Pytkowski Mariusz, Maciąg Aleksander, Sterliński Maciej, Jankowska Agnieszka, Kowalik Ilona, Farkowski Michał M, Kuteszko Rafał, Zając Dariusz, Firek Bohdan, Chmielak Zbigniew, Szwed Hanna
2nd Department of Coronary Artery Disease, Institute of Car diology, Warsaw, Poland.
Cardiol J. 2014;21(3):284-92. doi: 10.5603/CJ.a2013.0111. Epub 2013 Aug 30.
Previously presented new electrocardiography (ECG) algorithm for localization of arrhythmogenic focus (AFo) in right ventricular outflow tract (RVOT) was based on spontaneous arrhythmia QRS morphology analysis. The aim of this study was to estimate the clinical value of our RVOT algorithm in a prospective study.
Algorithm validation was made on 62 patients with RVOT arrhythmias (45 women), mean age 41.6 ± 14.3 years, scheduled for transcatheter ablation. Results of preablation ECG analysis with RVOT algorithm were matched with successful ablation sites and statistical indices: sensitivity (sens), specificity (spec), and positive and negative predictive values (PPV, NPV) were calculated for algorithm and for each of 9 RVOT zones (septal and free wall). An algorithm precisely localized AFo in 57 out of 62 patients (sens 91.3%, spec 99%, PPV 91%, NPV 98.8%). Sensitivity values for superior RVOT aspect (71% patients) varied from 88% to 100%, specificity from 95.9% to 100%; PPV values from 85.7% to 100%, NPV from 92.5% to 100%. Although the total number of patients was relatively small in the 2 remaining RVOT aspects (29% patients) high values (sens, spec, PPV, NPV) were gained for intermediate and inferior zones.
On the basis of spontaneous arrhythmia QRS analysis, a novel algorithm was built for preablation localization of RVOT arrhythmia in 1 of the 9 RVOT zones. Prospective analysis of our ECG algorithm confirmed that it is a valuable tool to predict the site of successful ablation in patients with RVOT arrhythmias.
之前提出的用于定位右心室流出道（RVOT）致心律失常灶（AFo）的新心电图（ECG）算法是基于自发性心律失常的QRS波形态分析。本研究的目的是在前瞻性研究中评估我们的RVOT算法的临床价值。
对62例计划进行经导管消融的RVOT心律失常患者（45例女性）进行了算法验证，平均年龄41.6±14.3岁。使用RVOT算法进行消融前ECG分析的结果与成功消融部位相匹配，并计算统计指标：算法以及9个RVOT区域（间隔和游离壁）中每个区域的敏感性（sens）、特异性（spec）以及阳性和阴性预测值（PPV、NPV）。该算法在62例患者中的57例中精确地定位了AFo（敏感性91.3%，特异性99%，PPV 91%，NPV 98.8%）。RVOT上缘（71%的患者）的敏感性值在88%至100%之间变化，特异性在95.9%至100%之间；PPV值在85.7%至100%之间，NPV在92.5%至100%之间。尽管在其余2个RVOT区域（29%的患者）患者总数相对较少，但中间和下缘区域获得了较高的值（敏感性、特异性、PPV、NPV）。
基于自发性心律失常的QRS分析，构建了一种新算法，用于在9个RVOT区域中的1个区域进行RVOT心律失常消融前定位。对我们的ECG算法的前瞻性分析证实，它是预测RVOT心律失常患者成功消融部位的有价值工具。
