Economic analysis of epoetin alfa in critically ill trauma patients
Bibliographic record
Abstract
BACKGROUND: Recent randomized control trials (RCTs) suggest that epoetin alfa reduces mortality in critically ill trauma patients; however, epoetin alfa is also costly and associated with adverse events. This study evaluates the cost-effectiveness of epoetin alfa in surgical trauma patients in an intensive care unit setting. METHODS: We constructed a decision analytic model to compare adjunctive use of epoetin alfa with standard care in trauma patients from the perspective of a Canadian payer. Baseline risks of events, relative efficacy, and resource use were obtained from RCTs and observational studies. One-way and probabilistic sensitivity analyses were conducted and longer time horizons explored through Markov models. RESULTS: Epoetin alfa was associated with a cost per quality-adjusted life year (QALY) gained of $89,958 compared with standard care at 1 year. One-way sensitivity analyses indicated that results were sensitive to plausible ranges of mortality risk, risk of thrombosis, relative risk of mortality, relative risk of thrombosis, and quality of life estimates. Cost-effectiveness acceptability curves generated from probabilistic sensitivity analysis indicated that the probability that epoetin alfa would be considered attractive ranged from 0% to 85% over a willingness-to-pay range of $25,000 to $120,000/QALY. Consideration of lifetime time horizons reduced the cost per QALY gained to $7,203, but results were sensitive to the effect of epoetin alfa on mortality. CONCLUSION: Although the cost per QALY gained with epoetin alfa use may fall into an acceptable range, there is significant uncertainty about its true cost-effectiveness. If data regarding long-term efficacy and safety are confirmed in future trials, epoetin alfa could potentially be cost-effective in this population. LEVEL OF EVIDENCE: Economic analysis, level I.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".