Improving Stroke Outcome Prediction Using Molecular and Machine Learning Approaches in Large Vessel Occlusion.

ABSTRACT: Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes for this specific patient population. The present study aimed to confirm whether sudden metabolic changes due to blood-brain barrier (BBB) disruption during LVO reflect differences in circulating metabolites and RNA between small and large core strokes. The second objective was to evaluate whether integrating molecular markers with existing neurological and imaging tools can enhance outcome predictions in LVO strokes. Methods: The infarction volume in patients was measured using mag

SUBMITTER: Rout M 

PROVIDER: S-EPMC11477941 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature