240 Scientific Programme 22. European Stroke Conference © 2013 S. Karger AG, Basel 5 Small vessel stroke and white matter disease Computational quantification of perivascular spaces (count and volume) in ischemic stroke patients is associated with white matter hyperintensities and cerebral atrophy. X. WANG1, M.C. Valdés Hernánde2, F.M. Chappell3, F.N. Doubal4, J.M. Wardlaw5 School of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UNIT-ED KINGDOM1, Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UNIT-ED KINGDOM2, Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UNITED KINGDOM3, Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UNITED KINGDOM4, Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UNITED KINGDOM5 Background: Perivascular spaces (PVS) increase with ageing, cerebral small vessel disease, inflammation and may be related to increased blood brain barrier permeability. Previous studies used visual rating scales for quantification. We investigated the associations between basal ganglia (BG) PVS count/ volume, white matter hyperintensity (WMH) rating scores and volume, atrophy rating scores and brain volume. Methods: We used T2-weighted MR images from 96 patients with lacunar or mild cortical stroke. We mea-sured the PVS count and volume using a novel, threshold-based computational method developed in house. We rated WMH (periventricular and deep: PVL and DWML) and atrophy using validated scales. We measured WMH volume, brain volume and intracranial volume (ICV) using validated software (MCMxxxVI) and Analyze. We tested associations between PVS count/volume and other factors with linear regression, and used coefficients of variation (CV) to assess model fit. Results: In 96 patients, BG PVS volume was positively associated with WMH rating (PVL: 0.027 95%CI 0.015 to 0.049, CV=49%; DWML: 0.019, CI 0.007 to 0.031, CV=51%), WMH volume (0.0009, CI 0.0006 to 0.0013, CV=48%), and brain atrophy rating (0.0089, CI 0.0023 to 0.016, CV=52%). BG PVS volume increased as brain volume (as % of ICV) decreased (-0.0032, CI -0.0057 to -0.0007, CV=52%). Similar CV values showed the measures of WMH and cerebral atrophy have relation-ships of equal strength with PVS volume. BG PVS count showed similar associations. Conclusion: BG PVS count and volume are associated with both WMH (PVL and DWML) rating and volume, atrophy rating, and brain volume. Computational measures of PVS are feasible and informative in analysis and quantification of small vessel disease. Table 1. Associations between risk factors and incidence of lacunar infarcts in deep white matter and basal ganglia during follow-up. Adjusted for age and sex (model 1) and additionally for baseline WML volume (model 2). Only risk factors with significant associations are shown. p≤0.05, **=p≤0.01, ***=p≤0.001 Risk factor Model New LACI in deep white matter (n=68) New LACI in basal ganglia (n=66) History of lacunar in-farcts in deep WM (y/n) Model 1 Model 2 5.1 (2.5 to 10.4)*** 4.1 (1.9 to 8.8)*** 4.7 (2.4 to 9.2)*** 3.1 (1.5 to 6.6)** History of lacunar in-farcts in basal ganglia (y/n) Model 1 Model 2 6.9 (3.2 to 10.8)*** 5.9 (2.7 to 12.8)** 10.8 (5.4 to 21.8)*** 9.0 (4.3 to 18.7)*** History of cerebrovascu-lar disease (y/n) Model 1 Model 2 2.6 (1.3 to 5.2)** 2.4 (1.2 to 4.8)* 2.6 (1.3 to 4.9)* 2.1 (1.1 to 4.1)* Hypertension (y/n) Model 1 Model 2 4.0 (1.4 to 11.4)* 3.6 (1.3 to 10.5)* 2.0 (0.9 to 4.5) 1.7 (0.8 to 3.9) Hyperhomocysteinemia (y/n) Model 1 Model 2 2.0 (0.8 to 4.7) 1.5 (0.6 to 3.8) 3.3 (1.6 to 6.8)** 2.6 (1.2 to 5.8)* Progression WML vo-lume (y/n) Model 1 Model 2 3.2 (1.5 to 6.5)** 2.4 (1.1 to 5.3)* 1.6 (0.8 to 3.1) 0.8 (0.3 to 1.7) Intima Media Thickness (per 1 mm) Model 1 Model 2 2.9 (1.3 to 6.7)** 3.0 (1.3 to 6.9) * 2.0 (0.9 to 4.6) 2.0 (0.9 to 4.7) Smoking (per packyear) Model 1 Model 2 1.02 (1.0 to 1.03)* 1.02 (1.0 to 1.03)* 1.01 (1.0 to 1.03) 1.02 (1.0 to 1.03)
Karger_ESC London_2013
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