Classification tree modeling to identify severe and moderate vehicular injuries in young and middle-aged adults

SummaryObjectives vehicle crashes are a leading cause of mortality and morbidity worldwide. Even though trauma centers provide the gold standard of care for motor vehicle crash patients with life- or limb-threatening injuries, many whose lives might be saved by trauma center care are treated instead at non-trauma center hospitals. Triage algorithms, designed to identify patients with life- or limb-threatening injuries who should be transported to a trauma center, lack appropriate sensitivity to many of these injuries. The challenge to the trauma community is differentiating patients with life- or limb-threatening injuries from those with less severe injuries at the crash scene so that the patients can be transported to the most appropriate level of care. The purpose of this study was to use crash scene data available to emergency responders to classify adults with moderate and severe injuries. These classifiers might be useful to guide triage decision making. s and material s of 74,626 adults, age 18–64 years, from the National Automotive Sampling System Crashworthiness Data Systems database were analyzed using classification and regression trees (CART) analysis. Both CART models (moderate injury and severe injury) included 13 predictor variables. The response variables were the targeted injury severity score cut points for moderate and severe injury. Two final classification trees were developed: one that classified occupants based on moderate injury and the other on severe injury. Misclassification costs were manipulated to achieve the best model fit for each tree. s derate injury classification tree had three splitters: police-estimated injury severity, restraint use, and number of persons injured. The severe injury classification tree had four splitters: police-estimated injury severity, manner of collision, number of persons injured in the crash, and age. Sensitivity and specificity of the classification trees were 93.70%, 77.53% (moderate) and 99.18%, 73.96% (severe), respectively. sions nalysis can be used to classify injury severity using crash scene information that is available to emergency responders. This procedure offers an opportunity to examine alternative methods of identifying injury severity that might assist emergency responders to differentiate more accurately persons who should receive trauma center care from those who can be treated safely at a non-trauma center hospital.