Predicting Brain Infarct Growth During Interhospital Transfer for Acute Stroke

Strokes that are caused by blockage of large blood vessels by clots are the most disabling. Despite new highly effective clot removal therapies, stroke-related disability remains substantial, because of brain damage (infarct) that progressively occurs in the time-period before clot removal. This time period can be long especially if patients need to be transferred from a community hospital that does not have the capability to provide the clot removal therapy to a comprehensive stroke center that does. Limiting infarct growth during this time-consuming transfer is a major challenge to reduce post-stroke disability. To this end, predicting patients at high risk of fast infarct growth during the transfer, to be included in future neuroprotection trials, is the essential first step. My project aims to predict, based on brain imaging data obtained in the community hospital, infarct growth during interhospital transfer. Data from a large US cohort using Computerized Tomography (CT), and from a large French cohort using Magnetic Resonance Imaging (MRI), will be analyzed, using innovative techniques such as artificial intelligence, to accurately predict infarct growth. This will help to design future trials aiming to prevent infarct growth in patients at high risk of fast infarct growth using MRI or CT.