Causative Classification Of Stroke Research Articles

Highlights From the Circulation Family of Journals.

Highlights From the Circulation Family of Journals.

Subtype Specificity of Genetic Loci Associated With Stroke in 16 664 Cases and 32 792 Controls.

Genome-wide association studies have identified multiple loci associated with stroke. However, the specific stroke subtypes affected, and whether loci influence both ischemic and hemorrhagic stroke, remains unknown. For loci associated with stroke, we aimed to infer the combination of stroke subtypes likely to be affected, and in doing so assess the extent to which such loci have homogeneous effects across stroke subtypes. We performed Bayesian multinomial regression in 16 664 stroke cases and 32 792 controls of European ancestry to determine the most likely combination of stroke subtypes affected for loci with published genome-wide stroke associations, using model selection. Cases were subtyped under 2 commonly used stroke classification systems, TOAST (Trial of Org 10172 Acute Stroke Treatment) and causative classification of stroke. All individuals had genotypes imputed to the Haplotype Reference Consortium 1.1 Panel. Sixteen loci were considered for analysis. Seven loci influenced both hemorrhagic and ischemic stroke, 3 of which influenced ischemic and hemorrhagic subtypes under both TOAST and causative classification of stroke. Under causative classification of stroke, 4 loci influenced both small vessel stroke and intracerebral hemorrhage. An EDNRA locus demonstrated opposing effects on ischemic and hemorrhagic stroke. No loci were predicted to influence all stroke subtypes in the same direction, and only one locus (12q24) was predicted to influence all ischemic stroke subtypes. Heterogeneity in the influence of stroke-associated loci on stroke subtypes is pervasive, reflecting differing causal pathways. However, overlap exists between hemorrhagic and ischemic stroke, which may reflect shared pathobiology predisposing to small vessel arteriopathy. Stroke is a complex, heterogeneous disorder requiring tailored analytic strategies to decipher genetic mechanisms.

Design and rationale for examining neuroimaging genetics in ischemic stroke: The MRI-GENIE study.

Objective:To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI–GENetics Interface Exploration (MRI-GENIE) study.Methods:MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include the manual and automated assessments of established MRI markers. A high-throughput MRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease.Conclusions:The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.

Assessment of the Predictive Validity of Etiologic Stroke Classification

The ability of present-day etiologic stroke classification systems to generate subtypes with discrete stroke characteristics is not known. To test the hypothesis that etiologic stroke subtyping identifies different disease processes that can be recognized through their different clinical courses. We performed a head-to-head evaluation of the ability of the Causative Classification of Stroke (CCS), Trial of Org 10172 in Acute Stroke Treatment (TOAST), and ASCO (A for atherosclerosis, S for small-vessel disease, C for cardiac source, and O for other cause) classification systems to generate etiologic subtypes with different clinical, imaging, and prognostic characteristics in 1816 patients with ischemic stroke. This study included 2 cohorts recruited at separate periods; the first cohort was recruited between April 2003 and June 2006 and the second between June 2009 and December 2011. Data analysis was performed between June 2014 and May 2016. Separate teams of stroke-trained neurologists performed CCS, TOAST, and ASCO classifications based on information available at the time of hospital discharge. We assessed the association between etiologic subtypes and stroke characteristics by computing receiver operating characteristic curves for binary variables (90-day stroke recurrence and 90-day mortality) and by calculating the ratio of between-category to within-category variability from the analysis of variance for continuous variables (admission National Institutes of Health Stroke Scale score and acute infarct volume). Among the 1816 patients included, the median age was 70 years (interquartile range, 58-80 years) (830 women [46%]). The classification systems differed in their ability to assign stroke etiologies into known subtypes; the size of the undetermined category was 33% by CCS, 53% by TOAST, and 42% by ASCO (P < .001 for all binary comparisons). All systems provided significant discrimination for the validation variables tested. For the primary validation variable (90-day recurrence), the area under the receiver operating characteristic curve was 0.71 (95% CI, 0.66-0.75) for CCS, 0.61 (95% CI, 0.56-0.67) for TOAST, and 0.66 (95% CI, 0.60-0.71) for ASCO (P = .01 for CCS vs ASCO; P < .001 for CCS vs TOAST; P = .13 for ASCO vs TOAST). The classification systems exhibited similar discrimination for 90-day mortality. For admission National Institutes of Health Stroke Scale score and acute infarct volume, CCS generated more distinct subtypes with higher between-category to within-category variability than TOAST and ASCO. Our findings suggest that the major etiologic stroke subtypes are distinct categories with different stroke characteristics irrespective of the classification system used to identify them. We further show that CCS generates discrete etiologic categories with more diverse clinical, imaging, and prognostic characteristics than either TOAST or ASCO.

