Responders to biologics in severe uncontrolled chronic rhinosinusitis with nasal polyps: a multicentric observational real-life study
Bibliographic record
Abstract
BACKGROUND: Clinical trials have demonstrated the effectiveness of biologics in treating chronic rhinosinusitis with nasal polyps (CRSwNP). However, real-world evidence regarding patient outcomes and predictors of clinical response remains limited. METHODOLOGY: In this multicentric 18-month follow-up study, 326 adult patients who initiated biologic therapy for severe uncontrolled CRSwNP were included. Patient characteristics, including clinical and inflammatory markers, and comorbidities were collected at baseline and at 3, 6, 12, and 18 months of follow-up. We examined success rates based on current guidelines and identified potential factors associated to clinical response at 6 months. RESULTS: We observed a significant decrease of Sino-Nasal Outcomes Test-22 (SNOT-22) from a median score (interquartile range) of 60.5 (47-74) at baseline to 26.0 (11-41) at 3 months. A significant decrease of nasal symptoms and endoscopic nasal polyp score was observed at 3 months. After 6 months of biologic treatment, 59% of patients were classified as excellent responders according to the EUFOREA-EPOS 2023 criteria. Multivariate analysis revealed a suggestive association between baseline eosinophil blood count, type of biologic and an excellent response at 6 months. CONCLUSIONS: This real-world study confirms the effectiveness of biologics as an add-on therapy in patients with severe uncontrolled CRSwNP. Biologics lead to rapid and sustained improvement in clinical symptoms. A significant proportion of patients exhibit an excellent response, with no need for systemic corticosteroids.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".