A computerized algorithm for etiologic classification of ischemic stroke - The causative classification of stroke system
A computerized algorithm for etiologic classification of ischemic stroke - The causative classification of stroke system
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DOI：
10.1161/strokeaha.107.490896
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发表时间：
2007-11-01
期刊：
影响因子：
8.3
通讯作者：
Sorensen, A. Gregory
中科院分区：
文献类型：
--
作者：
Ay, Hakan;Benner, Thomas;Sorensen, A. Gregory
背景和目的-SSS-TOAST是一种基于证据的急性缺血性卒中分类算法，旨在多种相互竞争的机制存在的情况下确定最可能的病因。在本文中，我们提出了一个自动化版本的SSS-TOAST，即因果分类系统(CCS)，以促进其在多中心设置中的应用。方法-CCS是一个基于网络的系统，由问卷式缺血性卒中分类方案(http://ccs.martinos.org).)组成通过指示临床和诊断评估结果的复选框提供数据输入。自动算法报告笔划子类型和分类原理的描述。结果：对于5项CCS(大动脉粥样硬化、心主动脉栓塞、小动脉闭塞、其他原因和不明原因)，检查者间一致性的Kappa值为0.86(95%CI，0.81到0.91)，0.85(95%CI，0.80到0.89)，未确定组分为隐源性栓塞组、其他隐源性组、不完全评估组和未分类组(8项CCS)和0.80(95%CI，0.80到0.89)。0.76到0.83)，用于16个项目的细分，其中诊断根据置信度水平进行分层。检验员内信度分别为0.90(0.75-1.00)、0.87(0.73-1.00)和0.86(0.75-0.97)。结论：基于网络的CCS能够快速分析患者数据，具有良好的检验员内和检验员间的可靠性，提示在多中心试验或研究数据库中提高卒中分类的保真度具有潜在的实用价值。
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.