Ay, Hakan

Background: The heterogeneity of ischemic stroke has relevance for determination of risk for recurrence, prediction of outcomes, and identification of genetic risk factors. Among the many systems created for classifying patients into subtypes of stroke, the Trial of Org10172 Acute Stroke Trial (TOAST) and Causative Classification of Stroke (CCS) systems are among the most commonly used. Both systems generate similar subtype diagnoses although the two systems use different criteria for subtype assignment. The purpose of this study was to assess the level of agreement between subtype classification made using these two systems. Methods: Study subjects included 14,106 adult men and women accrued in the NINDS Stroke Genetics Network (SiGN) across 21 centers in the US and Europe classified independently using both with TOAST and CCS. Kappa statistics were calculated for the five major ischemic stroke subtypes common to both systems. Results: The overall agreement between TOAST and CCS was moderate (agreement rate, 70%; kappa = 0.60, 95% confidence interval [CI], 0.59 to 0.61). Agreement varied widely across study sites, ranging from 28% to 90%. Agreement on specific subtypes was highest for large artery atherosclerosis (kappa, 0.71; 95% CI, 0.70 to 0.73) and lowest for small artery occlusion (kappa = 0.56, 95%CI, 0.54 to 0.58). Conclusion: Agreement between TOAST and CCS diagnoses was only moderate. Caution is warranted when comparing or combining results based upon the two systems. Re-phenotyping using one system should be encouraged when possible to ensure valid results. Table: Cross-classification of stroke subtypes by TOAST and CCS

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: Causative Classification of Stroke (CCS) system is a novel, web-based, and fully-computerized algorithm that integrates clinical, diagnostic, and etiologic stroke characteristics in an evidence-based manner (available at http://ccs.mgh.harvard.edu ) and provides in a given patient both phenotypic subtypes where abnormal evaluation findings are simply organized in major etiologic categories and causative subtypes where abnormal evaluation findings are filtered through a decision making process to identify the most likely cause of stroke. In this study, we sought to identify the concordance between phenotypic and causative CCS subtypes in the NINDS Stroke Genetics Network (SiGN). Methods: A total of 24 adjudicators from 15 US and European sites certified in CCS retrospectively determined CCS subtypes through chart review in 7134 patients. The CCS software provided causative subtypes in 3 confidence levels as “evident”, “probable” or “possible” based on the weight of causal evidence. We estimated how often CCS classified a given major abnormal evaluation finding as the causative stroke mechanism. Results: CCS assigned 55% of patients with the phenotype of moderate-to-severe atherosclerotic stenosis into the causative category of “evident large artery atherosclerosis”. Likewise, 50% of patients with a major cardiac source (such as atrial fibrillation) were classified into “evident cardio-aortic embolism”, and 74% of those with imaging evidence of a typical lacunar infarct into “evident small artery occlusion”. In 20% to 30% of patients, phenotypic and causative categories were the same but the causative subtype was assigned with a lower level of confidence. Six to 20% of patients with a given phenotypic feature were classified into a different causative category. Failure to investigate for other subtypes in the presence of a positive test finding and the presence of multiple competing etiologies were key contributors to the observed disconcordance between phenotypic and causative subtypes. Conclusions: Our findings show that the presence of a major abnormality in stroke evaluation does not necessarily mean that it is always the cause. The ability of CCS system to discriminate between phenotypic and causative characteristics of stroke etiology provides the opportunity to refine stroke phenotypes for use in research studies.

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: Recruitment and phenotyping constitute major costs in stroke genetics research. Clinical trial datasets that include biological samples provide an opportunity to leverage richly phenotyped cases for genomic research. However, the heterogeneity of stroke and methodological differences across studies present challenges. We developed and validated a systematic method of mapping data abstracted from a multicenter randomized clinical trial to the inputs of the CCS stroke subtyping system for the SiGN study. Methods: The SiGN Phenotype Committee and the Secondary Prevention of Small Subcortical Strokes (SPS3) research team systematically reviewed SPS3 case report forms (CRF) to identify key elements required to generate CCS subtyping, and drafted and refined mapping rules using a derivation set of 30 charts. The resultant algorithm was compared to manual entry of clinical information into the CCS in a test set of 30 randomly selected charts. This identified problems due to multiple versions of CRFs (up to 7 versions) and prompted revision of the mapping rules. We assessed the revised algorithm in an independent test set. Results: None of the subtype classifications agreed in the initial testing prompting revision to allow capture information from different versions of the CRFs. The revised mapping algorithm applied to the same test set performed well; 29/30 agreed - 20 small artery occlusion (SAO) evident, 4 SAO possible, 2 SAO probable and 3 supra-aortic large artery atherosclerosis evident). The one chart that disagreed was classified as SAO evident by manual entry but as undetermined by the data mapping rules. In the 2nd independent test set, 30/30 agreed: 22 SAO evident, 4 SAO possible, 3 supra-aortic large artery atherosclerosis evident and 1 undetermined unknown - incomplete evaluation. Conclusions: We created mapping rules using CRFs from a clinical trial to allow reliable subtype classification using the CCS. Issues related to multiple iterations of CRFs presented challenges. Highly phenotyped stroke cases from clinical trials represent a cost-effective opportunity for genomic research.

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.

