal Outcome Test-22 [SNOT-22]) and the Adelphi Adherence Questionnaire. Data were pseudo-anonymously linked, with all information recorded at the survey date from medical history or specific recall period. Results Most patients across EU4 and UK were prescribed CRSwNP-specific treatments (79–95%). Topical nasal corticosteroids were the most prescribed across all countries (58–79%); oral corticosteroid (OCS) use appeared to be slightly lower in the BG (3–22%) versus SG (7–27%). Biologic use was low and varied greatly across countries, with fewest patients receiving biologics in the UK. While all patients in the SG had ≥1 surgery since CRSwNP diagnosis, notably fewer patients in the BG had undergone surgery (23–57%). In the BG, 84% of patients experienced nasal blockage (86% in SG), 62% felt the need to blow their nose (71% in SG) and 57% had a runny nose (60% in SG). Average SNOT-22 score was 1.22 in the BG and 1.57 in the SG, with lower SNOT-22 scores representing reduced CRSwNP-related health burden. Conclusion This cross-sectional study emphasised the persistent OCS use in CRSwNP management, even as biologics are emerging as an alternative treatment option. Despite notable variation in biologic use across countries, the findings suggest a general trend wherein patients receiving biologics reported fewer symptoms and achieved lower SNOT-22 scores, indicative of better disease management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".