Cost‐effectiveness of omalizumab in patients with severe persistent allergic asthma
Bibliographic record
Abstract
BACKGROUND: The health, economic and societal burden of asthma is considerable, and is greatest in patients with severe asthma, particularly when inadequately controlled. Real-life studies that assess the effectiveness of treatment are of particular interest. METHODS: We determined the incremental cost-effectiveness ratio (ICER) of adding omalizumab to standard therapy using data from the real-life 1-year randomized open-label study (ETOPA) and using Canada as a reference country. Only patients receiving high-dose ICS plus LABA were included in the analysis, reflecting the EU label for omalizumab. Costs and quality-adjusted life years (QALYs) gained were used to calculate the ICER for omalizumab (cost/QALY). Probabilistic sensitivity analysis was performed to determine the 95% confidence interval and one-sided sensitivity analyses were performed. RESULTS: The base case lifetime analysis of standard therapy vs standard therapy plus add-on omalizumab for the first 5 years, gave an ICER of 31,209 Euro. Probabilistic sensitivity analysis indicated that the 95% confidence interval around the ICER was 27,739-40,840 Euro. The ICER range for one-way sensitivity analyses was 23,762 Euro without discounting to 66,443 Euro without inclusion of asthma-related mortality. CONCLUSIONS: This study demonstrates that add-on omalizumab therapy is cost-effective in patients with severe persistent allergic asthma.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".