Early Predictors of Massive Transfusion in Combat Casualties

Martin A. Schreiber, Jeremy Perkins, Laszlo Kiraly, Samantha Underwood, Charles Wade, John B. Holcomb

Research output: Contribution to journalArticlepeer-review

186 Scopus citations

Abstract

Background: An early predictive model for massive transfusion (MT) is critical for management of combat casualties because of limited blood product availability, component preparation, and the time necessary to mobilize fresh whole blood donors. The purpose of this study was to determine which variables, available early after injury, are associated with MT. We hypothesized that International Normalized Ratio and penetrating mechanism would be predictive. Study Design: We performed a retrospective cohort analysis in two combat support hospitals in Iraq. Patients who required MT were compared with patients who did not. Eight potentially predictive variables were subjected to univariate analysis. Variables associated with need for MT were then subjected to stepwise logistic regression. Results: Two hundred forty-seven patients required MT and 311 did not. Mean Injury Severity Score was 22 in the MT group and 5 in the non-MT group (p < 0.001). Patients in the MT group received 17.9 U stored RBCs and 2.0 U fresh whole blood, versus 1.1 U RBCs and 0.2 U whole blood in the non-MT group (p < 0.001). Mortality was 39% in the MT group and 1% in the non-MT group (p < 0.001). Variables that independently predicted the need for MT were: hemoglobin ≤ 11 g/dL, International Normalized Ratio > 1.5, and a penetrating mechanism. The area under the receiver operator characteristic curve was 0.804 and Hosmer-Lemeshow goodness-of-fit test was 0.98. Conclusion: MT after combat injury is associated with high mortality. Simple variables available early after admission allow accurate prediction of MT.

Original languageEnglish (US)
Pages (from-to)541-545
Number of pages5
JournalJournal of the American College of Surgeons
Volume205
Issue number4
DOIs
StatePublished - Oct 2007

ASJC Scopus subject areas

  • General Medicine

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