Glucarpidase for Treating Adults with Delayed Methotrexate Elimination Due to Impaired Renal Function: An Economic Simulation Analysis
Bibliographic record
Abstract
Background: Glucarpidase is indicated for treating delayed methotrexate (MTX) elimination due to impaired renal function. Although glucarpidase is capable of rapidly eliminating MTX independent of renal clearance, its cost can be perceived as a barrier to use. However, no published economic analyses have evaluated glucarpidase relative to comparable treatments. Purpose: To assess the economic value of glucarpidase for treating adult patients in the United States (US) who experience delayed MTX elimination due to impaired renal function. Methods: A decision tree model was developed to assess the economic value of glucarpidase. The short-term inpatient management of patients as well as long-term survival were simulated. Costs associated with the use of glucarpidase were compared against other methods for treating delayed MTX elimination due to impaired renal function under two scenarios: current practice (ie, mix of timely/delayed use of glucarpidase, hemodialysis, or supportive care [SC] alone) as compared with proposed practice (ie, timely glucarpidase administration within 60 hours for all eligible patients). Hypothetical practical scenarios for US institutions were also considered. Results: For adult patients with delayed MTX elimination, proposed practice as compared to current practice was associated with an increased cost of $20,024 per patient, not considering any incremental reimbursement associated with glucarpidase administration. Importantly, early treatment with glucarpidase, within 60 hours, was shown to be less expensive per patient than delayed glucarpidase treatment or treating with hemodialysis, but more expensive than SC alone. However, proposed practice was associated with multiple clinical benefits, including shorter hospital length of stay. For hypothetical practical scenarios, minimal shifts in treatment patterns had minimal cost impacts. Conclusion: Treatment of all eligible patients with glucarpidase within 60 hours was associated with an increased cost per patient (relative to current practice) but substantial improvements in clinical outcomes. Timely glucarpidase use was less expensive than delayed glucarpidase or hemodialysis.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".