title: Assessment of the Predictive Validity of Etiologic Stroke Classification.
abstract: The ability of present-day etiologic stroke classification systems to generate subtypes with discrete stroke characteristics is not known.
  
  
  
  To test the hypothesis that etiologic stroke subtyping identifies different disease processes that can be recognized through their different clinical courses.
  
  
  
  We performed a head-to-head evaluation of the ability of the Causative Classification of Stroke (CCS), Trial of Org 10172 in Acute Stroke Treatment (TOAST), and ASCO (A for atherosclerosis, S for small-vessel disease, C for cardiac source, and O for other cause) classification systems to generate etiologic subtypes with different clinical, imaging, and prognostic characteristics in 1816 patients with ischemic stroke. This study included 2 cohorts recruited at separate periods; the first cohort was recruited between April 2003 and June 2006 and the second between June 2009 and December 2011. Data analysis was performed between June 2014 and May 2016.
  
  
  
  Separate teams of stroke-trained neurologists performed CCS, TOAST, and ASCO classifications based on information available at the time of hospital discharge. We assessed the association between etiologic subtypes and stroke characteristics by computing receiver operating characteristic curves for binary variables (90-day stroke recurrence and 90-day mortality) and by calculating the ratio of between-category to within-category variability from the analysis of variance for continuous variables (admission National Institutes of Health Stroke Scale score and acute infarct volume).
  
  
  
  Among the 1816 patients included, the median age was 70 years (interquartile range, 58-80 years) (830 women [46%]). The classification systems differed in their ability to assign stroke etiologies into known subtypes; the size of the undetermined category was 33% by CCS, 53% by TOAST, and 42% by ASCO (P < .001 for all binary comparisons). All systems provided significant discrimination for the validation variables tested. For the primary validation variable (90-day recurrence), the area under the receiver operating characteristic curve was 0.71 (95% CI, 0.66-0.75) for CCS, 0.61 (95% CI, 0.56-0.67) for TOAST, and 0.66 (95% CI, 0.60-0.71) for ASCO (P = .01 for CCS vs ASCO; P < .001 for CCS vs TOAST; P = .13 for ASCO vs TOAST). The classification systems exhibited similar discrimination for 90-day mortality. For admission National Institutes of Health Stroke Scale score and acute infarct volume, CCS generated more distinct subtypes with higher between-category to within-category variability than TOAST and ASCO.
  
  
  
  Our findings suggest that the major etiologic stroke subtypes are distinct categories with different stroke characteristics irrespective of the classification system used to identify them. We further show that CCS generates discrete etiologic categories with more diverse clinical, imaging, and prognostic characteristics than either TOAST or ASCO.
authors.affiliation: AA Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston2Department of Neurology, Hacettepe University, Ankara, Turkey.
authors.forename: E Murat
authors.initials: EM
authors.lastname: Arsava
authors.affiliation: AA Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston3Department of Neurology, University of Massachusetts Medical School, Worcester4now with Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
authors.forename: Johanna
authors.initials: J
authors.lastname: Helenius
authors.affiliation: AA Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston5Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania.
authors.forename: Ross
authors.initials: R
authors.lastname: Avery
authors.affiliation: AA Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston6Department of Neurology, Faculty of Medicine, Ankara University, Ankara, Turkey.
authors.forename: Mine H
authors.initials: MH
authors.lastname: Sorgun
authors.affiliation: AA Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston7Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
authors.forename: Gyeong-Moon
authors.initials: GM
authors.lastname: Kim
authors.affiliation: AA Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston8Ribeirão Preto School of Medicine, University of São Paulo, São Paulo, Brazil.
authors.forename: Octavio M
authors.initials: OM
authors.lastname: Pontes-Neto
authors.affiliation: AA Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston9Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea.
authors.forename: Kwang Yeol
authors.initials: KY
authors.lastname: Park
authors.affiliation: Stroke Service, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston11Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Rosand
authors.affiliation: Biostatistics Center, Massachusetts General Hospital, Harvard Medical School, Boston.
authors.forename: Mark
authors.initials: M
authors.lastname: Vangel
authors.affiliation: AA Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston10Stroke Service, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.
authors.forename: Hakan
authors.initials: H
authors.lastname: Ay
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D050197:Atherosclerosis / Q000150:complications*; D002545:Brain Ischemia / Q000150:complications*; D002539:Cerebral Arterial Diseases / Q000150:complications*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests; D012044:Regression Analysis; D015203:Reproducibility of Results; D012307:Risk Factors; D018709:Statistics, Nonparametric; D020521:Stroke / Q000145:classification / Q000175:diagnosis* / Q000209:etiology*