Limitations of Available Blood Products for Massive Transfusion During Mass Casualty Events at US Level 1 Trauma Centers
Bibliographic record
Abstract
INTRODUCTION: Exsanguination remains a leading cause of preventable death in traumatically injured patients. To better treat hemorrhagic shock, hospitals have adopted massive transfusion protocols (MTPs) which accelerate the delivery of blood products to patients. There has been an increase in mass casualty events (MCE) worldwide over the past two decades. These events can overwhelm a responding hospital's supply of blood products. Using a computerized model, this study investigated the ability of US trauma centers (TCs) to meet the blood product requirements of MCEs. METHODS: Cross-sectional survey data of on-hand blood products were collected from 16 US level-1 TCs. A discrete event simulation model of a TC was developed based on historic data of blood product consumption during MCEs. Each hospital's blood bank was evaluated across increasingly more demanding MCEs using modern MTPs to guide resuscitation efforts in massive transfusion (MT) patients. RESULTS: A total of 9,000 simulations were performed on each TC's data. Under the least demanding MCE scenario, the median size MCE in which TCs failed to adequately meet blood product demand was 50 patients (IQR 20-90), considering platelets. Ten TCs exhaust their supply of platelets prior to red blood cells (RBCs) or plasma. Disregarding platelets, five TCs exhausted their supply of O- packed RBCs, six exhausted their AB plasma supply, and five had a mixed exhaustion picture. CONCLUSION: Assuming a TC's ability to treat patients is limited only by their supply of blood products, US level-1 TCs lack the on-hand blood products required to adequately treat patients following a MCE. Use of non-traditional blood products, which have a longer shelf life, may allow TCs to better meet the blood product requirement needs of patients following larger MCEs.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".