Real-Time Localization of Ventricular Tachycardia Origin From the 12-Lead Electrocardiogram

The aim of this study was to develop rapid computational methods for identifying the site of origin of ventricular activation from the 12-lead electrocardiogram. Catheter ablation of ventricular tachycardia in patients with structural heart disease frequently relies on a substrate-based approach, wh...

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Veröffentlicht in:JACC. Clinical electrophysiology 2017-07, Vol.3 (7), p.687-699
Hauptverfasser: Sapp, John L., Bar-Tal, Meir, Howes, Adam J., Toma, Jonathan E., El-Damaty, Ahmed, Warren, James W., MacInnis, Paul J., Zhou, Shijie, Horáček, B. Milan
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Sprache:eng
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Zusammenfassung:The aim of this study was to develop rapid computational methods for identifying the site of origin of ventricular activation from the 12-lead electrocardiogram. Catheter ablation of ventricular tachycardia in patients with structural heart disease frequently relies on a substrate-based approach, which may use pace mapping guided by body-surface electrocardiography to identify culprit exit sites. Patients undergoing ablation of scar-related VT (n = 38) had 12-lead electrocardiograms recorded during pacing at left ventricular endocardial sites (n = 1,012) identified on 3-dimensional electroanatomic maps and registered to a generic left ventricular endocardial surface divided into 16 segments and tessellated into 238 triangles; electrocardiographic data were reduced for each lead to 1 variable, consisting of QRS time integral. Two methods for estimating the origin of activation were developed: 1) a discrete method, estimating segment of activation origin using template matching; and 2) a continuous method, using population-based multiple linear regression to estimate triangle of activation origin. A variant of the latter method was derived, using patient-specific multiple linear regression. The optimal QRS time integral included the first 120 ms of the QRS interval. The mean localization error of population-based regressions was 12 ± 8 mm. Patient-specific regressions can achieve localization accuracy better than 5 mm when at least 10 training-set pacing sites are used; this accuracy further increases with each added pacing site. Computational intraprocedure methods can automatically identify the segment and site of left ventricular activation using novel algorithms, with accuracy within 
ISSN:2405-500X
2405-5018
DOI:10.1016/j.jacep.2017.02.024