Predictive Models and Algorithms for the Need of Transfusion Including Massive Transfusion in Severely Injured Patients

Abstract

BACKGROUND: Despite improvements on how to resuscitate exsanguinating patients, one remaining key to improve outcome is to expeditiously and reproducibly identify patients most likely to require transfusion including massive transfusion (MT). This work summarizes yet developed algorithms/scoring systems for transfusion including MT in civilian and military trauma populations. METHODS: A systematic search of evidence was conducted utilizing OVID/MEDLINE (1966 to present) and the 'Medical Algorithms Project'. RESULTS AND CONCLUSIONS: The models developed suggest combinations of physiologic, hemodynamic, laboratory, injury severity and demographic triggers identified on the initial evaluation of the bleeding trauma patient. Many approaches use a combination of dichotomous variables readily accessible after arrival but others rely on time-consuming calculations or complex algorithms and may have limited real-time application. Weighted and more sophisticated systems including higher numbers of variables perform superior. A common limitation to all models is their retrospective nature, and prospective validations are urgently needed. Point-of-care viscoelastic testing may be an alternative to these systems.