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Crash Telemetry-Based Injury Severity Prediction is Equivalent to or Out-Performs Field Protocols in Triage of Planar Vehicle Collisions

Part of: Technology Developments in Prehospital and Disaster Medicine

Published online by Cambridge University Press:  19 July 2019

Katherine He Open the ORCID record for Katherine He [Opens in a new window] ,
Peng Zhang  and
Stewart C. Wang
Katherine He*
Affiliation:
Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA
Peng Zhang
Affiliation:
Department of Surgery, Division of Acute Care Surgery, University of Michigan, Ann Arbor, Michigan, USA
Stewart C. Wang
Affiliation:
Department of Surgery, Division of Acute Care Surgery, University of Michigan, Ann Arbor, Michigan, USA
*
Correspondence: Katherine He, MD, MS Surgery Education Office Brigham and Women’s Hospital 75 Francis Street – CA034 Boston, Massachusetts 02115 USA E-mail: Khe1@partners.org
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Abstract

Introduction:

With the increasing availability of vehicle telemetry technology, there is great potential for Advanced Automatic Collision Notification (AACN) systems to improve trauma outcomes by detecting patients at-risk for severe injury and facilitating early transport to trauma centers.

Methods:

National Automotive Sampling System Crashworthiness Data System (NASS-CDS) data from 1999-2013 were used to construct a logistic regression model (injury severity prediction [ISP] model) predicting the probability that one or more occupants in planar, non-rollover motor vehicle collisions (MVCs) would have Injury Severity Score (ISS) 15+ injuries. Variables included principal direction of force (PDOF), change in velocity (Delta-V), multiple impacts, presence of any older occupant (≥55 years old), presence of any female occupant, presence of right-sided passenger, belt use, and vehicle type. The model was validated using medical records and 2008-2011 crash data from AACN-enabled Michigan (USA) vehicles identified from OnStar (OnStar Corporation; General Motors; Detroit, Michigan USA) records. To compare the ISP to previously established protocols, a literature search was performed to determine the sensitivity and specificity of first responder identification of ISS 15+ for MVC occupants.

Results:

The study population included 924 occupants in 836 crash events. The ISP model had a sensitivity of 72.7% (95% Confidence Interval [CI] 41%-91%) and specificity of 93% (95% CI 92%-95%) for identifying ISS 15+ occupants injured in planar MVCs. The current standard 2006 Field Triage Decision Scheme (FTDS) was 56%-66% sensitive and 75%-88% specific in identifying ISS 15+ patients.

Conclusions:

The ISP algorithm comparably is more sensitive and more specific than current field triage in identifying MVC patients at-risk for ISS 15+ injuries. This real-world field study shows telemetry data transmitted before dispatch of emergency medical systems can be helpful to quickly identify patients who require urgent transfer to trauma centers.

Keywords

Emergency Medical Servicesfield triageinjury severitytrauma centervehicle telemetry
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 34 , Issue 4 , August 2019 , pp. 356 - 362
Copyright
© World Association for Disaster and Emergency Medicine 2019 

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