Citations

Objectives: Genetic and environmental influences contribute to differences in ischemic stroke phenotype across race-ethnic groups. The Causative Classification System (CSS) is an ischemic stroke phenotype characterization tool developed for etiological and causative classification. The CCS phenotypes across ethnic groups were analyzed and compared. Methods: The NINDS S troke G enetics N etwork (SiGN) is a large consortium aimed at identifying subtype-specific ischemic stroke susceptibility genes. Over 12,000 subjects at 22 sites across US and Europe were enrolled, with CSS locally adjudicated. Central CCS quality control was performed on all participants. Analysis was performed on 7,674 patients with complete workups. Findings: Significant ethnic differences were found in stroke etiology across ethnic groups (p <0.0001) with large artery atherosclerosis present most commonly in Asians, cardioembolic sources among Caucasian Europeans, and lacunar strokes among African Americans. African Americans were the youngest ethnic cohort analyzed (see Table 1). Conclusion: Significant differences in stroke subtypes are present across ethnic groups. Future genetic analyses may contribute to our understanding of these differences.

Background: Considerable disagreement exists among experts regarding the appropriate extent of diagnostic testing after ischemic stroke (IS). Evidence-based data guiding stroke evaluation for individual patients according to the underlying phenotype are unknown. We sought to determine the yield of cardiac and arterial evaluations for identifying an alternative major stroke etiology in patients with a given phenotype in the NINDS Stroke Genetics Network (SiGN). Methods: SiGN is a consortium of IS studies at 22 US and European sites aiming to identify stroke susceptibility genes. Adjudicators certified in Causative Classification of Stroke (CCS) system determined major etiologic stroke phenotypes based on chart review. All diagnostic data entered into the CCS software were stored in a confidential database. We determined the probability of identifying an alternative major abnormality based on cardiac (history, EKG, echocardiography) and arterial [intracranial (IC), extracranial (EC)] evaluations in 3 stroke phenotypes: lacunar infarct (LI), large artery atherosclerosis (LAA), and cardiac embolism (CE). Proportions of alternative major etiologies were calculated in patients with complete evaluations in each phenotype. Results: The analyses included 15720 patients. Among these, 2299 had phenotypic subtype of LI (1660 with cardiac and 1691 with arterial evaluations); 4228 had CE (2451 with arterial evaluation); and 2613 had LAA (2047 with cardiac evaluation). Cardiac evaluation revealed a major cardiac source of embolism in 12.3% of patients with LI and 20.3% with LAA. Echocardiography demonstrated an additional major structural cardiac source not seen by history/examination/EKG in 1.9% and 1.6% in LI and LAA respectively. Arterial evaluation led to the identification of ≥50%ipsilateral stenosis in 13.5% (IC: 8.9%, EC: 5.8%) of patients with LI and 17% (IC: 8.1%, EC: 11.3%) of those with CE. In LI patients with complete cardiac and vascular evaluations, 24.1% had an alternative major stroke etiology. Conclusions: A considerable proportion of IS patients with LI, LAA, and CE harbor an alternative major etiology. In the future, these results can be used to generate evidence-based and cost-effective evaluation and management strategies.

Background and Purpose: NINDS Stroke Genetics Network (SiGN) is an international consortium of ischemic stroke studies that aims to generate high quality phenotype data to identify the genetic basis of ischemic stroke subtypes. The goal of this analysis is to characterize the etiopathogenetic basis of ischemic stroke in the consortium. Methods: This analysis included 16,954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries. 52 trained and certified adjudicators used the web-based Causative Classification of Stroke System for etiologic stroke classification through chart reviews to determine both phenotypic (abnormal test findings categorized in major etiologic groups without weighting towards the most likely cause in the presence of multiple etiologies) and causative subtypes in each subject. Classification reliability was assessed with blinded re-adjudication of 1509 randomly selected cases. Findings: The figure shows the distribution of etiologic categories. Overall, only 40% to 54% of cases with a given major ischemic stroke etiology (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (kappa 0·72, 95%CI:0·69-0·75) and phenotypic classifications (kappa 0·73, 95%CI:0·70-0·75). Conclusions: This study provides high quality data on etiologic stroke subtypes and demonstrates that etiologic subtypes can be determined with good reliability in studies that include investigators with different expertise and background, institutions with different stroke evaluation protocols and geographic location, and patient populations with different epidemiological characteristics. The discordance between phenotypic and causative stroke subtypes suggests that the presence of an abnormality in a stroke patient does not necessarily mean that it is the cause of stroke.