Abstract TMP58: Determinants of White Matter Hyperintensity in Acute Ischemic Stroke Patients: The MRI-GENIE Study

Introduction: White matter hyperintensity (WMH) is a highly heritable trait and a significant contributor to stroke risk and severity. Vascular risk factors contribute to WMH severity; however, knowledge of the determinants of WMH in acute ischemic stroke (AIS) is still limited. Hypothesis: WMH volume (WMHv) varies across AIS subtypes and is modified by vascular risk factors. Methods: We extracted WMHv from the clinical MRI scans of 2683 AIS subjects from the MRI-Genetics Interface Exploration (MRI-GENIE) study using a novel fully-automated, volumetric analysis pipeline. Demographic data, stroke risk factors and stroke subtyping for the Causative Classification of Stroke (CCS) were performed at each of the 12 international study sites. WMHv was natural log-transformed for linear regression analyses. Results: Median WMHv was 5.7cm 3 (interquartile range (IQR): 2.2-12.8cm 3 ). In univariable analysis, age (63.1 ± 14.7 years, β=0.04, SE=0.002), prior stroke (10.2%, β=0.66, SE=0.08), hypertension (65.4%, β=0.75, SE=0.05), diabetes mellitus (23.1%, β=0.35, SE=0.06), coronary artery disease (17.6%, β=0.04, SE=0.002), and atrial fibrillation (14.6%, β=0.48, SE=0.07) were significant predictors of WMHv (all p&lt;0.0001), as well as smoking status (52.2%, β=0.15, SE=0.05, p=0.005), race (16.5% Non-Caucasian, β=0.25, SE=0.07) and ethnicity (8.2% Hispanic, β=0.30, SE=0.11) (all p&lt;0.01). In multivariable analysis, age (β=0.04, SE=0.002), prior stroke (β=0.56, SE=0.08), hypertension (β=0.33, SE=0.05), smoking status (β=0.16, SE=0.05), race (β=0.42, SE=0.06), and ethnicity (β=0.34, SE=0.09) were independent predictors of WMHv (all p&lt;0.0001), as well as diabetes mellitus (β=0.13, SE=0.06, p=0.02). WMHv differed significantly (p&lt;0.0001, unadjusted) across CCS stroke subtypes: cardioembolic stroke (8.0cm 3 , IQR: 4.2-15.4cm 3 ), large-artery stroke (6.9cm 3 , IQR: 3.1-14.7cm 3 ), small-vessel stroke (5.8cm 3 , IQR: 2.5-13.5cm 3 ), stroke of undetermined (4.7cm 3 , IQR: 1.6-11.0cm 3 ) or other (2.55cm 3 , IQR: 0.9-8.8cm 3 ) causes. Conclusion: In this largest-to-date, multicenter hospital-based cohort of AIS patients with automated WMHv analysis, common vascular risk factors contribute significantly to WMH burden and WMHv varies by CCS subtype.

Abstract WP176: Assessing Genome-wide Associations With Ischemic Stroke Using Alternate Definitions of Stroke Subtypes: The SiGN Study

Stroke subtype classification, applying standard criteria to clinical data, can reduce the heterogeneity of ischemic stroke for genetics studies. To date, most of the replicated genetic loci for ischemic stroke appear subtype-specific. Increasing homogeneity of phenotype comes with a trade-off in sample size, which influences the potential for successful identification of new loci. We estimated genetic associations using the union and intersection of two widely used stroke subtyping systems to assess the influence of sample size and homogeneity on test statistics. Methods: The Stroke Genetics Network (SiGN) study used both the Causative Classification of Stroke (CCS) and the Trial of ORG10172 Acute Stroke Trial (TOAST) classification systems. The CCS generates both a causal and a phenotypic subtype. Using all available data from case-control strata from the a previously performed GWAS, we focused on three main stroke subtypes: cardioembolic (CE), large artery atherosclerosis (LAA), and small artery occlusion (SAO). We estimated genetic associations with the union of the two CCS outputs and TOAST ( eg called CE by at least one method) and the intersection ( eg called CE by all three methods). Our modelling approach included a fixed-effects meta-analysis, i.e. pooling stratum specific estimates from logistic regression models using 10 principle components to account for genetic ancestry responsible for population stratification. Results: The majority of confirmatory findings from SiGN ( PITX2 and ZFHX3 for CE and HDAC9 for LAA) were evident in both models and in both the union and intersection of the subtyping systems. Some findings ( TSPAN2 in LAA) showed up best in the intersection with stronger corresponding p-values and odds ratios whereas other findings (12q24 for SAO) appear stronger in all of the union analyses, but were also evident in intersection analyses. We identify 16 potential novel loci, 6 of which appear in numerous analyses. Conclusion: Both expanding and refining stroke subtypes may help in the identification of additional stroke genetic risk variants and should be considered as complementary to a single classification system. We are pursuing replication of the novel findings.

