392 Regional Implementation of a Prehospital Whole Blood Transfusion Program: Laboratory and EMS Collaboration for Trauma Readiness
Bibliographic record
Abstract
Abstract Introduction/Objective Group O Whole Blood (WB) has served as a universal transfusion option since World War II, with proven efficacy in reducing battlefield mortality and hemorrhage. WB plays a central role in Damage Control Resuscitation (DCR) by countering the lethal triad of trauma-induced coagulopathy, hypothermia, and acidosis. Increasingly, trauma centers and emergency medical services (EMS) have adopted WB, notably Low-Titer O Whole Blood (LTOWB), as a frontline intervention for severely injured patients, replacing crystalloids in early resuscitation strategies. While prehospital use of low-titer O whole blood (LTOWB) is gaining traction, much of the existing literature is focused on military or isolated trauma center settings. This study documents a civilian, regional, hospital-based blood center’s operationalization of LTOWB across 26 diverse EMS agencies—a practical model not yet widely represented in published literature. This work positions the clinical laboratory as a key driver of trauma readiness, not just a support function. It extends the traditional boundaries of lab practice into the prehospital and emergency response domains. Methods/Case Report This retrospective study reviewed the implementation of a pre-hospital low-titer O whole blood (LTOWB) program launched in September 2020. Inova Blood Donor Services (IBDS) supplied units collected from O-positive male donors with anti-A/B titers ≤128. Units were leukoreduced using platelet-sparing filters, labeled per FDA/AABB guidelines, and had a 21-day shelf life in CPD anticoagulant. Unused units were returned for conversion to packed red blood cells (PRBCs) when appropriate; others were discarded. A multidisciplinary IBDS team ensured compliance, donor selection, validation, and cold chain standards. The Quality team developed SOPs for titer testing, labeling, and transport monitoring. Continuous tracking was implemented for expirations, deviations, and adverse events. EMS personnel across 26 Virginia and Maryland agencies received standardized training on transfusion indications, handling, and adverse event recognition, led by the Medical Director in collaboration with EMS leadership. An early challenge involved returned units exceeding acceptable temperature limits. The investigation revealed limitations to the original temperature monitoring device. IBDS validated and implemented an improved alternative with superior accuracy and visual cues, significantly reducing temperature-related discards and enhancing cold chain compliance. Results From 2020 to 2024, a total of 10,122 LTOWB units were created. Of these, 5,828 units (approximately 58%) were returned and successfully converted into packed red blood cells (PRBCs). A smaller portion—377 units (3.7%)—was discarded due to temperature excursions, outdated expiration, or venting. The data show a steady increase in LTOWB production and EMS utilization across the region, indicating enhanced program adoption. In parallel with increased product utilization, the program’s regional footprint expanded yearly. EMS and law enforcement agencies across Northern Virginia and Maryland joined the initiative annually, covering an estimated cumulative population of nearly 4.8 million by 2025. Conclusion The successful implementation of a regional LTOWB program demonstrates the power of coordinated, multidisciplinary collaboration between hospital-based blood centers and diverse EMS and law enforcement agencies. Beyond improving prehospital access to life-saving transfusions, the program has established a robust operational framework for regulatory compliance, quality assurance, and clinical education. The consistent growth in utilization and geographic reach underscores the feasibility and impact of integrating whole blood into emergency response protocols. Looking ahead, further opportunities exist to optimize donor selection algorithms, refine temperature stability monitoring, and explore the potential for universal donor panels. Regional data-sharing and research collaborations could help quantify outcomes such as survival rates, time-to-transfusion benchmarks, and comparative effectiveness between crystalloids and LTOWB in trauma settings. Expanding the initiative into rural, under-resourced, or high-incident regions, alongside standardizing EMS transfusion protocols at the state or interstate level, could improve equity and access.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".