Comparative net cost impact of the utilization of romiplostim and intravenous immunoglobulin for the treatment of patients with immune thrombocytopenia in Québec, Canada
Bibliographic record
Abstract
OBJECTIVES: Immune thrombocytopenia (ITP) is an autoimmune disorder characterized by platelet destruction, sub-optimal platelet production, and mild-to-severe bleeding. Nplate® (romiplostim), a thrombopoietin receptor agonist, and intravenous immunoglobulin (IVIg), an expensive and occasionally scarce blood product, are used in the treatment of ITP. The objective of this study was to compare the total cost of treating patients with romiplostim vs IVIg in Québec, Canada. METHODS: A net cost impact model was developed to calculate the annual cost of romiplostim compared with IVIg based on actual practice observations in all patients (n = 95) treated for chronic ITP with IVIg from April 2010 to March 2011 in two participating hospitals. The model included costs of: drug acquisition, drug preparation and administration, patient monitoring, and indirect costs. Healthcare practitioners were consulted regarding romiplostim and IVIg treatment algorithms and the resources involved in patient monitoring. RESULTS: The average annual drug acquisition costs of romiplostim and IVIg were $48,024 and $98,868, respectively. Lower costs for drug preparation and administration ($309 vs $1245) and less time lost from work ($256 vs $2086) were attributed to romiplostim. The cost of follow-up monitoring was the same for both romiplostim and IVIg ($121). The total average annual per patient costs for romiplostim vs IVIg were, respectively, $48,710 and $102,320. The use of romiplostim was projected to save, on average, almost $54,000 per patient per year. LIMITATIONS: The study was conducted in two hospitals in Québec. Romiplostim may show different cost savings in other hospitals and other provincial and national jurisdictions. CONCLUSIONS: Scarce blood products must be used wisely. Romiplostim can allow for improved healthcare resource allocation by reserving IVIg for use in other areas of greater need while also providing cost savings for the overall provincial healthcare budget.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".