Emerging Risk Factors for Stroke: What Have We Learned From Mendelian Randomization Studies?

Establishing new approaches for the prevention and treatment of stroke relies on identifying modifiable risk factors that contribute to the development of this complex disease. Mendelian randomization (MR) studies, analogous to naturally occurring randomized trials, can assess causality of potentially modifiable biomarkers and offer new insights into biological pathways. Stroke is the second leading cause of death worldwide and the chief determinant of long-term disability. Stroke is a heterogeneous disease arising from several distinct underlying pathologies and is typically classified as ischemic or hemorrhagic, and further subclassified using imaging data. Ischemic stroke (IS), including its 3 main subtypes: small vessel disease, large vessel disease, and cardioembolic stroke, accounts for ≈80% of stroke and is the result of an interrupted blood supply, leading to localized areas of ischemia in the brain. Small vessel disease may be a consequence of nonatherosclerotic, as well as atherosclerotic, mechanisms that result in an occlusion of the small perforating arteries, whereas large vessel disease results from occlusions or emboli from plaque rupture in larger vessels, such as a carotid artery. Cardioembolic stroke arises typically from emboli from the heart. By contrast, hemorrhagic stroke is a consequence of intracerebral hemorrhage (bleeding into the brain) or subarachnoid hemorrhage (bleeding into the subarachnoid space). These diverse stroke subtypes have distinct underlying pathologies reflecting different risk factor distributions. MR studies, using genetic variants as instrumental variables, afford a powerful approach to assessing causality of risk factors and avoid biases inherent in observational studies, including confounding and reverse causation. This review considers the contribution of MR studies to stroke epidemiology and their relevance to understanding risk factors and new therapeutic targets for stroke. Meta-analyses of large prospective studies have enhanced our knowledge of classical and emerging risk factors for stroke.1–4 Classical risk factors for stroke include nonmodifiable characteristics, …

Loci associated with ischaemic stroke and its subtypes (SiGN): a genome-wide association study

