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. The study was published last month in the Journal of Clinical Medicine.
“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.”
ECMO works by routing blood out of a patient’s body, pumping it through a device that adds oxygen, removes carbon dioxide, and keeps the blood warm before returning it to the body. ECMO is used in both children and adults, but the most frequent patients are newborns. The therapy takes pressure off the heart and lungs while they mature or recover from injury.
Many patients treated with ECMO end up with brain complications, however, and doctors don’t