Kleindorfer, Dawn

Introduction: Intravenous alteplase (tissue plasminogen activator) has been shown to be cost-effective due to savings in long-term disability. In October of 2005, an increased DRG payment to hospitals for alteplase-treated stroke patients was introduced. We sought to describe the trends in the cost of alteplase over time, in comparison to trends in hospital reimbursement in the United States. Methods: Utilizing publicly available information on the Centers for Medicare and Medicaid Services (CMS) website (www.cms.gov), we obtained CMS quarterly payment amounts between 1/1/2005-10/1/2014 for alteplase, listed as “alteplase recombinant, per mg.” CMS payment amounts are the manufacturer’s average sales price (ASP) + 6% until 4/2014, when it was lowered to + 4.3%. Estimates for DRG base payments were calculated within MEDPAR for FY 2006 (DRG 559) and 2013 (MS DRGs 61,62, and 63) as: (DRG relative weights) x (standardized operating and capital amount). The Consumer Price Index (CPI) was also queried for all prescription drugs, urban areas, during the same study period as reference. Results: The CMS payment amount for alteplase/mg was $30.50 in 1/2005, and $64.30 in 10/2014. Trends in the CMS payment amounts for alteplase increased by 111% between 2005-2014, shown in the Figure. The CPI for all prescription drugs increased by 30.2% in the same time frame. The base payment for alteplase-treated stroke admissions was $11,173 in 2006, and $12,064 in 2013, an 8% increase. Discussion: We found a striking increase in the cost of alteplase over the last decade, with a 100mg vial now with a CMS payment of ∼$6400, a more than 100% increase over ten years. During the same timeframe, the DRG base payment to hospitals increased by only 8%, and alteplase cost increased from 27% of the payment in 2006 to 53% in 2013. Researchers and stroke physicians should be aware of these changes in drug costs and their impact on cost-effectiveness analyses.

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: Stroke symptoms in the general population are common (≈18% of adults 45+) and are associated with future stroke. These symptoms may also identify a broad spectrum of clinically-undetected vascular conditions. Methods: Stroke symptoms (sudden weakness, numbness, unilateral or general loss of vision, and loss of ability to communicate or understand) and time to self-reported hospitalization for any cardiovascular (CVD) reason (heart, stroke, or TIA) or stroke were assessed in the REGARDS Study (a national, population-based, longitudinal study of 30,239 African-American and white adults > 45 years enrolled 2003-2007). The symptom-hospitalization association was assessed by proportional hazards analysis in persons stroke/TIA-free at baseline, with adjustment for demographics (age, race and sex), further adjustment for SES (income and education), CVD risk factors (smoking, hypertension, diabetes, dyslipidemia, atrial fibrillation and heart disease), and “perceived health status.” Results: Over an average follow-up of 5.6 years, presence of any stroke symptom was associated with greater risk of hospitalization for stroke (11% versus 6%; HR = 1.68; 95% CI: 1.52 - 1.85), a risk only modestly attenuated by adjustment for SES (HR = 1.59; 95% CI: 1.44 - 1.76), risk factors (HR = 1.44; 95% CI: 1.29 - 1.60), and perceived health (HR = 1.37; 95% CI: 1.23 - 1.53). However, presence of any stroke symptom was more strongly related to hospitalization for CVD (42% vs. 24%; HR = 1.92; 95 CI: 1.82 - 2.03), only modestly mediated by adjustment for SES (HR = 1.82; 95% CI: 1.73 - 1.93), risk factors (HR = 1.66; 95% CI: 1.57 - 1.76), and perceived health (HR = 1.57; 95% CI: 1.48 - 1.66). Conclusions: Stroke symptoms appear to be a marker not only for stroke risk, but also for general CVD. Simple questions can identify 18% of the general population that have over a 40% chance of hospitalization for CVD over a 5-year horizon. Implications for potential intervention to reduce risk are profound.

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: 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.