The discovery of disease-associated loci through genome-wide association studies (GWAS) is the leading genetic approach to the identification of novel biological pathways underlying diseases in humans. Until recently, GWAS in ischaemic stroke have been limited by small sample sizes and have yielded few loci associated with ischaemic stroke. We did a large-scale GWAS to identify additional susceptibility genes for stroke and its subtypes. To identify genetic loci associated with ischaemic stroke, we did a two-stage GWAS. In the first stage, we included 16 851 cases with state-of-the-art phenotyping data and 32 473 stroke-free controls. Cases were aged 16 to 104 years, recruited between 1989 and 2012, and subtypes of ischaemic stroke were recorded by centrally trained and certified investigators who used the web-based protocol, Causative Classification of Stroke (CCS). We constructed case-control strata by identifying samples that were genotyped on nearly identical arrays and were of similar genetic ancestral background. We cleaned and imputed data by use of dense imputation reference panels generated from whole-genome sequence data. We did genome-wide testing to identify stroke-associated loci within each stratum for each available phenotype, and we combined summary-level results using inverse variance-weighted fixed-effects meta-analysis. In the second stage, we did in-silico lookups of 1372 single nucleotide polymorphisms identified from the first stage GWAS in 20 941 cases and 364 736 unique stroke-free controls. The ischaemic stroke subtypes of these cases had previously been established with the Trial of Org 10 172 in Acute Stroke Treatment (TOAST) classification system, in accordance with local standards. Results from the two stages were then jointly analysed in a final meta-analysis. We identified a novel locus (G allele at rs12122341) at 1p13.2 near TSPAN2 that was associated with large artery atherosclerosis-related stroke (first stage odds ratio [OR] 1·21, 95% CI 1·13-1·30, p=4·50 × 10-8; joint OR 1·19, 1·12-1·26, p=1·30 × 10-9). Our results also supported robust associations with ischaemic stroke for four other loci that have been reported in previous studies, including PITX2 (first stage OR 1·39, 1·29-1·49, p=3·26 × 10-19; joint OR 1·37, 1·30-1·45, p=2·79 × 10-32) and ZFHX3 (first stage OR 1·19, 1·11-1·27, p=2·93 × 10-7; joint OR 1·17, 1·11-1·23, p=2·29 × 10-10) for cardioembolic stroke, and HDAC9 (first stage OR 1·29, 1·18-1·42, p=3·50 × 10-8; joint OR 1·24, 1·15-1·33, p=4·52 × 10-9) for large artery atherosclerosis stroke. The 12q24 locus near ALDH2, which has previously been associated with all ischaemic stroke but not with any specific subtype, exceeded genome-wide significance in the meta-analysis of small artery stroke (first stage OR 1·20, 1·12-1·28, p=6·82 × 10-8; joint OR 1·17, 1·11-1·23, p=2·92 × 10-9). Other loci associated with stroke in previous studies, including NINJ2, were not confirmed. Our results suggest that all ischaemic stroke-related loci previously implicated by GWAS are subtype specific. We identified a novel gene associated with large artery atherosclerosis stroke susceptibility. Follow-up studies will be necessary to establish whether the locus near TSPAN2 can be a target for a novel therapeutic approach to stroke prevention. In view of the subtype-specificity of the associations detected, the rich phenotyping data available in the Stroke Genetics Network (SiGN) are likely to be crucial for further genetic discoveries related to ischaemic stroke. US National Institute of Neurological Disorders and Stroke, National Institutes of Health.

Etiologic Classification of TIA and Minor Stroke by A-S-C-O and Causative Classification System as Compared to TOAST Reduces the Proportion of Patients Categorized as Cause Undetermined

Background: The assortment of patients based on the underlying pathophysiology is central to preventing recurrent stroke after a transient ischemic attack and minor stroke (TIA-MS). The causative classification of stroke (CCS) and the A-S-C-O (A for atherosclerosis, S for small vessel disease, C for Cardiac source, O for other cause) classification schemes have recently been developed. These systems have not been specifically applied to the TIA-MS population. We hypothesized that both CCS and A-S-C-O would increase the proportion of patients with a definitive etiologic mechanism for TIA-MS as compared with TOAST. Methods: Patients were analyzed from the CATCH study. A single-stroke physician assigned all patients to an etiologic subtype using published algorithms for TOAST, CCS and ASCO. We compared the proportions in the various categories for each classification scheme and then the association with stroke progression or recurrence was assessed. Results: TOAST, CCS and A-S-C-O classification schemes were applied in 469 TIA-MS patients. When compared to TOAST both CCS (58.0 vs. 65.3%; p < 0.0001) and ASCO grade 1 or 2 (37.5 vs. 65.3%; p < 0.0001) assigned fewer patients as cause undetermined. CCS had increased assignment of cardioembolism (+3.8%, p = 0.0001) as compared with TOAST. ASCO grade 1 or 2 had increased assignment of cardioembolism (+8.5%, p < 0.0001), large artery atherosclerosis (+14.9%, p < 0.0001) and small artery occlusion (+4.3%, p < 0.0001) as compared with TOAST. Compared with CCS, using ASCO resulted in a 20.5% absolute reduction in patients assigned to the ‘cause undetermined' category (p < 0.0001). Patients who had multiple high-risk etiologies either by CCS or ASCO classification or an ASCO undetermined classification had a higher chance of having a recurrent event. Conclusion: Both CCS and ASCO schemes reduce the proportion of TIA and minor stroke patients classified as ‘cause undetermined.' ASCO resulted in the fewest patients classified as cause undetermined. Stroke recurrence after TIA-MS is highest in patients with multiple high-risk etiologies or cryptogenic stroke classified by ASCO.

