title: Clinical determinants of infarct pattern subtypes in large vessel atherosclerotic stroke.
abstract: BACKGROUND
  Although stroke from large vessel atherothromboembolism has a common pathogenesis, its topographic presentation is variable. Given the impact of cerebral infarct size and location on incident stroke magnitude and subsequent prognosis, we evaluated the determinants of cerebral infarct topography among patients with atherosclerotic stroke.
  
  
  METHODS
  We analyzed data on 148 consecutive patients admitted over a 4-year period to a university medical center with acute ischemic stroke within the MCA distribution on DWI, presumed due to atherosclerosis. Based on the DWI data, we divided the patients into three stroke phenotypes: large cortical, small (< 1 cm in diameter) cortical, and deep pattern. Independent factors for each stroke phenotype were evaluated using logistic regression.
  
  
  RESULTS
  After adjusting for covariates, premorbid statin use (OR, 3.05; 95% CI, 1.40-6.65) and older age (OR, 1.05 per 1 year increase; 95% CI, 1.02-1.08) were independently associated with the small cortical phenotypic pattern. In contrast, younger age (OR, 0.95 per 1 year increase; 95% CI, 0.92-0.98), premorbid statin non-use (OR, 0.40; 95% CI, 0.17-0.99), and higher levels of fasting s-glucose (OR, 1.01 per 1 mg/dl increase; 95% CI, 1.00-1.02) and admission peripheral WBC counts (OR, 1.13 per 1 x 10(9) cells/L; 95% CI, 1.00-1.27) were independently associated with the large cortical pattern. There was no relation between DWI patterns and LDL-cholesterol levels.
  
  
  CONCLUSIONS
  Age, premorbid statin use, s-glucose and WBC count predict atherosclerotic stroke phenotype. Further studies should examine whether modifying some of these factors may result in more favorable phenotypic patterns.
authors.affiliation: Dept. of Neurology, Samsung Medical Center, Seoul, South Korea.
authors.forename: Oh Young
authors.initials: OY
authors.lastname: Bang
authors.forename: Bruce
authors.initials: B
authors.lastname: Ovbiagele
authors.forename: David S
authors.initials: DS
authors.lastname: Liebeskind
authors.forename: Lucas
authors.initials: L
authors.lastname: Restrepo
authors.forename: Sa Rah
authors.initials: SR
authors.lastname: Yoon
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Saver
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001921:Brain / Q000473:pathology; D020520:Brain Infarction / Q000175:diagnosis* / Q000473:pathology / Q000523:psychology; D008078:Cholesterol, LDL / Q000097:blood; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D005947:Glucose / Q000378:metabolism; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use; D020244:Infarction, Middle Cerebral Artery / Q000175:diagnosis / Q000473:pathology / Q000523:psychology; D007958:Leukocyte Count; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D010641:Phenotype; D012307:Risk Factors; D020521:Stroke / Q000175:diagnosis* / Q000473:pathology / Q000523:psychology
chemical_list: D008078:Cholesterol, LDL; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D005947:Glucose