Cost-effectiveness of rituximab versus azathioprine for maintenance treatment in antineutrophil cytoplasmic antibody-associated vasculitis.
Bibliographic record
Abstract
OBJECTIVES: Rituximab was proven superior to azathioprine for maintenance treatment of antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). The high cost of rituximab might, however, limit its routine use. This study determined the cost-effectiveness of intravenous rituximab (5 x 500 mg until month 18), versus oral azathioprine (2 mg/kg per day, gradually decreased between month 12 and 22), for maintenance treatment of patients with granulomatosis with polyangiitis, microscopic polyangiitis, or renal-limited vasculitis, aged 18-75. METHODS: We performed a single-trial based economic evaluation. MAINRITSAN was a 28-month multicentre, prospective, randomised, controlled open-label trial. We estimated the cost of healthcare resources and quality of life using prospectively collected data. Healthcare costs were estimated from the perspective of the French Social Health Insurance's perspective, using 2016 tariffs for reimbursement. Utilities were derived from Short Form 36 scores. We estimated total average cost, incremental cost per incremental relapse averted and per quality-adjusted life-year (QALY) gained. Sensitivity analyses were performed to assess uncertainty over relapses, severe adverse events, discount rate, utility weights, time horizon and the cost of rituximab. Costs drivers were tested using a generalised linear model. RESULTS: Total average costs were €13,387 (€11,605-€15,646) and €10,217 (€7,567-12,949) in the rituximab and azathioprine groups respectively. The incremental cost-effectiveness ratio (ICER) was €12,824 per relapse averted and the incremental cost-utility ratio (ICUR) €37,782 per QALY gained. Besides the unit cost of rituximab, the major cost drivers were relapses and severe adverse events. CONCLUSIONS: Maintenance treatment by rituximab could be cost-effective for preventing relapses in patients with AAV.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".