Development and Evaluation of an Injury-based Advanced Automatic Crash Notification Algorithm for Occupant Triage Recommendation

作者：Stitzel Joel D^*; Schoell Samantha L; Weaver Ashley A; Barnard Ryan T; Talton Jennifer W; Martin R Shayn; Meredith J Wayne

来源：Traffic Injury Prevention, 2015, 16: S249-S250.

Objective: The objective of this study was to develop an injurybased Advanced Automatic Crash Notification (AACN) algorithm and evaluate its performance in making optimal occupant triage decisions.
Methods: The AACN algorithm known as the Occupant Transportation Decision Algorithm (OTDA) uses vehicle telemetry measurements to predict risk of occupant injury and recommend a transportation decision (Level I/II trauma center (TC) versus non-TC). Injuries associated with a patient's need for treatment at a Level I/II TC were determined using an injury-based approach based on three facets (severity, time sensitivity, and predictability). These three facets were quantified from expert physician and emergency medical services (EMS) professional opinion and database analyses of the National Trauma Data Bank (NTDB) and National Inpatient Sample (NIS). Severity, Time Sensitivity, and Predictability Scores were summed for each injury to compute an Injury Score. Injuries with an Injury Score exceeding a particular threshold were included on a Master Target Injury List, a list of injuries more likely to require Level I/II TC treatment. The OTDA used multivariate logistic regression of National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) occupants to predict an occupant's risk of sustaining an injury on the Master Target Injury List from longitudinal/lateral delta-v, number of quarter turns, belt status, multiple impacts, and airbag deployment. The OTDA has five tunable parameters that were optimized with a genetic algorithm that compared the OTDA transportation decision for each occupant to a dichotomous representation of their Injury Severity Score (ISS). OTDA optimization minimized under triage (UT) and over triage (OT) rates with the goal of producing UT rates <5% and OT rates <50%.
Results: For the optimized OTDA, UT rates by crash mode were 5.9% (frontal), 4.6% (near side), 2.9% (far side), 7.0% (rear), and 16.0% (rollover). OT rates by crash mode for the optimized OTDA were 49.7% (frontal), 47.9% (near side), 48.7% (far side), 44.0% (rear), and 49.7% (rollover).
Conclusions: The injury-based OTDA has been rigorously optimized and has demonstrated improved UT rates compared to other AACN algorithms in the literature and OT rates meeting American College of Surgeons (ACS) recommendations. Since the OTDA uses only vehicle telemetry measurements specified in Part 563 regulation, this AACN algorithm could be readily incorporated into new vehicles to inform emergency personnel of recommended triage decisions for occupants. The overall societal purpose of this AACN algorithm is to reduce response times, increase triage efficiency, and improve overall patient outcome.

• 出版日期2015-10-8
• 单位美国弗吉尼亚理工大学(Virginia Tech)