A computerized algorithm for etiologic classification of ischemic stroke: the Causative Classification of Stroke System. Export

@article{citeulike:4908558, abstract = {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.}, author = {Ay, H. and Benner, T. and Arsava, E. M. and Furie, K. L. and Singhal, A. B. and Jensen, M. B. and Ayata, C. and Towfighi, A. and Smith, E. E. and Chong, J. Y. and Koroshetz, W. J. and Sorensen, A. G.}, citeulike-article-id = {4908558}, citeulike-linkout-0 = {http://dx.doi.org/10.1161/STROKEAHA.107.490896}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17901381}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17901381}, doi = {10.1161/STROKEAHA.107.490896}, issn = {1524-4628}, journal = {Stroke; a journal of cerebral circulation}, keywords = {acute, automated, classification, ischemia, stroke}, month = {November}, number = {11}, pages = {2979--2984}, posted-at = {2009-06-19 15:58:54}, priority = {2}, title = {A computerized algorithm for etiologic classification of ischemic stroke: the Causative Classification of Stroke System.}, url = {http://dx.doi.org/10.1161/STROKEAHA.107.490896}, volume = {38}, year = {2007} }

TY - JOUR ID - citeulike:4908558 L3 - citeulike-article-id:4908558 N2 - 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. IS - 11 JF - Stroke; a journal of cerebral circulation SN - 1524-4628 EP - 2984 TI - A computerized algorithm for etiologic classification of ischemic stroke: the Causative Classification of Stroke System. VL - 38 SP - 2979 KW - acute KW - automated KW - classification KW - ischemia KW - stroke AU - Ay, H AU - Benner, T AU - Arsava, EM AU - Furie, KL AU - Singhal, AB AU - Jensen, MB AU - Ayata, C AU - Towfighi, A AU - Smith, EE AU - Chong, JY AU - Koroshetz, WJ AU - Sorensen, AG PY - 2007/11// UR - http://dx.doi.org/10.1161/STROKEAHA.107.490896 ER -