Agreement between TOAST and CCS ischemic stroke classification

The objective of this study was to assess the level of agreement between stroke subtype classifications made using the Trial of Org 10172 Acute Stroke Treatment (TOAST) and Causative Classification of Stroke (CCS) systems. Study subjects included 13,596 adult men and women accrued from 20 US and European genetic research centers participating in the National Institute of Neurological Disorders and Stroke (NINDS) Stroke Genetics Network (SiGN). All cases had independently classified TOAST and CCS stroke subtypes. Kappa statistics were calculated for the 5 major ischemic stroke subtypes common to both systems. The overall agreement between TOAST and CCS was moderate (agreement rate, 70%; κ = 0.59, 95% confidence interval [CI] 0.58-0.60). Agreement varied widely across study sites, ranging from 28% to 90%. Agreement on specific subtypes was highest for large-artery atherosclerosis (κ = 0.71, 95% CI 0.69-0.73) and lowest for small-artery occlusion (κ = 0.56, 95% CI 0.54-0.58). Agreement between TOAST and CCS diagnoses was moderate. Caution is warranted when comparing or combining results based on the 2 systems. Replication of study results, for example, genome-wide association studies, should utilize phenotypes determined by the same classification system, ideally applied in the same manner.

Abstract 26: Agreement Between TOAST and CCS Classification in SiGN

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

Abstract 17: Ischemic Stroke Profile, Risk Factors and Outcomes in India: The Indo-US Stroke Project

Background: There is limited data about ischemic stroke in India. We characterized the clinical profile, risk factors and predictors of outcome after stroke in the Indian population, and investigated regional variations. Methods: The Indo-US Stroke Registry and Infrastructure Development Project, jointly funded by NINDS and the Indian Department of Biotechnology, currently includes 5 geographically diverse centers in North and South India and one in Boston, USA. Trained MD co-investigators and research coordinators prospectively collect data on consecutive adult patients with imaging-confirmed ischemic stroke &lt;2 weeks after symptom onset. Data is entered into a central web-based electronic database. Results: From Nov-2012 to Jun-2013, 967 patients were enrolled. Mean age 59.7±13.9 yrs (range 20-92), 66% males. The patient population was 65% Hindu, 21% Sikh, 7% Christian, 6% Muslim, and 0.3% Jain; 58% had government health insurance. Only 3% arrived via EMS. Stroke risk factors included hypertension (66%), diabetes (39%), hyperlipidemia (20%), CAD (16%), atrial fibrillation (7.4%, rheumatic heart disease (5%). In addition to cigarette smoking (20%), exposure to beedi, hukkah, and smokeless tobacco (paan) was common (13%). The median admission NIHSS score was 9 (IQR, 5-15). Stroke subtypes using the automated ‘Causative Classification of Stroke’ program were large-artery atherosclerosis 31%, cardio-embolism 24%, small-vessel disease 15%, other defined causes 6%, and undetermined 24%. There were several regional differences, eg. hyperlipidemia, North 5% vs South 42% (p&lt;0.001), good outcome (discharge mRS 0-2), North 45% vs South 57, p&lt;0.001). Multivariate logistic regression analysis adjusting for age, gender and stroke severity showed that large artery atherosclerosis (OR 2.24, CI 2.18-2.37, p=0.02) and MCA territory strokes (OR 3.48, CI 2.02-3.67, p&lt;0.0001) were predictors of poor outcome (mRS 3-6). Conclusions: The data provide insights about stroke in a large developing country. Resources are needed to address risk factors, regional differences, and develop the infrastructure for stroke care in India.

Abstract T P153: Women and Men with Ischemic Stroke in SiGN Have Different Subtype Distributions and Risk Factors

