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

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.

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 < 0.2 in univariable analysis were used for creating a ML predictive tool. We then applied this tool to ESUS cases, with ≥ 75% likelihood serving as the threshold for reclassification to CE or LAA. In patients with longitudinal data, we evaluated future cardiovascular events.

191 ischemic stroke patients (80 CE, 61 LAA, 50 ESUS) were included. Seven and 6 predictors positively associated with CE and LAA etiology, respectively. The c-statistic for discrimination between CE and LAA was 0.88. The strongest predictors for CE were left atrial volume index (OR = 2.17 per 1 SD increase) and BNP (OR = 1.83 per 1 SD increase), while the number of non-calcified stenoses ≥ 30% upstream (OR = 0.34 per 1 SD increase) and not upstream (OR = 0.74 per 1 SD increase) from the infarct were for LAA. When applied to ESUS cases, the model reclassified 40% (20/50), with 11/50 reclassified to CE and 9/50 reclassified to LAA. In 21/50 ESUS with 30-day cardiac monitoring, 1/4 in CE and 3/16 equivocal reclassifications registered cardiac events, while 0/1 LAA reclassifications showed events.

ML tools built using standard ischemic stroke workup clinical biomarkers can potentially reclassify ESUS stroke patients into cardioembolic or atherosclerotic etiology categories.