Citation

Hartka T, Glass G, Kao C, McMurry T. Traffic Injury Prev. 2018; 19(Suppl 2): S114-S120.

Affiliation

Department of Public Health , University of Virginia , Charlottesville , Virigina.

Copyright

(Copyright © 2018, Informa - Taylor and Francis Group)

DOI

10.1080/15389588.2018.1543872

PMID

30543473

Abstract

OBJECTIVE: The clinical evaluation of motor vehicle collision (MVC) victims is challenging and commonly relies on computed tomography (CT) to detect internal injuries. CT scans are financially expensive and each scan exposes the patient to additional ionizing radiation with an associated, albeit low, risk of cancer. Injury risk prediction based on regression modeling has been to be shown to be successful in estimating Injury Severity Scores (ISSs). The objective of this study was to (1) create risk models for internal injuries of occupants involved in MVCs based on CT body regions (head, neck, chest, abdomen/pelvis, cervical spine, thoracic spine, and lumbar spine) and (2) evaluate the performance of these risk prediction models to predict internal injury.

METHODS: All Abbreviated Injury Scale (AIS) 2008 injury codes were classified based on which CT body region would be necessary to scan in order to make the diagnosis. Cases were identified from the NASS-CDS. The NASS-CDS data set was queried for cases of adult occupants who sought medical care and for which key crash characteristics were all present. Forward stepwise logistic regression was performed on data from 2010-2014 to create models predicting risk of internal injury for each CT body region. Injury risk for each region was grouped into 5 levels: very low (<2%), low (2-5%), medium (5-10%), high (10-20%), and very high (20%). The models were then tested using weighted data from 2015 in order to determine whether injury rates fell within the predicted risk level.

RESULTS: The inclusion and exclusion criteria identified 5,477 cases in the NASS-CDS database. Cases from 2010-2014 were used for risk modeling (n = 4,826). Seven internal injury risk models were created based on the CT body regions using data from 2010-2014. These models were tested against data from 2015 (n = 651). In all CT body regions, the majority of occupants fell in the very low or low predicted injury rate groups, except for the head. On average, 57% of patients were classified as very low risk and 15% as low risk for each body region. In most cases the actual rate of injury was within the predicted injury risk range. The 95% confidence interval overlapped with predicting injury risk range in all cases.

CONCLUSION: This study successfully demonstrated the ability for internal injury risk models to accurately identify occupants at low risk for internal injury in individual body regions. This represents a step towards incorporating telemetry data into a clinical tool to guide physicians in the use of CT for the evaluation of MVC victims.

Language: en

Keywords

Advanced automatic crash notification; emergency medicine; event data recorder; injury risk curve