ST-T改变在鉴别窄QRS波心动过速中的价值

Values of ST-T Changes in Differentiating Narrow QRS Complex Tachycardia

为评价ST T改变在鉴别窄QRS波心动过速中的价值 ,用SPSS分析 12 4例窄QRS波心动过速患者体表 12导联心电图的ST T改变 ,其中房室折返性心动过速 (AVRT) 72例 ,房室结折返性心动过速 (AVNRT) 5 2例。结果 :AVRT组Ⅰ aVL导联ST段压低幅度 (0 .10± 0 .0 7mV)大于AVNRT组 (0 .0 6± 0 .0 6mV) ,P =0 .0 0 2 ;AVRT组V1导联T波幅度 (0 .14± 0 .19mV)大于AVNRT组 (0 .0 1± 0 .13mV) ,P <0 .0 0 1。鉴别窄QRS波心动过速的预测指标为QRS波终末改变 (R2 =0 .6 0 4 ,P <0 .0 0 1)、V1导联T波方向 (R2 =0 .2 4 9,P <0 .0 0 1)、V1导联T波幅度 (R2 =0 .180 ,P <0 .0 0 1)、Ⅰ aVL导联ST段压低 (R2 =0 .0 4 3,P <0 .0 0 1)。QRS波终末改变阳性预测AVNRT的特异性 98.6 % ,敏感性 75 .0 % ;V1导联T波正向预测AVRT的特异性 6 7.3% ,敏感性 81.9%。结论 :ST T改变有助于鉴别窄QRS波心动过速。QRS波终末改变是鉴别窄QRS波心动过速的较强预测指标 ;V1导联T波方向与幅度、Ⅰ aVL导联ST段压低是鉴别窄QRS波心动过速的较弱预测指标。

To evaluate the values of ST-T changes in differentiating narrow QRS complex tachycardia, SPSS was used to analyze the ST-T changes in 12-lead electrocardiograms of 124 narrow QRS complex tachycardias, of them 72 atrioventricular reentrant tachycardias(AVRT) and 52 atrioventricular node reentrant tachycardias(AVNRT). Results: ST segment depress in lead Ⅰ-aVL in AVRT (0.10±0.07 mV ) was greater than in AVNRT ( 0.06±0.06 mV ) ( P= 0.002 ). The magnitude of T wave in lead V 1 in AVRT (0.14±0.19 mV ) was greater than in AVNRT ( 0.01±0.13 mV ) ( P<0.001 ) .The predictors in differentiating narrow QRS complex tachycardia were the changes at the end of QRS (R 2=0.604, P<0.001), the direction of T wave in lead V 1 (R 2=0.249, P<0.001), the magnitude of T wave in lead V 1 (R 2=0.180, P<0.001), and the ST segment depress in lead Ⅰ-aVL (R 2=0.043, P<0.001). The specificity of positive changes at the end of QRS predicting AVNRT was 98.6%, and the sensitivity was 75.0%. The specificity of positive T wave in lead V 1 predicting AVRT was 67.3%, and the sensitivity was 81.9%. Conclusions: ST-T changes are helpful in differentiating narrow QRS complex tachycardia. The changes at the end of QRS complex is a strong predictor; while the direction and the magnitude of T-wave in lead V 1, and the ST segment depression in lead Ⅰ-aVL are the weak predictors.