When newborn babies or children with heart or lung distress are struggling to survive, doctors often turn to a form of life support that uses artificial lungs. This treatment, called Extracorporeal Membrane Oxygenation (ECMO), has been credited with saving countless lives. But in some cases, it can also lead to long-term brain injury.
Now, a research team led by UT Southwestern scientists has shown that a machine learning program can predict, more accurately than doctors, which babies and children are most likely to suffer brain injury after ECMO.
“Doctors have always had some intuition about who might be at risk, but until now we really haven’t had good data to pinpoint what factors are precipitating brain injury from ECMO,” says study leader Lakshmi Raman, M.D., associate professor of pediatrics at UT Southwestern and a critical care specialist at Children’s Health. “I don’t think we’ll be able to fully eliminate these injuries, but I hope that with better predictions we can mitigate the risk.”
