Algorithm used in research study to analyze brain-scan data on 25 retrospective Canadian cases of unresponsive ICU patients with severe brain injuries
Deciding the fate of a patient on life support with a severe brain injury is an agonizing choice for family members, and a challenging one for doctors.
A poor outcome, accurately predicted 12 out of 15 times, meant a patient had died, was in a vegetative state or had a severe disability. A good outcome, which the algorithm got right eight out of 10 times, meant the patient could look after themselves even with some physical or cognitive impairments, or had fully recovered. The finding was based solely on a resting-state fMRI, a brain-function scan accessible to trauma centres and intensive-care units.
“I don’t think we’ve ever scrutinized a data set more than this one to make sure that we were absolutely right,” Dr. Owen said. The sample size is small, he acknowledged, so the algorithm needs to be tested with many more patients. But ideally, with more data, the accuracy rate might even improve. But while doctors can generally predict who won’t survive in catastrophic circumstances, Dr. Owen said there’s no reliable method for predicting recovery in less clear-cut cases.
Rick Swartz, a neurologist at Sunnybrook Health Sciences Centre who was familiar with the study, said the algorithm was intriguing and outperformed existing clinical methods, but needs more testing. “It can help us see things we won’t usually be able to see ourselves,” he said. “We can focus on actually interpreting the results.”
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