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(Stroke. 2007;38:2979.)
© 2007 American Heart Association, Inc.

Original Contributions

A Computerized Algorithm for Etiologic Classification of Ischemic Stroke

The Causative Classification of Stroke System

Hakan Ay, MD; Thomas Benner, PhD; E. Murat Arsava, MD; Karen L. Furie, MD; Aneesh B. Singhal, MD; Matt B. Jensen, MD; Cenk Ayata, MD; Amytis Towfighi, MD; Eric E. Smith, MD; Ji Y. Chong, MD; Walter J. Koroshetz, MD A. Gregory Sorensen, MD

From Stroke Service, Department of Neurology, and AA Martinos Center for Biomedical Imaging (H.A), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass; AA Martinos Center for Biomedical Imaging (T.B., E.M.A., A.G.S.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Stroke Service (K.L.F., A.B.S., E.E.S.), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Stroke Center (M.B.J.), University of California-San Diego, San Diego, Calif; Stroke Service, Department of Neurology and Stroke and Neurovascular Regulation Laboratory (C.A.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Stroke Center (A.T.), Department of Neurology, UCLA Medical Center, Los Angeles, Calif; Division of Stroke and Critical Care (J.Y.C.), Department of Neurology, Columbia University, New York, NY; National Institute of Neurological Disorders and Stroke (W.J.K.), NIH, Bethesda, MD.

Correspondence to Hakan Ay, MD, AA Martinos Center for Biomedical Imaging and Stroke Service, Departments of Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Room 2301, Charlestown MA 02129. E-mail hay{at}partners.org

Background and Purpose— The SSS-TOAST is an evidence-based classification algorithm for acute ischemic stroke designed to determine the most likely etiology in the presence of multiple competing mechanisms. In this article, we present an automated version of the SSS-TOAST, the Causative Classification System (CCS), to facilitate its utility in multicenter settings.

Methods— The CCS is a web-based system that consists of questionnaire-style classification scheme for ischemic stroke (http://ccs.martinos.org). Data entry is provided via checkboxes indicating results of clinical and diagnostic evaluations. The automated algorithm reports the stroke subtype and a description of the classification rationale. We evaluated the reliability of the system via assessment of 50 consecutive patients with ischemic stroke by 5 neurologists from 4 academic stroke centers.

Results— The kappa value for inter-examiner agreement was 0.86 (95% CI, 0.81 to 0.91) for the 5-item CCS (large artery atherosclerosis, cardio-aortic embolism, small artery occlusion, other causes, and undetermined causes), 0.85 (95% CI, 0.80 to 0.89) with the undetermined group broken into cryptogenic embolism, other cryptogenic, incomplete evaluation, and unclassified groups (8-item CCS), and 0.80 (95% CI, 0.76 to 0.83) for a 16-item breakdown in which diagnoses were stratified by the level of confidence. The intra-examiner reliability was 0.90 (0.75–1.00) for 5-item, 0.87 (0.73–1.00) for 8-item, and 0.86 (0.75–0.97) for 16-item CCS subtypes.

Conclusions— The web-based CCS allows rapid analysis of patient data with excellent intra- and inter-examiner reliability, suggesting a potential utility in improving the fidelity of stroke classification in multicenter trials or research databases in which accurate subtyping is critical.

Key Words: classification cerebral infarct • etiology