Development and evaluation of an advanced automatic crash notification algorithm for pediatric occupants

Weaver, Ashley; Schoell, Samantha; Barnard, Ryan; Talton, Jennifer; Doud, Andrea; Stitzel, Joel

Affiliations

¹Wake Forest School of Medicine, United States

Abstract

The objective of the study was to develop and evaluate a pediatric-specific advanced automatic crash notification (AACN) algorithm that uses a more comprehensive scoring system than the Abbreviated Injury Scale (AIS)-based severity to predict the risk that a child in a motor vehicle crash (MVC) is severely injured and requires treatment at a designated trauma center (TC). Though several research groups have developed AACN algorithms for adults, none have yet been developed for children. Given a child’s constant growth and development, use of currently-developed AACN algorithms in children is problematic because they provide no method for modification of injury risk based upon a child’s developmental stage.

A list of injuries associated with a pediatric patient’s need for Level I/II TC treatment known as the Target Injury List was determined using an approach based on 3 facets of injury: severity, time sensitivity, and predictability. The inputs used to create the pediatric-specific AACN algorithm include the Target Injury List (TIL) and 12,058 MVC occupants from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) 2000-2014. The algorithm uses multivariable logistic regression to predict an occupant's risk of sustaining an injury on the TIL from the following input variables: delta-v, number of quarter turns, belt status, multiple impacts, airbag deployment, and age group. The pediatric-specific AACN algorithm was optimized in order to minimize under triage (UT) and over triage (OT) rates with the goal of producing UT rates < 5% and OT rates < 50% as recommended by the American College of Surgeons (ACS).

The OT rates were 44% (frontal), 47% (near side), 43% (far side), 25% (rear), and 49% (rollover). The UT rates were 3% (frontal), 3% (near side), 2% (far side), 8% (rear), and 14% (rollover). Note there are not separate algorithms for each of the developmental age groups (due to sample size limitations), but these results are for the pediatric population as a whole.

Injury patterns change as children grow and develop. Current AACN algorithms in industry are not pediatric specific. The developed pediatric-specific AACN algorithm uses measurements obtainable from vehicle telemetry to predict risk of occupant injury and recommend a transportation decision for the occupant. The AACN algorithm developed in this study will aid emergency personnel in making the correct triage decision for pediatric occupants after a MVC, and once incorporated into the trauma triage network it can reduce response times, increase triage efficiency, and improve overall patient outcome.

Links

URL: http://indexsmart.mirasmart.com/25esv/PDFfiles/25ESV-000313.pdf

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Cited By (1)

Year	Entry
2023	Baker C. AutoTriage: A Feasibility Assessment of Using in-Vehicle Sensors to Predict Traumatic Brain Injury in Road Traffic Collisions [PhD thesis]. Imperial College London; March 23, 2023.