Transfusion of Fresh Frozen Plasma and Platelets in Critically Ill Adults
Bibliographic record
Abstract
BACKGROUND: Platelets and fresh frozen plasma (FFP) are frequently administered to critically ill patients. Considering the variability in indications and thresholds guiding these transfusions, a comprehensive review of current evidence was conducted to provide guidance to critical care practitioners. This American College of Chest Physicians guideline examined the literature on platelet transfusions in critically ill patients with thrombocytopenia, with and without active bleeding, as well as data on prophylactic platelet and FFP transfusions for common procedures in the critical care setting. METHODS: A panel of experts developed 7 Population, Intervention, Comparator, and Outcome questions addressing platelet and FFP transfusions in critically ill patients and performed a comprehensive evidence review. The panel applied the Grading of Recommendations, Assessment, Development, and Evaluations approach to assess the certainty of evidence, and to formulate and grade recommendations. A modified Delphi technique was used to reach consensus on the recommendations. RESULTS: The initial search identified a total of 7,172 studies, and after the initial screening, 100 articles were reviewed. Sixteen studies met inclusion criteria, comprising 1 randomized controlled trial and 15 observational studies. Overall, the certainty of the evidence for all questions was very low. The panel formulated 7 conditional recommendations. CONCLUSIONS: In critically ill patients with thrombocytopenia or coagulopathy, a risk/benefit assessment should be made by providers prior to transfusion of platelets or FFP. Given the known risks of blood product transfusion, and the limited data regarding the benefits from platelet or FFP transfusion, most patients will benefit from avoiding transfusion of these blood products. In patients at high risk of bleeding, or where the bleeding complication may be catastrophic, transfusion should be considered.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".