Kissela, Brett

Background: Administrative data such as Medicare data are commonly used for health services research and comparative effectiveness studies. These databases are readily available and a good source of long-term data on healthcare utilization and health outcomes in real-world settings. Outcomes are captured using diagnostic codes, but the evidence on the validity of these codes is scarce in contemporary Medicare data. Methods: We linked the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study data using SSN, date of birth and sex to 2003-2009 Medicare claims. In addition to the events captured by REGARDS phone interviews, medical charts were pursued for Medicare-identified strokes not previously identified in REGRADS. Events were adjudicated by stroke specialists using the retrieved medical charts. Using all the adjudicated strokes as gold standard, we calculated the sensitivity, specificity, PPV and NPV of inpatient stroke algorithms with ICD-9-CM codes [430, 431, 433.x1, 434.x1, 436] in the primary discharge diagnosis. Results: We successfully linked 15,089 REGARDS participants with mean age of 69.3, 52% female and 37% Black. We adjudicated 457 strokes, of which 48 were identified by claims only and not by interviews. The tested algorithms had high specificity (99.8-100%), PPV (88.6-90.5%) and NPV (98.9-99.9%), but low sensitivity (58.6-67.4%) (Table). Conclusions: High specificity and PPV of our algorithms to identify strokes in contemporary Medicare populations support their use in etiological and comparative effectiveness studies. While our inpatient Medicare algorithms identified 12% extra cases of strokes, their usefulness for estimating stroke incidence or related healthcare utilizations is limited by their low sensitivity. Linking cohorts to Medicare data is feasible and should be considered to increase the completeness of follow-up. Further studies are needed to evaluate more sensitive Medicare algorithms.

Introduction: Low rates of enrollment into stroke clinical trials are an ongoing challenge for the stroke research community. We sought to retrospectively describe the association between medical eligibility and recruitment rates for two acute clinical trials. Methods: Within a population of 1.3 million, we screened local hospital admissions in 2010 using ICD-9 discharge codes 430-436, and performed chart review. Within the region are 15 adult acute care hospitals, all of which are covered by the same stroke team for clinical care and trial enrollment. Medical eligibility, the number of eligible patients/total number of patients, was calculated for the IMS-III trial and the CLEAR-ER trials using their inclusion/exclusion criteria. The number of patients enrolled in the trial per month of open enrollment was determined for each hospital. Statistical analysis included paired signed rank test and regression. Results: In 2010, 2035 ischemic strokes (IS) were hospitalized in the region. Regional medical eligibility was 1.7% for IMS III and 4.5% for CLEARER (p=0.001), overall regional hospital enrollment/month was 0.81 for IMS III and 1.84 for CLEAR-ER (p=0.001). Eligibility rates per hospital ranged from 0-9.5%, and recruitment rates per hospital/mo ranged from 0-0.38. Hospital-level eligibility and recruitment rates were significantly correlated (see figure for both trials combined, adjusted r2 = 0.57, p<0.001.) Discussion: Within our population, medical eligibility explained 57% of the variability in hospital-level enrollment rates for two acute IS trials. The use of population-based epidemiology in trial planning may identify optimal trial enrollment criteria, which must be weighed against proper patient selection. Many other factors impact enrollment rates, such as competing trials, which were not considered here. Further evaluation of our population-based eligibility rates in sites outside our region and with additional clinical trials is needed.

