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建立基于临床和磁共振表观弥散系数的急性前循环缺血性卒中预后评估系统
引用本文:马丽,高培毅,胡庆茂,林燕,薛静,荆利娜,王效春,陈志军,王伊龙,廖晓凌,刘梅丽,刘萍,陈伟健,蔡业峰,招远祺.建立基于临床和磁共振表观弥散系数的急性前循环缺血性卒中预后评估系统[J].中国卒中杂志,2011,6(3):192-200.
作者姓名:马丽  高培毅  胡庆茂  林燕  薛静  荆利娜  王效春  陈志军  王伊龙  廖晓凌  刘梅丽  刘萍  陈伟健  蔡业峰  招远祺
作者单位:北京市首都医科大学附属北京天坛医院放射科2中国科学院香港中文大学深圳先进集成技术研究所,中国科学院深圳先进技术研究院,中国科学院医学信息与健康工程学重点实验室3山西医科大学第一医院放射科4首都医科大学附属北京天坛医院神经内科5天津环湖医院放射科6天津环湖医院神经内科7温州医学院附属第一医院放射科8广东省中医院神经一科
基金项目:" 十一五" 国家科技支撑计划重点项目
摘    要:目的 探讨影响急性缺血性卒中预后的因素,建立一种基于临床和多模式磁共振成像(magneticresonance imaging,MRI)的急性前循环缺血性卒中预后评估系统。方法 选择发病9小时内完成多模式MRI的前循环急性缺血性卒中患者40例。按照改良的Ranking量表(modified Ranking Scale,mRS)分为预后良好组(0~1分)和预后不良组(2~6分)。评价两组年龄、基线美国国立卫生研究院卒中量表评分(national institutes of health stroke scale,NIHSS)、基线弥散加权像(diffusion-weighted imaging,DWI)体积、基线灌注加权像(perfusion-weighted imaging,PWI)体积以及由基于表观弥散系数(apparent diffusion coefficient,ADC)的图像分析方法获得的预测梗死核心体积、预测可挽救脑组织体积等临床/影像信息对预后的影响;采用多因素分析筛查出单因素分析中具有统计学意义的变量作为预后评估系统的组成部分,应用受试者工作特征曲线(receiver operatorcharacteristic curve,ROC)分析获得各变量的阈值评分,整合后获得临床/ADC评分,应用ROC曲线下面积(area under curve,AUC)分析各评分模式判断预后的效能。结果 预后良好组与预后不良组在年龄、基线NIHSS、预测梗死核心体积、预测可挽救脑组织体积、预测最终梗死体积、实际最终梗死体积和基线DWI异常区域体积的差异均具有统计学意义。多因素分析显示年龄、预测梗死核心体积、预测最终梗死体积和基线NI HSS能作为判断预后的风险因素,构成临床/ADC预后评分系统的四个因素。应用ROC分析获得以上四个变量判断预后不良的阈值分别为>58岁、>5.84 ml 、>10.6 ml 和>12分。该评分系统的AUC最大(AUC=0.878,P<0.01),其判断急性缺血性卒中患者90 d预后的效能最高,其次是实际最终梗死体积(AUC=0.802,P =0.001)、预测最终梗死体积(AUC=0.797,P =0.001)、预测梗死核心体积(AUC=0.739,P =0.01)、基线NIHSS(AUC=0.759,P =0.005)、预测可挽救脑组织体积(AUC=0.719,P =0.018)和基线DWI异常区域体积(AUC=0.693,P =0.037)。其中,临床/ADC预后评分系统与预测梗死核心体积、预测可挽救脑组织体积、基线DWI异常区域体积AUC之间的差异具有统计学意义(P分别为0.043,0.035和0.01)。结论 临床/ADC预后评分系统比基线NIHSS评分和各影像参数判断90 d急性缺血性卒中患者预后的效能高;制定急性缺血性卒中患者治疗方案时,应结合患者临床和影像信息综合考虑。

关 键 词:卒中  缺血性  血栓溶解疗法  磁共振成像  磁共振成像  弥散  
收稿时间:2010-09-01

Identifying Clinical and Radiologic Factors Influencing Functional Outcome in Acute Ischemic Stroke of the Anterior Circulation
MA Li,GAO Pei-Yi,HU Qing-Mao,LIN Yan,XUE Jing,XING Li-Na,WANG Xiao-Chun,CHEN Zhi-Jun,WANG Yi-Long,LIAO Xiao-Ling,LIU Li-Mei,LIU Ping,CHEN Wei-Jian,CAI Ye-Feng,ZHAO Yuan-Qi.Identifying Clinical and Radiologic Factors Influencing Functional Outcome in Acute Ischemic Stroke of the Anterior Circulation[J].Chinese Journal of Stroke,2011,6(3):192-200.
