A computerized algorithm for etiologic classification of ischemic stroke: the Causative Classification of Stroke System.
OBJECTIVE
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.
Ay
Hakan
H
AA Martinos Center for Biomedical Imaging and Stroke Service, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Room 2301, Charlestown, MA 02129, USA. hay@partners.org
Benner
Thomas
T
Arsava
E Murat
EM
Furie
Karen L
KL
Singhal
Aneesh B
AB
Jensen
Matt B
MB
Ayata
Cenk
C
Towfighi
Amytis
A
Smith
Eric E
EE
Chong
Ji Y
JY
Koroshetz
Walter J
WJ
Sorensen
A Gregory
AG
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002545:Brain Ischemia / Q000145:classification* / Q000175:diagnosis / Q000209:etiology*; D002318:Cardiovascular Diseases / Q000150:complications; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020521:Stroke / Q000145:classification* / Q000175:diagnosis / Q000209:etiology*; D011795:Surveys and Questionnaires / Q000592:standards