| 宋光,乔伟,任卫东,张晶,毕文静.早期心尖肥厚型心肌病超声特征的Logistic回归分析[J].中国医学影像技术,2015,31(8):1212~1215 |
| 早期心尖肥厚型心肌病超声特征的Logistic回归分析 |
| Echocardiographic features of pre-apical hypertrophic cardiomyopathy: A Logistic regression analysis |
| 投稿时间：2014-11-28 修订日期：2015-07-01 |
| DOI：10.13929/j.1003-3289.2015.08.024 |
| 中文关键词: 超声心动描记术 心肌病,肥厚性 Logistic回归分析 |
| 英文关键词:Echocardiography Cardiomyopathy, hypertrophic Logistic regression analysis |
| 基金项目:辽宁省科技厅科学技术计划项目(2013225049)。 |
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| 中文摘要: |
| 目的 探讨超声诊断早期心尖肥厚型心肌病(PAHCM)的特征性指标。方法 对69例疑似PAHCM患者行超声心动图检查,于相应切面测量舒张末期左心室心尖部心肌和左心室后壁心肌的厚度,并计算二者比值。同时观察并记录心尖部心肌回声,心尖部心腔面积变化及心尖部心肌运动情况。采用Logistic回归分析模型分析PAHCM超声特征,并以ROC曲线评价Logistic模型的预报能力。结果 左心室心尖部与后壁心肌厚度比值(X1)、左心室心尖部心肌回声(X2)、左心室心尖部心腔面积(X3)为PAHCM的特征性超声指标。Logistic模型:Logit(P)=-3.022+4.474X1+2.830X2+3.665X3。此模型预报正确率为94.20%(65/69),ROC曲线下面积为0.979±0.015。结论 左心室心尖部与后壁心肌厚度比值、左心室心尖部心肌回声、左心室心尖部心腔面积可以较精确地预测PAHCM。 |
| 英文摘要: |
| Objective To investigate the characteristics of pre-apical hypertrophic cardiomyopathy (PAHCM) by echocardiography. Methods A total of 69 suspected PAHCM patients underwent echocardiography. Left ventricular apical and posterior wall of left ventricular myocardial thickness were measured in the correspondent sections, then the ratio of them were calculated. The apical myocardial echo, apical cavity area change and apical myocardial motion were observed and recorded. The echocardiographic features of PAHCM were analysed using Logistic regression analysis, the prediction of Logistic regression model was evaluated by generating ROC curve. Results The ratio between left ventricular apical and posterior wall myocardial thickness (X1), apical myocardial echo (X2) and apical cavity area (X3) were the three echocardiographic features of PAHCM. Logistic regression model: Logit(P)=-3.022+4.474X1+2.830X2+3.665X3. The correct rate of the model prediction was 94.20% (65/69). The area under ROC curve was 0.979±0.015. Conclusion The ratio between left ventricular apical and posterior wall myocardial thickness, apical myocardial echo and apical cavity area can accurately predict PAHCM. |
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