Comparative efficacy and safety of monoclonal antibodies biologic therapies for chronic rhinosinusitis with nasal polyps: A systematic review and network meta-analysis
Bibliographic record
Abstract
Chronic rhinosinusitis with nasal polyps (CRSwNP) represents a significant therapeutic challenge with high recurrence rates despite the current standard treatments. Several biologic therapies targeting type-2 inflammation have emerged, but detailed comparisons between these agents are lacking. We conducted a systematic review and network meta-analysis of randomized controlled trials evaluating biologics for CRSwNP. Literature databases were searched up to the date of 28th of April 2025. Primary outcomes included changes in nasal polyp score (NPS), nasal congestion, SNOT-22, and surgery/systemic corticosteroid reduction. Sixteen studies (with total of 3040 patients included) investigating six biologics (dupilumab, omalizumab, mepolizumab, benralizumab, tezepelumab, reslizumab) were included. For NPS reduction, dupilumab showed greatest improvement (−2.44; 95 %CI: −2.85,-2.03), followed by tezepelumab (−2.07; 95 %CI:-2.39,-1.74). Mepolizumab demonstrated superior nasal congestion improvement (−2.64; 95 %CI:-3.24,-2.04). For quality of life, preliminary findings from a single study showed tezepelumab with the greatest SNOT-22 improvement (−27.26; 95 %CI:-32.32,-22.21), however validation in additional trials is needed. Surgery/systemic corticosteroid need was most reduced with tezepelumab in this single study (HR:0.02; 95 %CI:0.00–0.09). Omalizumab had the lowest adverse event rate (49.6 %). Network meta-analysis identified omalizumab and tezepelumab as highest-ranked overall (efficacy/safety combination). Our study demonstrated significant efficacy and safety profiles among biologics for CRSwNP. Treatment selection should consider specific symptom focus and comorbidity patterns, with network meta-analysis suggesting favorable overall profiles for omalizumab, with tezepelumab showing promise but requiring additional validation.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".