Cost‐effectiveness analysis comparing dupilumab and aspirin desensitization therapy for chronic rhinosinusitis with nasal polyposis in aspirin‐exacerbated respiratory disease
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis with nasal polyposis (CRSwNP) in the setting of aspirin-exacerbated respiratory disease (AERD) is a disease that is difficult to treat and prone to recurrence. Dupilumab is a promising treatment for these patients, but its cost-effectiveness has not yet been compared with aspirin (acetylsalicyclic acid, or ASA) desensitization, a known and effective treatment. We aimed to compare the cost-effectiveness of ASA desensitization with dupilumab therapy for the treatment of CRSwNP in AERD. METHODS: Analyses of cost-effectiveness, as measured in quality-adjusted life years (QALYs), and cost-utility, as measured in number of required revision endoscopic sinus surgeries (ESSs), were conducted. RESULTS: ASA desensitization after ESS was cost-effective and dominated appropriate medical management. Adding salvage dupilumab was also cost-effective (incremental cost-effectiveness ratio [ICER] $135,517.33), and upfront dupilumab therapy was not cost-effective in any scenario (ICER $273,181.32). The cost-utility analysis demonstrated that, over a 10-year period per patient, appropriate medical management after ESS cost $54,125.31 and resulted in 2.25 revision ESSs, ASA desensitization after ESS cost $53,775.15 and resulted in 2.02 revision ESSs, ASA desensitization with salvage dupilumab cost $121,176.25 and resulted in 1.68 revision ESSs, and upfront dupilumab cost $185,950.34 and resulted in 1.51 revision ESSs. CONCLUSION: Dupilumab for the treatment of severe CRSwNP was found to be cost-effective as salvage therapy under the willingness-to-pay threshold of $150,000. Further analysis highlighted that the cost-effectiveness of dupilumab was most sensitive to drug price and expected gains in quality of life. This suggests that additional investigation into improving patient population selection and tailoring treatment algorithms may improve the cost-effectiveness of dupilumab in specific scenarios.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".