Assessment of the Predictive Validity of Etiologic Stroke Classification | Cerebrovascular Disease | JAMA Neurology | JAMA Network
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Original Investigation
April 2017

Assessment of the Predictive Validity of Etiologic Stroke Classification

Author Affiliations
  • 1AA Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston
  • 2Department of Neurology, Hacettepe University, Ankara, Turkey
  • 3Department of Neurology, University of Massachusetts Medical School, Worcester
  • 4now with Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
  • 5Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania
  • 6Department of Neurology, Faculty of Medicine, Ankara University, Ankara, Turkey
  • 7Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
  • 8Ribeirão Preto School of Medicine, University of São Paulo, São Paulo, Brazil
  • 9Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
  • 10Stroke Service, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston
  • 11Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston
  • 12Biostatistics Center, Massachusetts General Hospital, Harvard Medical School, Boston
JAMA Neurol. 2017;74(4):419-426. doi:10.1001/jamaneurol.2016.5815
Key Points

Question  Can etiologic stroke subtyping generate categories with discrete clinical, imaging, and prognostic characteristics?

Findings  A head-to-head, blind evaluation of Causative Classification of Stroke, Trial of Org 10172 in Acute Stroke Treatment, and ASCO (A for atherosclerosis, S for small-vessel disease, C for cardiac source, and O for other cause) classification systems in 1816 consecutive patients with ischemic stroke revealed that all systems generated etiologic subtypes with different 90-day stroke recurrence, 90-day survival, admission stroke severity, and acute infarct burden. The Causative Classification of Stroke system redistributed 20% to 40% of the population assigned into the undetermined category by other systems into known subtypes and provided a greater discrimination for most of the stroke characteristics tested as compared with the Trial of Org 10172 in Acute Stroke Treatment and ASCO systems.

Meaning  Etiologic stroke classification identifies discrete categories with different stroke features.


Importance  The ability of present-day etiologic stroke classification systems to generate subtypes with discrete stroke characteristics is not known.

Objective  To test the hypothesis that etiologic stroke subtyping identifies different disease processes that can be recognized through their different clinical courses.

Design, Setting, and Participants  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.

Main Outcomes and Measures  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).

Results  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.

Conclusions and Relevance  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.