A computerized algorithm for etiologic classification of ischemic stroke: the Causative Classification of Stroke System.
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 Classificati...
| Publicado en: | Stroke (00392499) Vol. 38; no. 11; pp. 2979 - 2985 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Nov2007
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105831536&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105831536 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00392499 1FT jtl: Stroke (00392499) issn: 00392499 maglogo: N pubinfo: dt: Nov2007 vid: 38 iid: 11 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 105831536 105831536 NLM17901381 2009708408 NLM17901381 105831536 ppf: 2979 ppct: 6 formats: tig: atl: A computerized algorithm for etiologic classification of ischemic stroke: the Causative Classification of Stroke System. aug: au: Ay H Benner T Arsava EM Furie KL Singhal AB Jensen MB Ayata C Towfighi A Smith EE Chong JY Koroshetz WJ Sorensen AG Ay, Hakan Benner, Thomas Arsava, E Murat Furie, Karen L Singhal, Aneesh B Jensen, Matt B Ayata, Cenk Towfighi, Amytis affil: 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 sug: subj: Algorithms Cerebral Ischemia Classification Cerebral Ischemia Etiology Diagnosis, Computer Assisted Methods Stroke Classification Stroke Etiology Adult Aged Aged, 80 and Over Cardiovascular Diseases Complications Cerebral Ischemia Diagnosis Diagnosis, Differential Female Male Middle Age Observer Bias Predictive Value of Tests Questionnaires Standards Reproducibility of Results Sensitivity and Specificity Stroke Diagnosis Human Adult: 19-44 years Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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