Citation

Sanchez EJ, Gabler LF, McGhee JS, Olszko AV, Chancey VC, Crandall JR, Panzer MB. J. Neurotrauma 2017; 34(16): 2410-2424.

Affiliation

University of Virginia, Center for Applied Biomechanics, Charlottesville, Virginia, United States ; panzer@virginia.edu.

Copyright

(Copyright © 2017, Mary Ann Liebert Publishers)

DOI

10.1089/neu.2016.4681

PMID

28358277

Abstract

Risk assessment models are developed to estimate the probability of brain injury during head impact using mechanical response variables such as head kinematics and brain tissue deformation. Existing injury risk functions have been developed using different datasets based on human volunteer and scaled animal injury responses to impact. However, many of these functions have not been independently evaluated with respect to laboratory-controlled human response data. In this study, the specificity of fourteen existing brain injury risk functions was assessed by evaluating their ability to correctly predict non-injurious response using previously conducted sled tests with well-instrumented human research volunteers. Six degree-of-freedom head kinematics data were obtained for 335 sled tests involving subjects in frontal, lateral, and oblique sled conditions up to 16 Gs peak sled acceleration. A review of the medical reports associated with each individual test indicated no clinical diagnosis of mild or moderate brain injury in any of the cases evaluated. Kinematic-based head and brain injury risk probabilities were calculated directly from the kinematic data, while strain-based risks were determined through finite element model simulation of the 335 tests. Several injury risk functions sub¬stanti¬ally over pre¬dict the likelihood of concussion and diffuse axonal injury; proposed maximum principal strain (MPS)-based injury risk functions predicted nearly 80 concussions and 14 cases of severe diffuse axonal injury out of the 335 non-injurious cases. This work is an important first step in assessing the efficacy of existing brain risk functions and highlights the need for more predictive injury assessment models.

Language: en

Keywords

FINITE ELEMENT MODELS; HUMAN STUDIES; MODELS OF INJURY; TRAUMATIC BRAIN INJURY