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

Abstract

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 < 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.

Results: 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.

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

Keywords: Atherosclerosis; Cardioembolism; Machine learning; Stroke.