Economic Evaluation of Dupilumab Versus Endoscopic Sinus Surgery for the Treatment of Chronic Rhinosinusitis With Nasal Polyps
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Dupilumab is a novel monoclonal antibody that recently received US Food and Drug Administration approval for the treatment of chronic rhinosinusitis with nasal polyps. Endoscopic sinus surgery (ESS) has been the mainstay of treatment for patients refractory to initial medical therapy. Data comparing the cost-effectiveness of these treatments are scarce. The objective of this study is to compare the cost-effectiveness of dupilumab and ESS treatment for patients with chronic rhinosinusitis with nasal polyps refractory to medical therapy. METHODS: A cohort-style Markov decision tree economic evaluation with 10-year time horizon was performed. The two comparative treatment strategies were dupilumab therapy or ESS followed by postoperative maintenance therapy. Patients with response to treatment continued with either maintenance or dupilumab therapy; patients with no response underwent ESS. The primary outcome measure was incremental cost per quality-adjusted life-year calculated from Sino-Nasal Outcome Test (SNOT-22) scores. Sensitivity analyses were performed including discounting scenarios and a probabilistic sensitivity analysis. RESULTS: The dupilumab strategy cost $195,164 and produced 1.779 quality-adjusted life-years. The ESS strategy cost $20,549 and produced 1.526 quality-adjusted life-years. This implies an incremental cost of $691,691 for dupilumab for every 1-unit increase in quality-adjusted life-year compared with ESS. Probability sensitivity analysis indicated that ESS was more cost-effective than dupilumab in all iterations. CONCLUSIONS: While dupilumab and ESS may demonstrate similar clinical effectiveness, ESS remains the most cost-effective treatment option and should remain the standard of care for patients with chronic rhinosinusitis with nasal polyps refractory to medical therapy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it