Vaglio Martino, Couderc Jean-Philippe, McNitt Scott, Xia Xiaojuan, Moss Arthur J, Zareba Wojciech
Heart Research Follow-Up Program, University of Rochester Medical Center, Rochester, New York, USA.
Heart Rhythm. 2008 Jan;5(1):11-8. doi: 10.1016/j.hrthm.2007.08.026. Epub 2007 Aug 28.
The clinical course and the precipitating risk factors in the congenital long QT syndrome (LQTS) are genotype specific.
The goal of this study was to develop a computer algorithm allowing for electrocardiogram (ECG)-based identification and differentiation of LQT1 and LQT2 carriers.
Twelve-lead ECG Holter monitor recordings were acquired in 49 LQT1 carriers, 25 LQT2 carriers, and 38 healthy subjects as controls. The cardiac beats were clustered based on heart-rate bin method. Scalar and vectorial repolarization parameters were compared for similar heart rates among study groups. The Q to Tpeak (QTpeak), the Tpeak to Tend interval, T-wave magnitude and T-loop morphology were automatically quantified using custom-made algorithms.
QTpeak from lead II and the right slope of the T-wave were the most discriminant parameters for differentiating the 3 groups using prespecified heart rate bin (75.0 to 77.5 beats/min). The predictive model utilizing these scalar parameters was validated using the entire spectrum of heart rates. Both scalar and vectorcardiographic models provided very effective identification of tested subjects in heart rates between 60 and 100 beats/min, whereas they had limited performance during tachycardia and slightly better discrimination in bradycardia. In the 60 to 100 beats/min heart rate range, the best 2-variable model identified correctly 89% of healthy subjects, 84% of LQT1 carriers, and 92% of LQT2 carriers. A model including 3 parameters based purely on scalar ECG parameters could correctly identify 90% of the population (89% of healthy subjects, 90% of LQT1 carriers, and 92% of LQT2 carriers).
Automatic algorithm quantifying T-wave morphology discriminates LQT1 and LQT2 carriers and healthy subjects with high accuracy. Such computerized ECG methodology could assist physicians evaluating subjects suspected for LQTS.
先天性长QT综合征(LQTS)的临床病程和诱发风险因素具有基因型特异性。
本研究的目的是开发一种计算机算法,用于基于心电图(ECG)识别和区分LQT1和LQT2携带者。
采集了49例LQT1携带者、25例LQT2携带者和38例健康受试者作为对照的12导联心电图动态监测记录。根据心率区间法对心搏进行聚类。比较了研究组中相似心率下的标量和矢量复极参数。使用定制算法自动量化Q至T峰(QTpeak)、T峰至T终末间期、T波幅度和T环形态。
使用预先指定的心率区间(75.0至77.5次/分钟),II导联的QTpeak和T波的右斜率是区分这三组的最具鉴别力的参数。利用这些标量参数的预测模型在整个心率范围内得到了验证。标量和矢量心电图模型在60至100次/分钟的心率范围内都能非常有效地识别受试对象,而在心动过速时性能有限,在心动过缓时鉴别能力稍好。在60至100次/分钟的心率范围内,最佳的双变量模型正确识别了89%的健康受试者、84%的LQT1携带者和92%的LQT2携带者。一个仅基于标量心电图参数的包含3个参数的模型可以正确识别90%的人群(8