Cost Effectiveness of Different Platelet Preparation, Storage, Selection and Dosing Methods in Platelet Transfusion: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Evidence-based guidelines on platelet transfusion therapy assist clinicians to optimize patient care, but currently do not take into account costs associated with different methods used during the preparation, storage, selection and dosing of platelets for transfusion. This systematic review aimed to summarize the available literature regarding the cost effectiveness (CE) of these methods. METHODS: Eight databases and registries, as well as 58 grey literature sources, were searched up to 29 October 2021 for full economic evaluations comparing the CE of methods for preparation, storage, selection and dosing of allogeneic platelets intended for transfusion in adults. Incremental CE ratios, expressed as standardized cost (in 2022 EUR) per quality-adjusted life-year (QALY) or per health outcome, were synthesized narratively. Studies were critically appraised using the Philips checklist. RESULTS: Fifteen full economic evaluations were identified. Eight investigated the costs and health consequences (transfusion-related events, bacterial and viral infections or illnesses) of pathogen reduction. The estimated incremental cost per QALY varied widely from EUR 259,614 to EUR 36,688,323. For other methods, such as pathogen testing/culturing, use of apheresis instead of whole blood-derived platelets, and storage in platelet additive solution, evidence was sparse. Overall, the quality and applicability of the included studies was limited. CONCLUSIONS: Our findings are of interest to decision makers who consider implementing pathogen reduction. For other preparation, storage, selection and dosing methods in platelet transfusion, CE remains unclear due to insufficient and outdated evaluations. Future high-quality research is needed to expand the evidence base and increase our confidence in the findings.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".