Rheumatic adverse events associated with biologic therapy for chronic rhinosinusitis: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Biologic therapies approved for treating chronic rhinosinusitis with nasal polyps (CRSwNP) have well‐established safety profiles but reports of rheumatic adverse events (AEs) are increasing and not well defined. This review aims to assess the risk and incidence of rheumatic AEs associated with biologic therapy in CRSwNP and summarize current reported management strategies. Methods A protocol was registered in PROSPERO [CRD42024525663]. A search was conducted in four electronic databases: Medline (Ovid), Embase, Scopus, and Cochrane CENTRAL from inception until January 4, 2024. Two reviewers independently screened citations and extracted data. Methodological quality was assessed using the Joanna Briggs Institute's critical appraisal tool. Data were pooled using a random effects model to calculate overall incidence and relative risk. Results Twenty‐one studies met the final inclusion criteria, totaling 3434 patients of which 2763 (80%) received either dupilumab (n = 2257; 82%), mepolizumab (n = 372; 13%), or omalizumab (n = 134; 5%) for treatment of CRSwNP. The overall incidence rate for any on‐treatment rheumatic AE was 0.05 per person–year (95% CI, 0.03–0.09, I2 = 75%). Biologic therapy increased the risk of developing a rheumatic AE (RR = 2.53; 95% CI, 1.29–4.94) compared with placebo. The most frequently reported rheumatic AE was arthralgia or joint pain (n = 94; 95%), followed by lupus‐like syndrome or lupus erythematosus‐like reaction (n = 2; 2.5%). Discontinuation of treatment was the most common intervention (n = 21, 39%). Conclusion Biologic therapy increases the risk of rheumatic AEs in CRSwNP patients by over twofold. Healthcare providers should remain vigilant in monitoring rheumatic AEs and apply appropriate management strategies on a case‐by‐case basis.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".