Abstract

Background: Current ischemic stroke (IS) subtyping systems are very labor-intensive and costly to implement in large samples. There is a need to develop phenotyping approaches for genetic and other studies that can be implemented using generally available items captured in electronic medical records. Objective: In this report, we seek to validate a simplified approach using results from a genetic association study. Specifically, the strength of association for known genetic signals in the NINDS Stroke Genetics Network study (SiGN) is compared between the established IS subtyping systems (TOAST and Causative Classification System (CCS) subtypes) and phenotyping based on a single item within the CCS. Methods: Non-mutually exclusive “Simple” IS subtypes were defined from single CCS items as follows: 1) Small vessel stroke- lacunar infarct on imaging (with no other imaging or other requirements); 2) large artery stroke - vascular imaging showing > 50% stenosis or <50% stenosis with plaque rupture or thrombosis judged to be due to atherosclerosis in clinically relevant artery; and 3) cardioembolic stroke - persistent or paroxysmal atrial fibrillation. We compared the GWAS signals using these definitions to the published SiGN GWAS results for TOAST and CCS, among those of European ancestry only. Results: The Table shows that, for loci significant in SiGN GWAS study, the “Simple” IS subtypes had similar strengths of association and p-values compared to results from TOAST and the causative and phenotypic CCS subtypes. Conclusions: “Simple” IS subtypes perform as well in GWAS analyses as more complex phenotyping systems and could potentially be derived through automated or semi-automated methods applied to electronic medical record systems. Since the ‘Simple” phenotypes were based on high quality CCS data, the current findings reflect optimal performance. Additional validation studies will be required for implementation in other settings.

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