Authors:MA Li  GAO Pei-Yi  HU Qing-Mao  LIN Yan  XUE Jing  XING Li-Na  WANG Xiao-Chun  CHEN Zhi-Jun  WANG Yi-Long  LIAO Xiao-Ling  LIU Li-Mei  LIU Ping  CHEN Wei-Jian  CAI Ye-Feng  ZHAO Yuan-Qi
Institution:.(Radiology Department, Beoing Tiantan Hospital, Capital Medical University, Beijing 100050, China)
Abstract:Objective To identify clinical and imaging predictors of outcome and to develop a clinical/ apparent diffusion coefficient score that will enable better patient selection for thrombolytic therapy in acute ischemic stroke. Methods Baseline clinical and radiologic data variables (including predicted volumes obtained from apparent diffusion coefficient (ADC) based imaging analysis system) that considered possibly related to outcome were selected in 40 patients with acute ischemic stroke of the anterior circulation. A univariate analysis was conducted to explore the association between these factors and bad outcome, defined as a modified Rankin scale score_〉2. Logistic regression was then performed to select the most important variables independently affecting prognosis. Receiver operator characteristic curve (ROC) was then used to obtain cut-off points for each independent variable. A risk score (clinical/apparent diffusion coefficient score) was then developed based on these variables. The predictive value of the clinical/multimodal magnetic resonance imaging (MRI) score was compared with single clinical data with respect to the clinical outcome 3 months after stroke onset by use of modified Rankin Scale (mRS). Results In the univariate analysis, variables associated with poor outcome were: age, baseline National Institutes of Health Stroke Scale (NIHSS), predicted infarct core volume, predicted salvageable ischemic tissue volume, predicted final infarct volume, final infarct volume and admission diffusion-weighted imaging (DWI) lesion volume. Four variables independently associated with poor outcome were identified by Logistic regression: age, predicted infarct core volume, predicted final infarct volume and NIHSS. We then developed the clinical/apparent diffusion coefficient score: 1 point for age〉58 years, 1 for predicted infarct core volume〉5.84 ml, 1 for predicted final infarct volume〉10.6 ml and 1 for〉12 for NIHSS. The factor that most accurately predicted good clinical outcome 3 months after stroke was clinical/apparent diffusion coefficient score (area under the curve-0.878, P〈0.001), followed by final infarct volume (area under the curve=0.802, P=0.001), predicted final infarct volume (area under the curve=0.797, P-0.001), predicted infarct core volume (area under the curve=0.739, P=0.01), baseline NIHSS (area under the curve=0.759, P=0.005), predicted salvageable ischemic tissue volume (area under the curve=0.719, P=0.018) and baseline DWI lesion volume (area under the curve=0.693, P=0.037). Conclusion Clinical/apparent diffusion coefficient score based on clinical and radiologic data is superior to single clinical and ADC in estimating the chances of poor outcome.
Keywords:Stroke  ischemic  Thrombolytic therapy  Magnetic resonance imaging  Diffusionmagnetic resonance imaging
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