Machine learning improves prediction of cerebral ischemia after subarachnoid hemorrhage

Machine learning models significantly outperformed standard models in predicting delayed cerebral ischemia and functional outcomes at 3 months after a subarachnoid hemorrhage, according to findings published in Neurology. "After subarachnoid hemorrhage (SAH), delayed cerebral ischemia (DCI) is the biggest contributor to poor functional outcomes," Jude P.J. Savarraj, PhD, a bioinformatics postdoctoral fellow in the department of neurosurgery at McGovern Medical School, and colleagues wrote. "Previous studies show that several [electronic medical record] parameters, including white blood count panel, measures of coagulation and fibrinolysis, serum glucose and sodium and vital signs (including ECG and BP) are either marginally or strongly associated with DCI and functional outcomes." The researchers hypothesized that machine learning models would be able to learn these associations and accurately predict DCI and functional outcomes and outperform standard models. To test this, Savarraj and colleagues performed a retrospective analysis of outcomes among 451 patients [women, 290; average age, 54 years; median modified Rankin Scale score (mRS) at discharge 3; median mRS at month 3 1] who had a subarachnoid hemorrhage between July 2009 and August 2016.