PP409 Cost-Effectiveness Of Ruxolitinib For Patients With Myelofibrosis: A Review Of The Literature
Bibliographic record
Abstract
Introduction Myelofibrosis (MF) is a rare (annual incidence estimated to be 1/100,000 in Europe), chronic hematologic disorder associated with morbidity and mortality as well as the risk of evolution to acute myeloid leukemia. Ruxolitinib (Jakavi®, Novartis) is the first JAK 1/2 inhibitor approved by the FDA and EMA in 2011 in treating MF. Ruxolitinib is considered a high-cost and life-time treatment. UK-based estimates of the cost of treatment are in the region of GBP43,000/year/patient (in 2013). Against the background of the challenge of treatments for rare diseases reaching cost-effectiveness thresholds, this study identified, collected, and appraised the available evidence on the cost-effectiveness of ruxolitinib in the treatment of MF. Methods A systematic approach was taken to conducting the literature review. Databases searched included PubMed, EMBASE, MEDLINE, and the Cochrane Library based on search terms informed by PICO: myelofibrosis, ruxolitinib, best available therapy/standard of care, and cost-effectiveness/cost-utility/pharmacoeconomics. The search was limited to studies published in the English language. A narrative synthesis was used to evaluate studies and the CHEERS checklist to explore the quality of reporting of the cost-effectiveness analysis. Results The narrative synthesis included five studies conducted in the UK, Portugal, Chile, Canada, and Finland. All cost-effectiveness analyses used data from the same two large, randomized controlled, double-blind, phase III studies (COMFORT-I and -II). Ruxolitinib was compared to the best available therapy (BAT), including hydroxyurea, no medication, and prednisone/prednisolone. Perspectives and included costs varied among analyses. Markov models and discrete state cohort models were used to evaluate the cost-effectiveness and clinical benefit was measured in quality-adjusted life years (QALY) or life years (LY) gained. These analyses estimated the base-case incremental cost-effectiveness ratios (ICER) per QALY of (converted into USD, if appropriate, at the historic average annual exchange rate) GBP44,905 in the UK (2013; USD 70,226), EUR40,000 in Portugal (2016; USD44,272), USD54,500 (2016), CAD61,444 in Canada (2012; USD61,474), and EUR42,367 in Finland (2015; USD42,027). Based upon the cost-effectiveness thresholds applied in each of these countries, ruxolitinib was found to be universally cost-effective, albeit with price adjustments as part of the wider pricing and reimbursement processes used in these countries. Conclusions Ruxolitinib was found to be cost-effective in treating MF informed by different types of models and from different perspectives; however, there was some uncertainty around available data due to limited data sources.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".