Background: Levels of the pro-inflammatory cytokines interleukin (IL)-6 and IL-8 and the anti-inflammatory cytokine IL-10 are altered in acute stroke patients compared to controls. However, results for IL-10 are inconsistent (higher in stroke patients compared to controls in some studies and decreased in others) and very few studies have examined associations of these biomarkers with risk of incident stroke. Methods: We examined associations of baseline levels of IL-6, IL-8 and IL-10 with stroke risk factors and risk of incident stroke in 1,572 white and black men and women from the REGARDS Study; an observational cohort of 30,239 adults followed for 5 years. Among those without prebaseline stroke, stroke cases (n=592, 53% men, 59% white, mean age 70 years) were compared to a cohort random sample (n=980, 44% men, 58% white, mean age 65 years). We used Cox proportional hazards models to examine associations of biomarkers with incident stroke. Hazard ratios (HR; 95% confidence intervals) for highest compared to lowest quartile for each biomarker are presented. Results: Baseline IL-6 was significantly higher in incident stroke cases compared to the cohort sample (p<0.001) while IL-8 and IL-10 levels did not vary significantly (p>0.05 for both). Adjusting for age, sex and race, IL-6 was higher in blacks compared to whites and higher in current smokers compared to never/former smokers (all p≤0.01). IL-6 was inversely associated with physical activity, alcohol use and education (all p≤0.01). IL-8 and IL-10 did not vary significantly with the risk factors examined. Adjusting for age, sex and race, the HR for risk of incident stroke in those in the highest compared to lowest quartile of IL-6 was 2.4 (1.6-3.4). HRs for IL-8 and IL-10 were 1.5 (1.0-2.1) and 1.4 (1.0-1.9), respectively. Adjusting for Framingham stroke risk factors and history of coronary heart disease, only IL-6 remained significantly associated with stroke risk (HR 2.0; 1.3-3.2). HRs for IL-8 and IL-10 were 1.1 (0.7-1.6) and 1.2 (0.8-1.8), respectively. IL-6 remained significantly associated with stroke risk when C-reactive protein was added to the model (HR 1.7; 1.1-2.7). Associations did not differ by race. Conclusions: In this population-based sample of US black and white adults, IL-6, but not IL-8 or IL-10, was associated with stroke risk factors and risk of incident stroke. Further study is needed on the clinical utility of IL-6 measurement in stroke risk assessment.

Introduction: White matter hyperintensity volume (WMHv) is an important and highly heritable cerebrovascular phenotype; however, manual or semi-automated approaches to clinically acquired MRI analysis hinder large-scale studies in acute ischemic stroke (AIS). In this work, we develop a high-throughput, fully automated WMHv analysis pipeline for clinical fluid-attenuated inversion recovery (FLAIR) images to facilitate rapid genetic discovery in AIS. Methods: Automated WMHv extraction from multiple subjects relies on significant pre-processing of medical scans, including co-registration of the images. To reduce the effects of anisotropic voxel sizes, each FLAIR image is upsampled using bi-cubic interpolation. Brain extraction is performed using RObust Brain EXtraction (ROBEX). Images are then registered to an in-house FLAIR template using Advanced Normalization Tools (ANTs). The spatial covariation of WMH is learned through principal component analysis (PCA) of manual outlines from 100 subjects. Areas of leukoaraiosis are identified and separated from other lesions using the PCA modes. Volumes are then computed using non-interpolated slices for each subject. Standard deviation (SD) in WMHv (9 subjects; 6 raters each) is calculated as a measure of variability. Good agreement between automated and manual outlines is assessed in 358 subjects (automated WMHv within 3SD of manual WMHv). Results: As part of the MRI - Gen etics I nterface E xploration (MRI-GENIE) study, WMHv were calculated on a set of 2703 FLAIR images of patients from 12 independent AIS cohorts (sites). Results are shown in Figure 1. Comparing manual and automated WMHv shows that 88% of the automated WMHv fall within 3 SD from the manual WMHv, suggesting good agreement. Conclusion: WMHv segmentation using a fully-automated pipeline for analysis of clinical MRIs is both feasible and accurate. Ongoing analysis of the extracted WMHv is expected to advance current knowledge of risks and outcomes in AIS.

Introduction: Even though neuroinflammation is increasingly recognized as an essential contributor to ischemic brain injury, the exact underlying mechanisms remain unclear. Higher expression of the vascular cell adhesion molecule 1 (VCAM-1) increases leukocyte-brain endothelium interaction and has been associated with larger infarct size following acute ischemic stroke (AIS). The activation of the TGF-β signaling pathways can down-regulate VCAM-1 expression and ameliorate deleterious tissue outcome during neuroinflammation. Hypothesis: We sought to investigate whether genetic variation in the TGF-beta pathway and adhesion molecule genes is associated with acute stroke lesion size. Methods: We completed genome-wide association (GWA) testing and diffusion-weighted imaging lesion volume (DWIv) analysis in a discovery cohort of 532 AIS patients of European ancestry enrolled within 48 hours of symptom onset. An independent European ancestry cohort of 724 AIS patients with GWA data and automated DWIv served as replication cohort. GWA testing per SNP was performed using linear regression modeling of natural log-transformed DWIv adjusted for age, sex and relevant principal components. We selected 42 inflammatory genes in the TGF-beta pathway for a gene-based analysis using VEGAS (Versatile Gene-based -Association Study) software. A pre-specified discovery phase Bonferroni-corrected threshold was set at p<0.001. In the replication phase, 14 SNPs overlapping were tested at the Bonferroni-corrected p-value threshold of p<0.004. Results: Of all genes in the TGF-beta pathway, VCAM1 (p=0.0006) was significantly associated with DWIv in the discovery AIS cohort (age: 66 ± 14.9 years, sex: 63.4% male, DWIv: 2.2cm 3 (IQR: 0.6-11.7cm 3 )). A single SNP within the VCAM1 gene boundaries (rs3176876 (BETA=0.2341, p=0.003)) was significantly associated with DWIv in the replication AIS cohort (age: 65 ± 14.1 years, sex: 68.6% male, DWIv: 4cm 3 (IQR: 1.3-17.2cm 3 )). Conclusion: The genetic variant rs3176876 in the VCAM1 gene is associated with larger infarct lesion size measured on brain MRI of AIS patients. These findings suggest genetic contribution to the pro-inflammatory mechanisms in acute cerebral ischemia and warrant further investigation.

