Joel D Stitzel1, Ashley A Weaver2, Jennifer W Talton3, Ryan T Barnard3, Samantha L Schoell2, Andrea N Doud4, R Shayn Martin4, J Wayne Meredith4. 1. Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC; Virginia Tech-Wake Forest University Center for Injury Biomechanics, Winston-Salem, NC. Electronic address: jstitzel@wakehealth.edu. 2. Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC; Virginia Tech-Wake Forest University Center for Injury Biomechanics, Winston-Salem, NC. 3. Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC. 4. Department of Surgery, Wake Forest School of Medicine, Winston-Salem, NC.
Abstract
BACKGROUND: Advanced Automatic Crash Notification algorithms use vehicle telemetry measurements to predict risk of serious motor vehicle crash injury. The objective of the study was to develop an Advanced Automatic Crash Notification algorithm to reduce response time, increase triage efficiency, and improve patient outcomes by minimizing undertriage (<5%) and overtriage (<50%), as recommended by the American College of Surgeons. STUDY DESIGN: A list of injuries associated with a patient's need for Level I/II trauma center treatment known as the Target Injury List was determined using an approach based on 3 facets of injury: severity, time sensitivity, and predictability. Multivariable logistic regression was used to predict an occupant's risk of sustaining an injury on the Target Injury List based on crash severity and restraint factors for occupants in the National Automotive Sampling System - Crashworthiness Data System 2000-2011. The Advanced Automatic Crash Notification algorithm was optimized and evaluated to minimize triage rates, per American College of Surgeons recommendations. RESULTS: The following rates were achieved: <50% overtriage and <5% undertriage in side impacts and 6% to 16% undertriage in other crash modes. Nationwide implementation of our algorithm is estimated to improve triage decisions for 44% of undertriaged and 38% of overtriaged occupants. Annually, this translates to more appropriate care for >2,700 seriously injured occupants and reduces unnecessary use of trauma center resources for >162,000 minimally injured occupants. CONCLUSIONS: The algorithm could be incorporated into vehicles to inform emergency personnel of recommended motor vehicle crash triage decisions. Lower under- and overtriage was achieved, and nationwide implementation of the algorithm would yield improved triage decision making for an estimated 165,000 occupants annually.
BACKGROUND: Advanced Automatic Crash Notification algorithms use vehicle telemetry measurements to predict risk of serious motor vehicle crash injury. The objective of the study was to develop an Advanced Automatic Crash Notification algorithm to reduce response time, increase triage efficiency, and improve patient outcomes by minimizing undertriage (<5%) and overtriage (<50%), as recommended by the American College of Surgeons. STUDY DESIGN: A list of injuries associated with a patient's need for Level I/II trauma center treatment known as the Target Injury List was determined using an approach based on 3 facets of injury: severity, time sensitivity, and predictability. Multivariable logistic regression was used to predict an occupant's risk of sustaining an injury on the Target Injury List based on crash severity and restraint factors for occupants in the National Automotive Sampling System - Crashworthiness Data System 2000-2011. The Advanced Automatic Crash Notification algorithm was optimized and evaluated to minimize triage rates, per American College of Surgeons recommendations. RESULTS: The following rates were achieved: <50% overtriage and <5% undertriage in side impacts and 6% to 16% undertriage in other crash modes. Nationwide implementation of our algorithm is estimated to improve triage decisions for 44% of undertriaged and 38% of overtriaged occupants. Annually, this translates to more appropriate care for >2,700 seriously injured occupants and reduces unnecessary use of trauma center resources for >162,000 minimally injured occupants. CONCLUSIONS: The algorithm could be incorporated into vehicles to inform emergency personnel of recommended motor vehicle crash triage decisions. Lower under- and overtriage was achieved, and nationwide implementation of the algorithm would yield improved triage decision making for an estimated 165,000 occupants annually.
Authors: Michaela Gaffley; Ashley A Weaver; Jennifer W Talton; Ryan T Barnard; Joel D Stitzel; Mark R Zonfrillo Journal: Traffic Inj Prev Date: 2019-09-27 Impact factor: 1.491
Authors: Ellen Ceklic; Hideo Tohira; Stephen Ball; Elizabeth Brown; Deon Brink; Paul Bailey; Rudolph Brits; Judith Finn Journal: BMC Emerg Med Date: 2022-05-06
Authors: Joshua R Lupton; Cynthia Davis-O'Reilly; Rebecca M Jungbauer; Craig D Newgard; Mary E Fallat; Joshua B Brown; N Clay Mann; Gregory J Jurkovich; Eileen Bulger; Mark L Gestring; E Brooke Lerner; Roger Chou; Annette M Totten Journal: Acad Emerg Med Date: 2022-04-22 Impact factor: 5.221