Objectives: Identification of gene variants of stroke subtypes is important for the development of tailored ischemic stroke therapies among various ethnic groups. Valid and reliable determination of ischemic stroke subtype is essential for achieving this goal and to standardize a classification scheme across multi-center studies and different populations. Causative Classification System for Ischemic Stroke (CCS) is a novel computerized subclassification tool developed to improve reliability and accuracy of classifying stroke types. The CCS algorithm relies on both phenotypic and causative stroke variables. A Hispanic subset of the SiGN, an important and distinct target population with greater risk of certain stroke subtypes, was evaluated with Trial of Org 10172 in Acute Stroke Treatment (TOAST) and CCS and the agreement between the two classification systems was analyzed. Methods: Over 6000 subjects at 15 sites across US and Europe were enrolled, with TOAST and CCS locally adjudicated. Blood collection and central data quality control (10% central readjudication) were performed on all participants. A subset of Hispanics was analyzed for the purpose of this study and the agreement between the TOAST and CCS were assessed by kappa statistic. Findings: Hispanics (n=595, 10.9%) compared to non-Hispanics (n=5457) were more likely to be younger (63.7 vs. 64.0), male (55% vs. 46%) and have fewer of the traditional stroke risk factors HTN (54% vs. 64%), Afib (11% vs. 14%), DM(23% vs. 25%), CAD(16% vs. 20%) and smoking(19% vs. 22%). While the TOAST showed no differences between stroke subtypes for Hispanic vs. non-Hispanics, in CCS, Hispanics were classified with more of large vessel (22% vs. 20%), cardioembolic (37% vs. 30%) and small vessel strokes (13% vs. 9%) and fewer with undetermined etiology (28% vs. 40%) as compared to non-Hispanics. TOAST and CCS offered moderate correlation across all stroke types in Hispanics: kappa of 0.66 for large artery atherosclerosis, 0.58 for cardioembolic, and 0.58 for small artery occlusion. Conclusion: CCS offers a more sensitive and accurate system for subphenotyping of strokes in Hispanics who tended to have relatively fewer risk factors and unclassified strokes. Further studies correlating the two classification systems and their relation to genotyping data are warranted.

Background: The Stroke Genetics Network (SiGN) funded by the NINDS aims to identify genetic risk factors in ischemic stroke using whole-genome association studies (GWAS). High quality phenotyping is crucial to successful application of GWAS. As a heterogenous disorder, stroke poses specific challenges. The Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification is a broadly used, but its validity is challenged especially when performed by multiple investigators with differing interpretations of the system. The Causative Classification System for Ischemic Stroke (CCS) system is a new, web-based, and computerized algorithm that integrates clinical, diagnostic, and etiologic stroke characteristics in an evidence-based manner ( ccs.mgh.harvard.edu ) to generate subtypes. Methods: In planning the SiGN proposal, a sample of 20 coded charts were collected from a subset of participating studies to assess feasibility of central adjudication and comparability to study-specific TOAST. Two central adjudicators reviewed all records and generated TOAST and CCS subtypes. These were compared to study-specific TOAST subtype and the CCS phenotype generated for SiGN by local trained adjudicators. CCS data is now available for 7134 included cases using both a 5 and a 7 category system as defined in the table . Results: All 4 phenotypes were available for 115 ischemic stroke cases from 6 studies in SiGN. Basic demographics were 54% women, 63% white, and median age between 65-74. Table 1 provides the agreement between the various subtypes. Table 2 describes the types of disagreement. Conclusions: Central adjudication with only two adjudicators and curated medical records yielded more consistent subtyping independent of phenotyping system. The agreement for TOAST was higher than published rates by independent groups (∼0.50). In contrast, the agreement for CCS was lower than previously published (0.85-0.95). Site adjudicators' familiarity with TOAST and inexperience with CCS may contribute. Although CCS is an automated algorithm and has a number of user friendly features, our findings suggest that formal training and certification process before starting to use CCS may be worthwhile to achieve optimal benefit from the system.

Background: Identification of clinical and imaging characteristics of ischemic stroke that can predict the underlying etiology is critical for accurate stroke subtyping. We sought to investigate the relationship between stroke characteristics and stroke etiology in 7,591 patients in the NINDS Stroke Genetics Network Study. Methods: We determined the probability of having a major etiology in the presence of certain stroke characteristics that were previously linked to specific stroke mechanisms. We excluded patients with multiple competing major etiologies. All patients had brain imaging, intra- and extra-cranial vascular imaging, and complete cardiac assessment. We defined major etiologies according to the Causative Classification of Stroke System Criteria without reference to the stroke characteristics under study. Results: History of 1 ipsilateral ischemic events within the last month, the presence of internal watershed infarcts or multiple, ipsilateral, unilateral, and temporally separate infarcts were associated with large artery atherosclerosis (Table). Multiple acute infarcts in different cerebral circulations were more common in patients with major cardiac source of embolism as well as in those with uncommon/cryptogenic causes. Stereotypic lacunar TIAs within the last week and lacunar syndrome were associated with lacunar infarction Discussion: Clinical and imaging stroke characteristics appear to predict stroke etiologies. This information may find utility in developing algorithms for a guided stroke evaluation as well as help identify the most likely causative etiology in the presence of multiple competing etiologies.