Citations

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.

Introduction: Heart Disease and Stroke Statistics_2014 Update suggests more than 424,000 people suffer out of hospital cardiac arrest (OHCA) in the United States. Hypothesis: We assessed seasonal variation in OHCA from a large national hospitalisation database in the past decade. Methods: The Nationwide Inpatient Sample database was used to estimate the annual number of hospitalisations with from 2000-2011. Identification of out of hospital cardiac arrest related hospitalisations was based on the designation of the International Classification of Diseases (9th Edition) Clinical Modification (ICD-9-CM) diagnosis code 427.5 (OHCA) as the principal discharge diagnosis. The frequency of hospitalisation for each month cumulative over 11 years was calculated and divided by number of days in that month to obtain the mean hospitalisations per day for each month. All calculations were carried out using the weighted estimates approximating nationwide population estimates. Results: An estimated 93,209 hospitalisations with primary diagnosis of OHCA occurred in the United States from the beginning of the calendar year 2000 to the end of the calendar year 2011. The number of hospitalisations per day in each month is shown in Figure 1.The number of hospitalisation was maximum in the winter months and minimum in summer months. Specifically, the mean number of hospitalisation each day (averaged over 11 years) was least in August (242). There was a rising trend from August to January. The average number of hospitalisation was highest in January (310); thereafter, the hospitalisation rate dropped to a nadir in August. There was however no seasonal pattern in inhospital mortality. Conclusions: We identified for the first time in United States an impressive pattern of seasonal variation in hospitalisations for OHCA. Further efforts must be made to identify triggers and methods to prevent OHCA and reduce its burden on health care system.

Objective: To identify possible racial and/or health insurance disparities with respect to ablation procedures for atrial fibrillation (AF). Method: Using the Nationwide Inpatient Sample (2000-2011), we identified adult patients (pts) admitted with principle diagnosis of AF by ICD 9 code 427.31 who had catheter ablation (ICD 9 code - 37.34). We stratified patients by race (White, Black, Hispanic, and other minority racial groups), insurance status, age, gender, type of hospital, and type of hospital admission. A hierarchical mixed effect multivariate model was created to identify independent predictors of AF ablation. Results: Among 4,547,144 patients hospitalized with AF during the study period, 201,447(4.43%) underwent ablation procedures. In absolute numbers, the majority of the ablations were performed in Whites (65.7%) and patients with Medicare (66.7%) or private insurance (24.7%). After adjusting for confounding factors, the proportions of Black pts (compare to White), and Medicare, Medicaid, and Uninsured pts (compare to private insurance), receiving ablation therapy were lower (see table). Odds of having ablation were lower in older and female patients and higher in teaching hospitals and after elective admissions (p<0.001 for all comparisions). Similar racial and insurance disparities were observed over the time course of the study. Conclusion: We identified lower rates of AF ablation among black pts even after correction of other factors such as insurance. Ablation rates were also lower among patients without private insurance. These trends persisted during the study period of 2000-2011.

Objective: To identify racial and/or health insurance disparities in the population undergoing ablation for paroxysmal supraventricular tachycardia(PSVT). Method: We identified adult patients (pts) admitted with principle diagnosis of PSVT by ICD 9 code 427.0 who had catheter ablation (ICD 9 code - 37.34), using the Nationwide Inpatient Sample (2000-2011). We stratified patients by race (White, Black, Hispanic, and other minority racial groups)and insurance status, and a hierarchical mixed effect multivariate model was created to identify independent predictors of PSVT ablation. Results: 64,564(20.2%) patients underwent ablation out of 319,579 patients admitted for PSVT during the period of 2000-2011. In absolute numbers, the majority of ablations were performed in Whites (58.7%) and patients with Medicare (56.3%) or private insurance (30.1%). After adjusting for confounding factors, the odds of having ablation were lower in Black pts (compare to White), and Medicare, Medicaid, and Uninsured pts (compare to private insurance) (see table). Other predictors of lower rate of ablations were: older age and female patients while odds of receiving ablation were higher for elective admissions and in teaching hospitals (p<0.001 for all comparisons). Conclusion: In a population of pts admitted for PSVT, those without private insurance and Black pts were less like to be treated with catheter ablation; and, this disparity persisted from 2000-2011. Since catheter ablation for PSVT is generally considered a curative procedure, future studies are needed to identify the factors that lead to these disparities of care.

Introduction: The availability, national scope and perceived unbiased nature of administrative claims data makes it an attractive choice by health care policy makers for evaluating quality and outcomes. We calculated Ventricular tachycardia (VT) ablation complication rates from national administrative data and compared them with data from randomized trials and prospective studies. Methods: A PUBMED database search was done to identify existing studies of VT ablation. Studies were divided into clinical (randomized controlled trials (RCTs) and prospective studies). Pooled rates of vascular, cardiac, neurological complications and procedure related mortality were computed and compared to reported complication rates from administrative database studies Results: We identified two administrative database studies of VT ablation that included a total of 14,352 procedures and 18 clinical studies with a total of 1,705 procedure. Overall complication rate in administrative studies was higher than in clinical studies (9.39% vs. 7.97%). Rates of cardiac/pericardial (4.47% vs. 2.29%) and vascular access complications (6.9% vs. 3.0%) were higher in real world studies. Neurological complications (1.17% vs. 0.45%) and procedure related mortality (1.53% vs. 1.29%) were higher in clinical studies. Among clinical studies, RCTs had the lowest reported complication rate. Conclusion: Despite substantial improvement, “real-world” complication rates from administrative data remain higher than those reported in RCTs. The discrepancy may be attributable to differences in operator skills, selection bias and variation in data quality. Use of large databases provides an important analytical resource as we embark on improving national health care efficacy and safety in the current challenging environment.

Abstract Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values<5x10-8 as genome-wide significant, and p-values<1x10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants.Results: None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values<1x105. The strongest evidence was found for rs4674019 (p-value=2.27x10-7), which showed genome-wide significant interaction (p-value=3.8x10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. Conclusions: In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

Abstract BackgroundCopy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data.ResultsGene burden tests detected the strongest association for deletions in BRCA1 (P= 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P= 0.0008), ATM (P= 0.002) and BRCA2 (P= 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci.ConclusionsThis is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.

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.