A new resource-constrained triage method applied to victims of penetrating injury.

BACKGROUND Resource-constrained triage occurs when the number of trauma patients exceeds the capacity for simultaneous transport and treatment. The objective of this article is to apply a new resource-constrained triage method (denoted Sacco triage method [STM]) to victims of penetrating trauma and compare it with existing methods. METHODS STM is a mathematical model of resource-constrained triage. Its objective is to maximize expected survivors given constraints on the timing and availability of resources. The model incorporates estimates of time-dependent victim survival probabilities based on initial assessments and expected deteriorations. For application to penetrating trauma, an "RPM" score based on respiratory rate, pulse rate, and best motor response was used to predict survivability. Logistic function-generated survival probability estimates for scene values of RPM were determined from 7,274 penetrating injury patients from the Pennsylvania Trauma Outcome Study. The Delphi Method provided expert consensus on victim deterioration rates, and the model was solved using linear programming. The accuracy of predicting survivability was assessed using calibration and discrimination statistics. STM was compared with START (Simple Triage and Rapid Treatment)-like triage methods with respect to process and outcomes (assessed by expected number of survivors in simulated resource-constrained casualty incidents). RESULTS RPM was shown to be an accurate predictor of survival probability for penetrating trauma, equivalent to the Revised Trauma Score and exceeding that of the Injury Severity Score, as measured by calibration and discrimination statistics. In the simulations, STM had substantially more expected survivors than did current triage methods. CONCLUSIONS Resource-constrained triage is modeled as an evidence-based, outcome-driven method (STM) that maximizes expected survivors in consideration of resources. STM offers lifesaving and operational advantages over current methods.