Mortality-based quantification of injury severity for frequently occurring motor vehicle crash injuries

Weaver, Ashley A.; Barnard, Ryan T.; Kilgo, Patrick D.; Martin, R. Shayn; Stitzel, Joel D.

Affiliations

¹Wake Forest University School of Medicine, Winston-Salem, NC

²Virginia Tech – Wake Forest University Center for Injury Biomechanics, Winston-Salem, NC

³Wake Forest University Public Health Sciences, Winston-Salem, NC

⁴Emory University School of Public Health, Atlanta, GA

Abstract

The study purpose was to develop mortality-based metrics of injury severity for frequent motor vehicle crash (MVC) injuries. Injury severity was quantified with mortality-based metrics for 240 injuries comprising the top 95% most frequently occurring AIS 2+ injuries in the National Automotive Sampling System – Crashworthiness Data System (NASS-CDS) 2000-2011. Mortality risk ratios (MRRs) were computed by dividing the number of deaths by occurrences for each of the 240 injuries using National Trauma Data Bank Research Data System (NTDB-RDS) MVC cases. MRRMAIS was computed using only patients with a maximum AIS (MAIS) equal to the AIS severity of a given injury. Each injury had an associated MRR and MRRMAIS which ranged from zero (0% mortality representing low severity) to one (100% or universal mortality representing high severity). Injuries with higher MRR and MRRMAIS values are considered more severe because they resulted in a greater proportion of deaths among injured patients. The results illustrated an overall positive trend between AIS severity and the MRR and MRRMAIS values as expected, but showed large variations in MRR and MRRMAIS for some injuries of the same AIS severity. Mortality differences up to 83% (MRR) and 54% (MRRMAIS) were observed for injuries of the same AIS severity. The MRR-based measures of injury severity indicate that some lower AIS severity injuries may result in as many deaths as higher AIS severity injuries. This data-driven determination of injury severity using MRR and MRRMAIS provides a supplement or an alternative to AIS severity classification

Links

PubMedCentral: PMC3861825

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Cited By (6)

Year	Entry
2015	Gabler HC, Weaver AA, Stitzel JD. Automotive field data in injury biomechanics. In: Yoganandan N, Nahum AM, Melvin JW, eds. Accidental Injury: Biomechanics and Prevention. 3rd ed. New York: Springer; 2015:33-49.
2015	Stitzel J, Weaver A, Schoell S, Barnard R, Talton J, Martin RS, Meredith JW. Occupant transportation decision algorithm: an advanced automatic crash notification algorithm developed and evaluated for motor vehicle crash occupant triage. In: Proceedings of the 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV). June 8-11, 2015; Gothenburg, Sweden.
2015	Kiuchi T, Motomura Y, Matsumoto H, Mashiko K. Pilot study on advanced automatic collision notification and helicopter emergency medical service system in Japan. In: Proceedings of the 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV). June 8-11, 2015; Gothenburg, Sweden.
2017	Weaver A, Schoell S, Barnard R, Talton J, Doud A, Stitzel J. Development and evaluation of an advanced automatic crash notification algorithm for pediatric occupants. In: Proceedings of the 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV). June 5-8, 2017; Detroit, MI.
2017	Stitzel J, Weaver A, Talton J, Barnard R, Schoell S, Martin RS, Meredith JW. Estimated triage improvement and societal cost savings with nationwide implementation of an advanced automatic crash notification algorithm in the U.S. In: Proceedings of the 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV). June 5-8, 2017; Detroit, MI.
2022	Brynskog E, Iraeus J, Pipkorn B, Davidsson J. Population variance in pelvic response to lateral impacts: a global sensitivity analysis. In: Proceedings of the 2022 International IRCOBI Conference on the Biomechanics of Injury. September 14-16, 2022; Porto, Portugal.173-196.