Rhodes, David

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.

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 Carotid Revascularization Endarterectomy versus Stenting Trial (CREST) previously reported increased mortality in patients who sustained a periprocedural stroke or cardiac event (myocardial infarction [MI] or biomarker only) in follow-up to 4 years. We now extend these observations to 10 years. Methods and Results: CREST is a randomized controlled trial designed to compare the outcomes of carotid stenting versus carotid endarterectomy. Proportional hazards models were used to assess the association between mortality and periprocedural stroke, MI, or biomarker-only events. For 10-year follow-up, patients with periprocedural stroke were at 1.74× the risk of death compared with those without stroke (adjusted hazard ratio [HR]=1.74; 95% CI, 1.21–2.50; P <0.003). This increased risk was driven by increased early (between 0 and 90 days) mortality (adjusted HR=14.41; 95% CI, 5.33–38.94; P <0.0001), with no significant increase in late (between 91 days and 10 years) mortality (adjusted HR=1.40; 95% CI, 0.93–2.10; P =0.11). Patients with a protocol MI were at 3.61× increased risk of death compared with those without MI (adjusted HR=3.61; 95% CI, 2.28–5.73; P <0.0001), with an increased hazard both early (adjusted HR=8.20; 95% CI, 1.86–36.2; P =0.006) and late (adjusted HR=3.40; 95% CI, 2.09–5.53; P <0.0001). Patients with a biomarker-only event were at 2.04× increased risk overall (adjusted HR=2.04; 95% CI, 1.09–3.84; P =0.03) than those without MI, with an increased early hazard (adjusted HR=8.44; 95% CI, 1.09–65.5; P =0.04) and a suggestive but nonsignificant association toward higher 91-day to 10-year risk (1.88; 95% CI, 0.97–3.64; P =0.062) contributing to the increased risk. Conclusions: In the CREST trial, patients with periprocedural events demonstrate a substantial increase in future mortality to 10 years. For stroke, this risk is largely confined to an early time frame while periprocedural MI or biomarker-only events confer a continuous increased mortality for 10 years. Strategies to reduce periprocedural events and to optimize the evaluation and management of patients with cardiac events should be considered in efforts to reduce not only early but also long-term mortality. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT00004732.

Background and Purpose— NINDS (National Institute of Neurological Disorders and Stroke)-SiGN (Stroke Genetics Network) is an international consortium of ischemic stroke studies that aims to generate high-quality phenotype data to identify the genetic basis of pathogenic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium. Methods— Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major pathogenic groups without weighting toward the most likely cause) and causative ischemic stroke subtypes in 16 954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System. Classification reliability was assessed with blinded readjudication of 1509 randomly selected cases. Results— The distribution of pathogenic categories varied by study, age, sex, and race ( P <0.001 for each). Overall, only 40% to 54% of cases with a given major ischemic stroke pathogenesis (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (κ 0.72; 95% confidence interval, 0.69–0.75) and phenotypic classifications (κ 0.73; 95% confidence interval, 0.70–0.75). Conclusions— This study demonstrates that pathogenic 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 highlights the fact that the presence of an abnormality in a patient with stroke does not necessarily mean that it is the cause of stroke.