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首页> 外文期刊>Heart rhythm: the official journal of the Heart Rhythm Society >A quantitative assessment of T-wave morphology in LQT1, LQT2, and healthy individuals based on Holter recording technology.
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A quantitative assessment of T-wave morphology in LQT1, LQT2, and healthy individuals based on Holter recording technology.

机译:基于动态心电图记录技术对LQT1,LQT2和健康个体的T波形态进行定量评估。

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摘要

BACKGROUND: The clinical course and the precipitating risk factors in the congenital long QT syndrome (LQTS) are genotype specific. OBJECTIVES: The goal of this study was to develop a computer algorithm allowing for electrocardiogram (ECG)-based identification and differentiation of LQT1 and LQT2 carriers. METHODS: 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. RESULTS: 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). CONCLUSION: 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.
机译:背景:先天性长QT综合征(LQTS)的临床过程和诱发危险因素是基因型特异性的。目的:本研究的目的是开发一种计算机算法,允许基于心电图(ECG)的LQT1和LQT2携带者识别和区分。方法:在49例LQT1携带者,25例LQT2携带者和38例健康受试者中获得十二导联心电图动态心电图监测记录。心跳基于心率bin方法进行聚类。比较了研究组中相似心率的标量和矢量复极参数。使用定制算法自动定量Q到峰(QTpeak),Tpeak到Tend间隔,T波幅值和T环形态。结果:使用预先指定的心率区(75.0至77.5次/分钟),从II导联的QTpeak和T波的右斜率是区分3组的最有区别的参数。使用这些标量参数的预测模型已使用整个心率谱进行了验证。标量和矢量心电图模型都可以非常有效地识别被测对象,其心率在60至100次/分钟之间,而它们在心动过速期间的表现有限,在心动过缓中的辨别力稍好。在60至100次/分钟的心率范围内,最佳2变量模型可正确识别89%的健康受试者,84%的LQT1携带者和92%的LQT2携带者。仅包含标量ECG参数的包括3个参数的模型可以正确识别90%的人口(89%的健康受试者,90%的LQT1携带者和92%的LQT2携带者)。结论:量化T波形态的自动算法可以准确区分LQT1和LQT2携带者以及健康受试者。这种计算机化的心电图方法学可以帮助医生评估疑似LQTS的受试者。

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