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.
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.
Authors: E M Arsava; E Ballabio; T Benner; J W Cole; M P Delgado-Martinez; M Dichgans; F Fazekas; K L Furie; K Illoh; K Jood; S Kittner; A G Lindgren; J J Majersik; M J Macleod; W J Meurer; J Montaner; A A Olugbodi; A Pasdar; P Redfors; R Schmidt; P Sharma; A B Singhal; A G Sorensen; C Sudlow; V Thijs; B B Worrall; J Rosand; H Ay Journal: Neurology Date: 2010-10-05 Impact factor: 9.910
Authors: James F Meschia; Donna K Arnett; Hakan Ay; Robert D Brown; Oscar R Benavente; John W Cole; Paul I W de Bakker; Martin Dichgans; Kimberly F Doheny; Myriam Fornage; Raji P Grewal; Katrina Gwinn; Christina Jern; Jordi Jimenez Conde; Julie A Johnson; Katarina Jood; Cathy C Laurie; Jin-Moo Lee; Arne Lindgren; Hugh S Markus; Patrick F McArdle; Leslie A McClure; Braxton D Mitchell; Reinhold Schmidt; Kathryn M Rexrode; Stephen S Rich; Jonathan Rosand; Peter M Rothwell; Tatjana Rundek; Ralph L Sacco; Pankaj Sharma; Alan R Shuldiner; Agnieszka Slowik; Sylvia Wassertheil-Smoller; Cathie Sudlow; Vincent N S Thijs; Daniel Woo; Bradford B Worrall; Ona Wu; Steven J Kittner Journal: Stroke Date: 2013-09-10 Impact factor: 7.914
Authors: Matthew B Maas; Michael H Lev; Hakan Ay; Aneesh B Singhal; David M Greer; Wade S Smith; Gordon J Harris; Elkan F Halpern; Walter J Koroshetz; Karen L Furie Journal: J Stroke Cerebrovasc Dis Date: 2010-12-24 Impact factor: 2.136
Authors: Glen C Jickling; Huichun Xu; Boryana Stamova; Bradley P Ander; Xinhua Zhan; Yingfang Tian; Dazhi Liu; Renée J Turner; Matthew Mesias; Piero Verro; Jane Khoury; Edward C Jauch; Arthur Pancioli; Joseph P Broderick; Frank R Sharp Journal: Ann Neurol Date: 2010-11 Impact factor: 10.422
Authors: H Ay; L Gungor; E M Arsava; J Rosand; M Vangel; T Benner; L H Schwamm; K L Furie; W J Koroshetz; A G Sorensen Journal: Neurology Date: 2009-12-16 Impact factor: 9.910