Abstract
Introduction
Early recognition and treatment of trauma patients requiring massive transfusion (MT)
has been shown to reduce mortality. While many risk factors predicting MT have been
demonstrated, there is no universally accepted method or algorithm to identify these
patients. We hypothesised that even among experienced trauma surgeons, the clinical
gestalt of identifying patients who will require MT is unreliable.
Methods
Transfusion and mortality outcomes after trauma were observed at 10 U.S. Level-1 trauma
centres in patients who survived ≥30 min after admission and received ≥1 unit of RBC within 6 h of arrival. Subjects who received ≥10 units within 24 h of admission were classified as MT patients. Trauma surgeons were asked the clinical
gestalt question “Is the patient likely to be massively transfused?” 10 min after the patients arrival. The performance of clinical gestalt to predict MT
was assessed using chi-square tests and ROC analysis to compare gestalt to previously
described scoring systems.
Results
Of the 1245 patients enrolled, 966 met inclusion criteria and 221 (23%) patients received
MT. 415 (43%) were predicted to have a MT and 551(57%) were predicted to not have
MT. Patients predicted to have MT were younger, more often sustained penetrating trauma,
had higher ISS scores, higher heart rates, and lower systolic blood pressures (all
p < 0.05). Gestalt sensitivity was 65.6% and specificity was 63.8%. PPV and NPV were 34.9%
and 86.2% respectively.
Conclusion
Data from this large multicenter trial demonstrates that predicting the need for MT
continues to be a challenge. Because of the increased mortality associated with delayed
therapy, a more reliable algorithm is needed to identify and treat these severely
injured patients earlier.
Keywords
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Article info
Publication history
Published online: February 04, 2015
Accepted:
December 26,
2014
Footnotes
☆Presented at the 72nd Annual Meeting of the American Association for the Surgery of Trauma, September 18–21, San Francisco, California.
Identification
Copyright
© 2015 Elsevier Ltd. Published by Elsevier Inc. All rights reserved.