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<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<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<0.0001), as well as diabetes mellitus (β=0.13, SE=0.06, p=0.02). WMHv differed significantly (p<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.

Introduction: The MRI-Genetics Interface Exploration (MRI-GENIE) study is the first international collaboration that aims to facilitate genetic discoveries in clinical cohorts of patients with acute ischemic stroke (AIS). We have amassed the largest-to-date collection of AIS cases with brain MRI scans and genome-wide genotyping to test the role of genetic susceptibility in MRI-based cerebrovascular traits. Objective/Hypothesis: To elucidate the genetic architecture of white matter hyperintensity (WMH) burden in AIS patients. Methods: Using a novel automated algorithm, we extracted WMH volume (WMHv) from clinical MRI scans of 2704 AIS patients (age 63.1 ± 14.7 years, 60.6% male) of European ancestry. Quality control (QC) measures were undertaken per subject and per SNP, excluding subjects with non-European ancestry and poor genotyping, as well as SNPs deviating from Hardy-Weinberg equilibrium and high levels of missingness. Imputation to the Haplotype Reference Consortium (HRC version r1.1) was conducted for 1712 remaining subjects with 2.8 million SNPs on the Michigan Imputation Server. After exclusion of poorly imputed SNPs (R 2 <0.5) and SNPs with minor allele frequency < 1%, 7.7 million SNPs remained for further analysis. Genome-wide association testing of natural log-transformed WMHv on the allelic dosage per SNP was adjusted for age, sex and principal components 1-10. Results: Genome-wide association testing has identified a novel locus on chromosome 2 (T allele at rs72856504) near the LDL Receptor related Protein 1B gene (LRP1B) that was significantly associated with WMHv burden in AIS (β=0.54, SE=0.098, p=3.65*10 -8 ). Conclusion: We have identified a novel locus (T allele rs72856504) on chromosome 2 near the LRP1B gene, which is specific for WMH in AIS and has not been previosuly described in stroke-free WMH cohorts. A replication effort involving additional independent cohorts of AIS patients with brain MRI and genome-wide genotyping is ongoing.

OBJECTIVE Given temporal changes in diabetes prevalence and stroke incidence, this study investigated age, race, and sex differences in the diabetes–stroke association in a contemporary prospective cohort, the REasons for Geographic and Racial Differences in Stroke (REGARDS) Study. RESEARCH DESIGN AND METHODS We included 23,002 non-Hispanic black and white U.S. adults aged ≥45 years without prevalent stroke at baseline (2003–2007). Diabetes was defined as fasting glucose ≥126 mg/dL, random glucose ≥200 mg/dL, or use of glucose-lowering medication. Incident stroke events were expert adjudicated and available through September 2017. RESULTS The prevalence of diabetes was 19.1% at baseline. During follow-up, 1,018 stroke events occurred. Among adults aged &lt;65 years, comparing those with diabetes to those without diabetes, the risk of stroke was increased for white women (hazard ratio [HR] 3.72 [95% CI 2.10–6.57]), black women (HR 1.88 [95% CI 1.22–2.90]), and white men (HR 2.01 [95% CI 1.27–3.27]) but not black men (HR 1.27 [95% CI 0.77–2.10]) after multivariable adjustment. Among those aged ≥65 years, diabetes increased the risk of stroke for white women and black men, but not black women (HR 1.05 [95% CI 0.74–1.48]) or white men (HR 0.86 [95% CI 0.62–1.21]). CONCLUSIONS In this contemporary cohort, the diabetes–stroke association varied by age, race, and sex together, with a more pronounced effect observed among adults aged &lt;65 years. With the recent increase in the burden of diabetes complications at younger ages in the U.S., additional efforts are needed earlier in life for stroke prevention among adults with diabetes.