April 23, 2014
SickKids study measures brain activity to predict outcomes for traumatic brain injury
Brain injury from trauma, cardiac arrest or stroke is the most common cause of death and acquired disability in the paediatric population. Due to the lifetime impact of brain injury, there is a need for methods to classify patient risk and ultimately predict outcomes. Early prognosis is fundamental to improved recovery, but there are no early clinical methods to accurately predict the risk of long lasting disabilities. For example, when children are in a coma following a traumatic brain injury, predicting whether they will emerge from coma and what the long term outcomes will be is very challenging.
The brain is composed of electrical networks yet no methods are available to test the health and recovery of these networks after brain injury. In a new study published in PLOS ONE, researchers at The Hospital for Sick Children (SickKids) used a common technology for detection of seizures called electroencephalography (EEG) and high-speed computer algorithms to measure the synchronization of these electrical brain waves. These methods were used in children admitted to critical care unit in coma following brain injury.
Normal brain activity measured using this new EEG technique is lively and almost chaotic. This study indicates that patients who had a poor outcome had more monotonous or synchronized electrical brain activity, while patients with more favourable outcomes had less synchronized electrical brain activity.
“This clinical test could be very useful in the prediction of outcome in paediatric patients with brain injury during the acute phase post-injury,” says Dr. Jamie Hutchison, senior author of the study and Research Director in Critical Care Medicine at SickKids. “Our study suggests that this technology may be a new method of real time evaluation of brain function to both monitor brain health and to predict outcome in children in coma.”
This work was supported in part by Dr. Vera Nenadovic’s CIHR Banting and Best Doctoral Fellowship, and by Dr. Jose Perez Velazquez’s Discovery grant from the Natural Sciences and Engineering Research Council of Canada (NSERC).