Prediction of probability of fatality due to brain injury in traffic accidents

Abstract

Objective: Fatal brain injuries result from physiological changes in brain tissues, subsequent to primary damage caused by head impact. Although efforts have been made in past studies to estimate the probability of brain injury, none of them involved prediction of such physiological changes. The goal of this study was to evaluate the fatality prediction capability of a novel approach that predicts an increase in intracranial pressure (ICP) due to primary head injury to estimate the fatality rate using clinical data that correlate ICP with fatality rate. Methods: A total of 12 sets of head acceleration time histories were used to represent no, severe, and fatal brain injury. They were obtained from the literature presenting head kinematics data in noninjurious volunteer sled tests or from accident reconstruction for severe and fatal injury cases. These were first applied to a Global Human Body Models Consortium (GHBMC) head-brain model to predict nodal displacement time histories of the brain, which were then fed into FEBio to predict ICP. A Weibull distribution was applied to the data for the relationship between fatality rate and ICP obtained from a clinical paper to estimate fatality rate from ICP (procedure A). Fatality rate was also estimated by applying the temporal and spatial maximum value of maximum principal strain (MPS max ) obtained from the GHBMC simulation to an injury probability function for MPS max (procedure B). Estimated fatality rates were compared between the 2 procedures. Results: Both procedures estimated higher average fatality rate for higher injury severity. The average fatality rate for procedure A without ischemia representation and procedure B was 72.4 and 51.0% for the fatal injury group and 8.2 and 21.7% for the severe injury group, respectively, showing that procedure A provides more distinct classification between fatal and nonfatal brain injury. It was also found that representation of ischemia in procedure A provides results sensitive to injury severity and impact conditions, requiring further validation of the initial estimate for the relationship between brain compression and ischemic cell death. Conclusions: Prediction of the probability of fatality by means of a combination of simulations of the primary brain deformation and subsequent ICP increase was found to be more distinct compared to the prediction of primary injury alone combined with the injury probability function from a past study in the select 12 head impact cases. ARTICLE HISTORY

Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain

“…The viscoelastic damping of the brain LVE model could also be altered to investigate its effect on the dynamic behavior of the brain, but such a change would differ from the subject-specific calibration to MRE and ex vivo data conducted in this study. Interestingly, the LVE simulations accurately capture the peak strain response of all strain components, which has been a primary correlate to predicted injury and has been implemented in safety metrics and evaluations [ 22 , 56 , 78 , 79 ]. The use of FE brain models with LVE properties across the automotive and sports industries to create injury risk metrics and curves emphasizes the importance of quantifying the accuracy and biofidelity of LVE constitutive formulations.…”