宋宏宁,陈金玲,秦真英,曹省,胡波,郭瑞强.超声心动图多参数联合临床诊断冠状动脉粥样硬化性心脏病[J].中国医学影像技术,2014,30(6):863~866
超声心动图多参数联合临床诊断冠状动脉粥样硬化性心脏病
Combination of multi echocardiographic parameters and clinical data in diagnosis of coronary heart disease
投稿时间:2013-12-09  修订日期:2014-05-03
DOI:
中文关键词:  冠状动脉疾病  超声心动描记术  受试者工作特征曲线
英文关键词:Coronary disease  Echocardiography  Receiver operating curve
基金项目:湖北省卫生厅科研基金(JX6B16)。
作者单位E-mail
宋宏宁 武汉大学人民医院超声影像科, 湖北 武汉 430060  
陈金玲 武汉大学人民医院超声影像科, 湖北 武汉 430060  
秦真英 枣庄市立医院超声科, 山东 枣庄 277101  
曹省 武汉大学人民医院超声影像科, 湖北 武汉 430060  
胡波 武汉大学人民医院超声影像科, 湖北 武汉 430060  
郭瑞强 武汉大学人民医院超声影像科, 湖北 武汉 430060 ruiqiangwhrm@hotmail.com 
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中文摘要:
      目的 探讨临床资料与超声心动图多参数联合诊断冠状动脉粥样硬化性心脏病的价值。方法 收集105例以胸痛为首发症状的患者,最终纳入75例,根据CAG结果分为狭窄组(n=33)与对照组(n=42)。评估两组患者的临床资料,应用超声心动图测量二尖瓣口血流频谱E、A值、E峰减速时间(E-DT)、等容舒张时间(IVRT),DTI测量二尖瓣环舒张期运动速度e、a峰值;应用4D-Auto LVQ功能测量左心室射血分数(LVEF)及舒张末期球形指数(SpI);以二维斑点 自动功能成像(AFI)测量左心室整体纵向应变平均值(GLPSS)。结果 狭窄组与对照组间E/e、E-DT、GLPSS及二型糖尿病(2DM)患病率差异有统计学意义(P均<0.05),其预测冠状动脉狭窄的ROC曲线下面积依次为E/e(0.715)>E-DT(0.667)>GLPSS(0.649)>2DM(0.609),二元Logistic回归方程P=1/[1+e-(-5.505+0.431×E/e+0.021×E-DT+0.127×GLPSS+2.141×2DM)] 。 P值的ROC曲线下面积为0.824。以P≥0.4509为截值预测冠状动脉狭窄>50%的敏感度和特异度最高,分别为78.8%和69.0%。结论 联合应用临床及超声心动图多个参数能够显著提高诊断冠心病的效能。
英文摘要:
      Objective To explore the value of combination with clinical data and multi echocardiography parameters in diagnosis of coronary heart disease (CHD). Methods Totally 75 patients with chest pain were divided into two groups, i.e. coronary stenosis group (n=33) and control group (n=42) according to the result of CAG. Clinical data were analyzed and the echocardiographic parameters were acquired by DTI, including mitral flow E and A velocities, E peak deceleration time (E-DT), mitral annulus velocity e and a in diastole. The left ventricular ejection fraction (LVEF) and end-diastolic sphericity index (SpI) were measured with 4D-auto LVQ, and the global longitudinal peak systolic strain (GLPSS) was acquired with automated function imaging (AFI). Results The differences of E/e, E-DT, GLPSS and prevalence of type 2 diabetes (2DM) were significant between coronary stenosis group and control groups. The area under ROC curve (AUC) were successively as E/e (0.715)>E-DT (0.667)>GLPSS (0.649)>2DM (0.609). The binary Logistic regression equation was P=1/[1+e-(-5.505+0.431×E/e+0.021×E-DT+0.127×GLPSS+2.141×2DM)]. AUC of P was 0.824. Taking P≥0.4509 as the cut-off value for forecast of coronary stenosis more than 50%, the sensitivity and specificity was 78.8% and 69.0%, respectively. Conclusion Combining multi echocardiographic parameters with clinic data can significantly enhance the diagnostic capability of CHD.
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