Hubley-Kozey C L, Mitchell L B, Gardner M J, Warren J W, Penney C J, Smith E R, Horácek B M
Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada.
Circulation. 1995 Oct 1;92(7):1825-38. doi: 10.1161/01.cir.92.7.1825.
Regional disparities of ventricular primary-repolarization properties contribute to an electrophysiological substrate for arrhythmias. Such disparities can be assessed from body-surface distributions of ECG QRST areas. Our objective was to isolate and test those features of QRST-area distributions that would be suitable for identifying patients at risk for life-threatening ventricular arrhythmias.
We recorded ECGs simultaneously from 120 leads during sinus rhythm for 204 patients taking no antiarrhythmic drugs: half had had sustained ventricular tachycardia (VT); the other half, a myocardial infarction but no history of VT. For each patient, we calculated the QRST area in each lead and, using Karhunen-Loeve (K-L) expansion, reduced these data to 16 coefficients (each relating to one spatial feature, an eigenvector, derived from the total set of 204 QRST-area maps). Using stepwise discriminant analysis, we selected feature subsets that best discriminated between the two groups, and we estimated by a bootstrap procedure using 1000 trials how these subsets would perform on a prospective patient population. The mean diagnostic performance of the classifier for 1000 randomly selected training sets (n = 102 in each, with both groups equally represented) increased monotonically with the number of features used for classification. The initial trend for the corresponding test sets (n = 102 in each) was the same but reversed when the number of features exceeded eight. For an optimal set of eight spatial features, the sensitivity and specificity of the classifier for detecting patients with VT in 1000 test sets were (mean +/- SD) 90.3 +/- 4.3% and 78.0 +/- 6.1%, and its positive and negative predictive accuracies were 80.7 +/- 4.2% and 89.2 +/- 4.2%, respectively. Use of QRS duration as a supplementary feature to eight K-L coefficients can, in the test sets, increase specificity to 80.9 +/- 5.4% and positive predictive accuracy to 82.8 +/- 3.9% compared with the results for the optimal number of eight K-L features alone.
Multiple body-surface ECGs contain valuable spatial features that can identify the presence of an arrhythmogenic substrate in the myocardium of patients at risk for ventricular arrhythmias. Our results compare very favorably with those achieved by any other known test, invasive or noninvasive, for arrhythmogenicity.
心室原发性复极特性的区域差异为心律失常提供了电生理基础。这种差异可通过心电图QRST面积的体表分布来评估。我们的目的是分离并测试QRST面积分布的那些特征，以识别有发生危及生命的室性心律失常风险的患者。
我们在窦性心律期间，同时记录了204例未服用抗心律失常药物患者的120导联心电图：其中一半有持续性室性心动过速（VT）；另一半有心肌梗死但无VT病史。对于每位患者，我们计算了每个导联的QRST面积，并使用卡尔胡宁 - 洛伊夫（K - L）展开，将这些数据简化为16个系数（每个系数与一个空间特征相关，该特征是从204张QRST面积图的总体中得出的特征向量）。使用逐步判别分析，我们选择了能最佳区分两组的特征子集，并通过1000次试验的自助法程序估计这些子集在未来患者群体中的表现。对于1000个随机选择的训练集（每组n = 102，两组均衡代表），分类器的平均诊断性能随着用于分类的特征数量单调增加。相应测试集（每组n = 102）的初始趋势相同，但当特征数量超过8个时趋势反转。对于一组最佳的8个空间特征，在1000个测试集中，分类器检测VT患者的敏感性和特异性分别为（均值±标准差）90.3±4.3%和78.0±6.1%，其阳性和阴性预测准确率分别为80.7±4.2%和89.2±4.2%。与仅使用最佳数量的8个K - L特征的结果相比，在测试集中，将QRS时限作为8个K - L系数的补充特征可使特异性提高到80.9±5.4%，阳性预测准确率提高到82.8±3.9%。
多个体表心电图包含有价值的空间特征，可识别有室性心律失常风险患者心肌中致心律失常基质的存在。我们的结果与任何其他已知的用于检测致心律失常性的有创或无创测试结果相比非常有利。
