Systematic literature review of the epidemiology and clinical burden of chronic rhinosinusitis with nasal polyposis
Bibliographic record
Abstract
OBJECTIVES: We conducted a systematic literature review (SLR) to determine the epidemiology and clinical burden of chronic rhinosinusitis with nasal polyps (CRSwNP) and to describe how the addition of biologics has affected outcomes for patients with CRSwNP. METHODS: The SLR adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Embase, MEDLINE, and Evidence-Based Medicine Reviews databases were searched using OVID. Relevant studies published between 1 January 2008 and 8 February 2019, for epidemiology, and 1 January 2008 and 16 February 2019, for clinical burden, and relevant conference abstracts from 1 January 2017 to 7 March 2019, for epidemiology and 1 January 2017-16 February 2019 for clinical burden were included. RESULTS: For the epidemiology and clinical burden SLR, 147 and 119 records, respectively, met the inclusion criteria. We found the prevalence of CRSwNP was 1-2.6% and was greater in men. Asthma, allergy, and allergic rhinitis were the most common comorbidities identified. Reported risk factors included asthma, gene polymorphisms, age, and eosinophilia. Studies indicated that dupilumab, mepolizumab, and omalizumab each improved different clinical outcomes. Non-biologics (drugs such as corticosteroids or antibiotics, surgery, or aspirin desensitization) improved clinical outcomes as well. CONCLUSIONS: CRSwNP is fairly prevalent in the general population. Despite the significant efficacy of existing treatments, several unmet needs remain. The high burden of uncontrolled symptoms, frequent recurrence of nasal polyps after surgery, and long-term adverse effects of oral corticosteroids indicate that new therapies addressing these unmet needs should be developed. Although data on biologics from randomized controlled trials look promising, the efficacy of biologics in the real world has yet to be established. The SLR of the epidemiology and clinical burden of CRSwNP revealed key gaps in the literature. There was a paucity of prevalence data across many geographic areas, and no prevalence projections could be determined. Studies showed varying efficacy of non-biologics and no studies directly compared biologics for efficacy. Data regarding clinical efficacy of agents for eosinophilic CRSwNP or severe CRSwNP were lacking, and these patient populations would be served by more trials.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".