Abstract

Background: An accurate test to identify atrial fibrillation in ischemic stroke populations would be of significant clinical utility. Using the Biomarkers of Acute Stroke Etiology (BASE) trial (NCT02014896) dataset, our goal was to utilize a database of genes appearing in literature determine if gene expression accurately differentiate patients with atrial fibrillation from those with large artery stroke. Methods: BASE enrolled suspected stroke patients presenting to 20 hospitals within 24 hrs of symptom onset. Final gold standard diagnosis and stroke etiology were determined by an adjudication committee using all hospital data but blinded to RNA test results. Whole blood, obtained in PAXgene tubes, was frozen at -20C within 72 hrs and analyzed at a core lab (Ischemia Care, LLC, Dayton, OH) using Affymetrix HTA micro arrays. Genes were filtered to those appearing in stroke literature resulting in 543 potential signature genes. A two-way random forest classifier was built through cross validation of the training data resulting in a 3 gene diagnostic signature with robust performance conserved across literature consisting of ELL2, GLIPR1, MAPKAPK3 genes. Results: Overall, 99 patients were enrolled with NIHSS>5, 68 (69%) with atrial fibrillation cause of stroke and 31 (31%) with large artery stroke; (48%) were male, and median (IQR) age was 74.4 (66.1,81.7). Median (IQR) time from symptoms to blood collection was 420 (322, 472) minutes. Coexistent pathology at presentation included high blood pressure 84 (85%), hyperlipidemia 45 (45%), diabetes 31 (31%), and coronary artery disease 38 (38%). Three genes were able to differentiate atrial fibrillation from large vessel stroke; C-statistic 0.86 (0.52-1.0, 95% CI), sensitivity 0.93 (0.56-1.0, 95% CI) and specificity of 0.58 (0.35-0.81, 95% CI ). Conclusion: RNA expression of ELL2, GLIPR1, MAPKAPK3 genes differentiates atrial fibrillation stroke patients from those with large artery stroke, and may have therapeutic and outcome implications.

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