Worrall, Bradford B.

Introduction: Abdominal aortic aneurysm (AAA) rupture is associated with high mortality and morbidity. The U.S. Preventive Services Task Force recommends one-time ultrasound screening for AAA in men age 65-75 who have ever smoked. Little is known about other high-risk subpopulations. Recent linkage and genome-wide association studies confirm a shared genetic risk between AAA and intracranial aneurysms (IA). We tested the hypothesis that sonographic screening for AAA in individuals known to have IA is cost-effective. Methods: A decision tree model was developed to compare costs and outcomes of AAA screening compared to no screening in a hypothetical cohort of patients with IA. Event probabilities, utilities, and costs included in the model were derived through a comprehensive literature review. Expected outcomes of screening vs. no screening were measured in terms of quality-adjusted life years (QALY), lifetime costs and the overall cost-effectiveness ratio. Sensitivity analyses were performed to assess the effects of ranging baseline variables, within plausible minimum and maximum values, on model outcomes, as well as to identify thresholds of model variables where a decision alternative was dominated (both more expensive and less effective than the alternative). Results: In our base-case analysis, screening for AAA provided an additional 0.1055 QALYs (28.1521-screening, 28.0466-no screening) at an additional lifetime cost of $3.62 ($374.57-screening and $370.95-no screening). This yielded an incremental cost-effectiveness ratio (ICER) of $34.31/QALY. In sensitivity analyses, a prevalence of AAA in the IA population ≥ 7.5%, sensitivity of ultrasound screening ≥ 91%, or extrapolating the risk of AAA rupture to 6 years each caused the screening arm to dominate the model. Conclusion: Based on this model, screening for AAA in individuals with IA is cost-effective with an ICER of $34.31/QALY. This is well below accepted societal thresholds estimated to be less than $50,000/QALY. The sensitive variables in the model, particularly co-prevalence of IA and AAA, favored screening within conservative estimates from the literature. Prospective study evaluating the co-prevalence of IA and AAA will further elucidate the value of dedicated screening.

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.

The Vitamin Intervention for Stroke Prevention (VISP) trial was a multi-center, controlled, double blinded clinical trial, designed to determine whether the daily intake of high dose folic acid, vitamins B6 and B12 reduced recurrent cerebral infarction. Baseline post-methionine load homocysteine levels were predictive of recurrent stroke risk in VISP. We have conducted a genome-wide association study (GWAS), investigating 2100 VISP stroke cases required to have homocysteine levels greater than the 25th percentile at enrolment. Using imputation with 1000 genomes and principal component analysis, we were able to detect an association of p≤6.70x10-17 for SNPs within, or flanking, the glycine N-methyltransferase (GNMT) gene and post-methionine load test homocysteine level. Change in homocysteine levels pre and post-methionine load test also resulted in significant GWAS scores in the chromosome 6 region (p = 2.54x10-23, rs7760250). GNMT catalyzes the conversion of S-adenosyl-L-methionine to S-adenosyl-L-homocysteine and sarcosine via the folate one-carbon metabolism pathway. In addition to being an essential component in the conversion of methionine to homocysteine, GNMT plays a significant role in the global epigenetic state as homocysteine is an important methyl donor. This region displays significant linkage disequilibrium, warranting close analysis of the entire GNMT locus in search of both rare and common variants. Cases who carry the minor allele haplotype at associated loci had a higher level of post-methionine load homocysteine (37.69µM/L) than those who carry the major allele haplotype (27.33µM/L), inferring functional differences due to GNMT variants. Taken together these data suggest a clear and important role for GNMT in post-methionine load test homocysteine level, epigenetic state and stroke risk.

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.

Background: Fundamental advances in stroke care will require pooling imaging phenotype data from multiple centers, to complement the current aggregation of genomic, environmental, and clinical information. Sharing clinically acquired MRI data from multiple hospitals is challenging due to inherent heterogeneity of clinical data, where the same MRI series may be labeled differently depending on vendor and hospital. Furthermore, the de-identification process may remove data describing the MRI series, requiring human review. However, manually annotating the MRI series is not only laborious and slow but prone to human error. In this work, we present a recurrent convolutional neural network (RCNN) for automated classification of the MRI series. Methods: We randomly selected 1000 subjects from the MRI-GENetics Interface Exploration study and partitioned them into 800 training, 100 validation and 100 testing subjects. We categorized the MRI series into 24 groups (see Table). The RCNN used a modified AlexNet to extract features from 2D slices. AlexNet was pretrained on ImageNet photographs. Since clinical MRI are 3D and 4D, a gated recurrent unit neural network was used to aggregate information from multiple 2D slices to make the final prediction. Results: We achieved a classification accuracy (correct/total cases) of 99.8%, 98.5% and 97.5% on the training, validation and testing set, respectively. The averaged F1-score (percent overlap between predicted cases and actual cases) over all categories were 99.8% 98.2% and 94.4% on the training, validation and testing set. Conclusion: We showed that automated annotation of MRI series by repurposing deep-learning techniques used for photographic image recognition tasks is feasible. Such methods can be used to facilitate high throughput curation of MRI data acquired across multiple centers and enable scientifically productive collaboration by researchers and, ultimately enhancing big data stroke research.

Introduction: Genetic studies of early-onset disease have been an effective strategy to identify novel pathways and drug targets relevant to later-onset disease. Few studies have investigated the role of common genetic variation in the etiology of early-onset ischemic stroke (IS). Methods: We performed a GWAS meta-analysis of 38 studies from 10 countries, comprised of 5,847 IS cases of European ancestry under age 60 and 32,533 controls. Results: We identified two genome-wide significant (p< 5 x 10 -8 ) loci (see Figure). The ABO locus has previously been associated with venous thrombosis and ischemic stroke in predominantly older adults, but the effect size of our top SNP (OR 1.18; p = 9.1 x 10 -12 ) is larger than the effect size for this same SNP in MEGASTROKE (OR: 1.05; p = 6.5 x 10 -5 ). The lead SNP at the BCL7A/MLXIP locus is a novel GWAS finding for stroke (OR 1.14, 95% CI 1.08-1.19; p = 1.7 x 10 -8 ) and is noteworthy because of prior reports linking SNPs in these genes to BMI and blood pressure. Conclusions: We identified a novel locus that is near variants associated with BMI and blood pressure. Further studies are needed to confirm this locus, examine subtype specificity, and determine its function. The larger effect size observed at the ABO in this early-onset IS sample compared to older-onset IS samples is consistent with a larger role for prothrombotic mechanisms in early-onset IS.