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Record W4416742808 · doi:10.1007/s00068-025-03033-z

Prehospital plasma transfusion versus standard of care following traumatic injury: a review of the systematic reviews and a meta-analysis

2025· article· en· W4416742808 on OpenAlexaboutno aff
Ayman El‐Menyar, Sandro Rizoli, Mashhood Naduvilekandy, Ammar Al‐Hassani, Fernando Antônio Campelo Spencer Netto, Mohammad Asim, Naushad Khan, Başar Cander, Sagar Galwankar, Łukasz Szarpak, Rubén Peralta, Hassan Al-Thani

Bibliographic record

VenueEuropean Journal of Trauma and Emergency Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsnot available
FundersHamad Medical Corporation
KeywordsSystematic reviewStandard of careSports medicineAdverse effectMeta-analysisMEDLINETrauma careBlood transfusion

Abstract

fetched live from OpenAlex

BACKGROUND: Individual studies suggest that administering prehospital blood products such as plasma to injured patients is feasible, may lower mortality, and improve coagulation. By compiling all existing evidence, we aimed to investigate whether prehospital plasma (PHP) transfusion can be safely administered and improve the clinical outcomes of trauma patients. METHODS: A systematic review (SR) and meta-analysis were conducted in accordance with the PRISMA guidelines to assess the effectiveness and safety of PHP transfusion compared to the standard of care in trauma patients. A literature search (2012 and 2024) was performed in PubMed, MEDLINE, EMBASE, and the Cochrane Library using the terms: "plasma resuscitation," "prehospital plasma," "prehospital blood components," "emergency transfusion," "trauma hemorrhage management," "lyophilized plasma," "freeze-dried plasma " "LyoPlas," FlyPlas," and "thawed fresh frozen plasma ."Studies focused on pediatric patients, in-hospital settings, feasibility only, or non-plasma interventions were excluded. Primary outcomes included early (24 hours) and late (28 or 30 days) mortality, and secondary outcomes included 24-hour transfusion units, vasopressor use, multiple organ failure, transfusion reaction, acute lung injury, and sepsis. The quality of studies was assessed using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias tool. The review was registered with PROSPERO. Sensitivity analyses were performed, excluding small studies with high variance and studies with combined blood products. RESULTS: Twelve studies comprising 3,193 trauma patients (1,579 intervention and 1,614 control arm) and seven SRs were included. There was no significant difference between the PHP and control groups for early and late mortality; however, the sensitivity analysis favored the PHP transfusion for 24-hour mortality. Without statistical significance, the total 24-hour volume of RBC units and vasopressor use was lower in the PHP group than in the controls. There was no significant difference between the PHP transfusion and control groups for the incidence of organ failure, adverse events, transfusion reactions, and sepsis. Observational studies were mostly of good quality, with two studies showing a moderate risk of bias. In contrast, RCTs had some concerns but were generally at a low risk for most domains. CONCLUSION: The overall pooled analysis revealed no significant benefit to PHP transfusion in trauma patients; however, sensitivity analyses showed a significant association of PHP and lower 24-hour mortality. PHP did not significantly decrease vasopressor use or late mortality; however, it may reduce the total use of RBCs in the first 24 h. Regarding safety, the review findings should be interpreted cautiously. Umbrella review was not conducted due to the heterogeneity and inconsistent inclusion criteria and outcomes. Further studies are needed to address the inconsistency in the existing evidence and determine whether PHP transfusion should be recommended for trauma patients with clearer and standardized endpoints and adverse event reporting.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.326
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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