Using clinical data to reclassify ESUS patients to large artery atherosclerotic or cardioembolic stroke mechanisms

Purpose Embolic stroke of unidentified source (ESUS) represents 10–25% of all ischemic strokes. Our goal was to determine whether ESUS could be reclassified to cardioembolic (CE) or large-artery atherosclerosis (LAA) with machine learning (ML) using conventional clinical data. Methods We retrospecti...

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Bibliographic Details
Published in:Journal of neurology 2025, Vol.272 (1), p.87
Main Authors: Klein-Murrey, Lauren, Tirschwell, David L., Hippe, Daniel S., Kharaji, Mona, Sanchez-Vizcaino, Cristina, Haines, Brooke, Balu, Niranjan, Hatsukami, Thomas S., Yuan, Chun, Akoum, Nazem W., Lila, Eardi, Mossa-Basha, Mahmud
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Arteriosclerosis
Atherosclerosis
Atherosclerosis - complications
Embolic Stroke - blood
Embolic Stroke - diagnostic imaging
Embolic Stroke - etiology
Etiology
Female
Heart
Humans
Ischemia
Ischemic Stroke - blood
Ischemic Stroke - classification
Machine Learning
Male
Medicine
Medicine & Public Health
Middle Aged
Neurology
Neuroradiology
Neurosciences
Original Communication
Retrospective Studies
Stroke
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Summary:Purpose Embolic stroke of unidentified source (ESUS) represents 10–25% of all ischemic strokes. Our goal was to determine whether ESUS could be reclassified to cardioembolic (CE) or large-artery atherosclerosis (LAA) with machine learning (ML) using conventional clinical data. Methods We retrospectively collected conventional clinical features, including patient, imaging (MRI, CT/CTA), cardiac, and serum data from established cases of CE and LAA stroke, and factors with p  
ISSN:0340-5354
1432-1459
1432-1459
DOI:10.1007/s00415-024-12848-6