A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA.
Neurology. 2010 Oct 5;75(14):1277-84. doi: 10.1212/WNL.0b013e3181f612ce.
Valid and reliable ischemic stroke subtype determination is crucial for well-powered multicenter studies. The Causative Classification of Stroke System (CCS, available at http://ccs.mgh.harvard.edu) is a computerized, evidence-based algorithm that provides both causative and phenotypic stroke subtypes in a rule-based manner. We determined whether CCS demonstrates high interrater reliability in order to be useful for international multicenter studies.
Twenty members of the International Stroke Genetics Consortium from 13 centers in 8 countries, who were not involved in the design and development of the CCS, independently assessed the same 50 consecutive patients with acute ischemic stroke through reviews of abstracted case summaries. Agreement among ratings was measured by kappa statistic.
The κ value for causative classification was 0.80 (95% confidence interval [CI] 0.78-0.81) for the 5-subtype, 0.79 (95% CI 0.77-0.80) for the 8-subtype, and 0.70 (95% CI 0.69-0.71) for the 16-subtype CCS. Correction of a software-related factor that generated ambiguity improved agreement: κ = 0.81 (95% CI 0.79-0.82) for the 5-subtype, 0.79 (95% CI 0.77-0.80) for the 8-subtype, and 0.79 (95% CI 0.78-0.80) for the 16-subtype CCS. The κ value for phenotypic classification was 0.79 (95% CI 0.77-0.82) for supra-aortic large artery atherosclerosis, 0.95 (95% CI 0.93-0.98) for cardioembolism, 0.88 (95% CI 0.85-0.91) for small artery occlusion, and 0.79 (0.76-0.82) for other uncommon causes.
CCS allows classification of stroke subtypes by multiple investigators with high reliability, supporting its potential for improving stroke classification in multicenter studies and ensuring accurate means of communication among different researchers, institutions, and eras.
有效的、可靠的缺血性脑卒中亚型确定对于强有力的多中心研究至关重要。卒中病因学分类系统(CCS,可在 http://ccs.mgh.harvard.edu 获得)是一种基于规则的、计算机化的、基于证据的算法,可提供病因学和表型卒中亚型。我们确定 CCS 是否具有高的组内可靠性,以便可用于国际多中心研究。
来自 8 个国家 13 个中心的 20 位国际卒中遗传学联合会成员,他们没有参与 CCS 的设计和开发,通过审查摘要病例总结,对 50 例连续急性缺血性卒中患者进行了独立评估。采用κ统计量衡量评分者间的一致性。
对于 5 型、8 型和 16 型 CCS,病因分类的κ 值分别为 0.80(95%置信区间 [CI] 0.78-0.81)、0.79(95% CI 0.77-0.80)和 0.70(95% CI 0.69-0.71)。纠正了一个产生歧义的软件相关因素后,一致性得到了提高:5 型、8 型和 16 型 CCS 的κ值分别为 0.81(95% CI 0.79-0.82)、0.79(95% CI 0.77-0.80)和 0.79(95% CI 0.78-0.80)。大动脉粥样硬化性狭窄、心源性栓塞、小动脉闭塞和其他少见病因的表型分类的κ 值分别为 0.79(95% CI 0.77-0.82)、0.95(95% CI 0.93-0.98)、0.88(95% CI 0.85-0.91)和 0.79(0.76-0.82)。
CCS 允许多位研究者进行卒中亚型分类,具有较高的可靠性,支持其在多中心研究中改善卒中分类的潜力,并确保不同研究人员、机构和时代之间准确的沟通手段。