Pathogen reduction of whole blood: Supplementing fibrinogen partly corrects clot formation in a massive transfusion model
Bibliographic record
Abstract
BACKGROUND: The use of whole blood (WB) to treat trauma patients is becoming more common. Similar to the treatment of individual components, pathogen inactivation (PI) technologies are available to treat WB. The impact of PI on WB function is not well understood. This study investigated the impact of PI of WB with riboflavin/ultraviolet (UV) light on its hemostatic function by modeling transfusion scenarios for trauma patients and assessing transfusion efficacy by rotational thromboelastometry (ROTEM). As fibrinogen is affected by PI of WB, the effect of fibrinogen supplementation commonly used in trauma patients was also analyzed in this model. STUDY DESIGN AND METHODS: Trauma transfusion scenarios were simulated by mixing untreated WB or WB treated with the Mirasol PI technology (riboflavin/UV) in different ratios with hemodiluted blood, and the thromboelasticity was monitored by ROTEM. The impact of supplementation with the fibrinogen concentrate RiaSTAP was investigated in this model. RESULTS: Pathogen-inactivated WB (PI-WB) showed decreased activity in the hemostatic profile compared to the untreated control. Hemodiluted blood at a hematocrit (hct) of 20%, which was reconstituted with PI-WB or untreated WB, exhibited increased alpha values, maximum clot firmness, and clot formation time. Simulating transfusion scenarios by blood replacement with PI-WB resulted in a significant difference in ROTEM parameters between reconstituted PI-treated and -untreated WB (p ≥ .05). The effect of PI treatment waned when PI-WB was enriched with fibrinogen. CONCLUSION: ROTEM investigations suggest that PI treatment has a negative impact on WB clot formation unless fibrinogen supplementation is used.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".