Development of Trauma Level Prediction Models Using Emergency Medical Service Vital Signs to Reduce Over- and Undertriage Rates in Penetrating Wounds and Falls of the Elderly. Issue 5 (May 2019)

• Record Type: Journal Article
• Authors: Cull, John; Riggs, Robert; Riggs, Sara; Byham, Megan; Witherspoon, Megan; Baugh, Nathan; Metcalf, Ashley; Kitchens, Debra; Manning, Benjamin
• Is Part Of: American surgeon. Volume 85:Issue 5(2019)
• Page Start: 524
• Page End: 529
• Publication Date: 2019-05
• Subjects: Surgery -- Periodicals; Surgery -- United States -- Periodicals; 617.0973
• DOI: 10.1177/000313481908500531
• Languages: English
• ISSNs: 0003-1348

Abstract

Determining triage activation levels in geriatric patients who fall (GF), and patients with penetrating wounds can be difficult and inaccurate, resulting in excessive overtriage (OT) and undertriage (UT) rates. We developed trauma activation prediction models using field data to predict with greater accuracy trauma activation level and triage rates consistent with the ACS recommendations. Using data from the 2014 National Trauma Data Bank, we created binary regression equations for each type of injury (GF and penetrating wounds). The 2014 data were randomized and divided into two halves. The first half for each injury type was used to generate prediction models, whereas the second half of the 2014 data were combined with 2013 and 2015 National Trauma Data Bank data for model verification. Binary regression equations were generated from vital signs collected by EMS. A Cribari grid with ISS ≥ 15 was used to determine the appropriateness of activation level. Chi-square analysis was used to determine significant differences between OT, UT, and accuracy predictions. Using our triage models, we were able to obtain UTrates of less than 4 per cent for GF with OT rates of less than 40 per cent, UT rates less than 4.1 per cent and OT of less than 50 per cent for patients with gunshot wounds, and UTrates less than 4 per cent and OT rates less than 25 per cent for patients who had stab wounds. Our developed trauma level prediction models enable health providers to predict trauma activation levels that can result in OT and UT rates in the recommended ranges by the ACS.