Introduction: The relative distribution of stroke risk factors, as well as ischemic stroke subtypes, in women compared with men is not well described. Hypothesis: We hypothesized that the distribution of ischemic stroke risk factors and subtypes would differ by sex, with a later onset in women and greater proportion of comorbidities. Methods: The NINDS Stroke Genetics Network (SiGN) consortium was established to evaluate genetic risk factors for ischemic stroke. A total of 23 separate studies performed Causative Classification of Stroke (CCS) typing using standardized criteria on ischemic stroke cases and contributed data on risk factors. We compared the distribution of ischemic stroke risk factors and CCS phenotypes between men and women with ischemic stroke. Results: Of the 16,228 ischemic strokes in SiGN, 8005 (49.3%) occurred in women. Median age at stroke was older in female than male stroke cases (73 vs. 66 years) (p=&lt;0.0001). Among stroke cases, women were more likely than men cases to have hypertension or atrial fibrillation and less likely to have diabetes or coronary artery disease, or to smoke (p &lt;0.003 for all). The distribution of stroke subtypes also differed by sex, with women less likely than men to have large artery infarction and small artery occlusion, and more likely to have cardioembolic stroke and undetermined stroke due to incomplete work-up (p values all &lt;0.0001; see Table). Results were similar when the distribution of stroke subtypes was examined for those &lt;70 years and ≥70 years, except for cardioembolic stroke remaining more common only among women ≥70. Conclusions: In this large group of carefully phenotyped ischemic strokes, the distribution of ischemic stroke subtypes and risk factor profiles differ significantly by sex. Evaluation of the causes of these differences may highlight areas for improved prevention and risk reduction in both genders.

Abstract TMP49: Yield of Diagnostic Evaluation in Major Stroke Phenoytypes

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.

Abstract 34: Improvement In Etiological Identification In Tia And Minor Stroke Using The Causative Classification Of Stroke.

Background: classification of Transient Ischemic attacks (TIA) and minor stroke is challenging, as there is no classification systems developed specifically for the TIA and minor stroke patient population. Hypothesis: We hypothesize that the newly developed Causative Classification System (CCS) and the Atherosclerosis Small Vessel Disease Cardiac Source Other Source (ASCO) classification would reduce the proportion of patients classified as cause undetermined compared with The Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification in a large prospectively evaluated TIA and Minor stroke population. Methods: Using published algorithms for TOAST, CCS, and ASCO, a single rater classified the etiology in patients presenting with a high-risk TIA (weakness or speech disturbance lasting ≥ 5minutes) or minor ischemic stroke (National Institute of Health Stroke Scale score ≤ 3) who underwent CT/CTA and subsequent MRI as part of the CATCH study. Results: 419 patients with TIA or Minor stroke were classified using TOAST, CCS, and ASCO. The proportion of patients with an undetermined etiology was 51.3% (215/419) with TOAST. This was significantly reduced by both CCS 36% (151/419, p&lt; 0.001) and ASCO 41% (172/419, p&lt; 0.001). CCS was also less likely to have an undetermined etiology as compared to ASCO (36% versus 41%, p = 0.024). When compared with TOAST, there was a 23.9% (95%CI:18.1- 29.7, P&lt; 0.001) and 17.4% (10.1- 24.7, P&lt; 0.001) reduction in the proportion of patients assigned to the undetermined group using CCS and ASCO respectively. The 8.5 % reduction in the undetermined group between CCS and ASCO was also statistically different P=0.031). Compared with ASCO1, CCS increased the assignment of patients to large artery disease (relative increase 7.4% {4.3-10.4}, P&lt; 0.001) and Cardio-embolism/cardio-aortic categories (relative increase 8.1% {4.6-11.5}, P&lt; 0.001). Conclusions: Both CCS and ASCO were superior to TOAST in assigning fewer patients to an undetermined etiology category. CCS was superior to ASCO at reducing the proportion of patients with undetermined etiology. This was largely driven by increased assignment in the large artery and Cardio-aorto embolic categories.

Abstract 11: The Utility of Causative Classification System: Correlation between Causative and Phenotypic Stroke Subtypes

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.

The diagnostic efficiency of transesophageal compared to transthoracic echocardiographic findings from 405 patients with ischemic stroke

This study compared the diagnostic efficacy of transthoracic echocardiography (TTE) and transesophageal echocardiography (TEE) for patients with stroke. Examination with TTE was followed by TEE. Stroke causes were classified according to the Trial of Org 10172 in Acute Stroke Treatment (TOAST) and Causative Classification of Stroke (CCS) criteria before and after TEE to determined, cryptogenic or unclassified groups. We identified that 55.1% of patients had a determined etiology for stroke with TTE; adding TEE to the examinations increased the proportion of patients with determined stroke etiology to 69.4% ( κ = 0.79). TTE alone showed fair agreement with the combined results of TTE and TEE in determining the cause of stroke in young adults ( κ = 0.38). TEE improved the detection of cardio-aortic sources, but had no impact on patients who had a determined cause for stroke with other examinations. TEE examination was most useful in young adults with stroke and in patients with an undetermined cause for stroke despite TTE examination and other standard investigations.