Machine learning model quickly and accurately predicts outcomes for TBI patients

Machine learning can be of great diagnostic value for predicting mortality in patients who have sustained traumatic brain injuries. 

A new study published in Radiology highlights the accuracy of a machine learning model that combines clinical data with imaging from head CT scans in individuals with severe traumatic brain injuries (sTBI) to quickly predict 6-month outcomes. Researchers trained the fusion model on multiple CT scanning protocols while also incorporating data, such as patients’ vital signs, blood tests and heart function. This resulted in a model performance that surpassed that of three experienced neurosurgeons when predicting patient mortality. 

“Every day, in hospitals across the United States, care is withdrawn from patients who would have otherwise returned to independent living,” said co-senior author David Okonkwo, MD, from the Department of Neurosurgery at University of Pittsburgh Medical Center. “The majority of people who survive a critical period in an acute care setting make a meaningful recovery—which further underscores the need to identify patients who are more likely to recover.” 

The authors explained that it can often take two weeks or more for TBI patients to emerge from their coma, although many individuals with more severe injuries are removed from life support within the first 72 hours of hospitalization. This presents a great need, the experts suggested, for prognostic tools that can predict what a patient’s recovery might look like well before they wake up. An early understanding of whether a patient’s condition has the potential to improve could prompt more precise treatment and help inform important decisions for clinicians and families.