The Comparative Predictive Accuracy of Spectral Turbulence Analysis and Time-Domain Analysis for Sustained Monomorphic Ventricular Tachycardia in Postinfarction Patients with Conduction Defects

Signal-averaged electrocardiograms obtained in 86 postinfarction patients with right bundle branch block (RBBB), left bundle branch block (LBBB), or intraventricular conduction defect (IVC-D), underwent time-domain analysis (TDA) and spectral turbulence analysis (STA) to determine which approach provided the more effective marker for patients with sustained monomorphic ventricular tachycardia. TDA parameters included the root mean square value of the last 40 ms of the vectormagnitude complex and the duration of the low amplitude signal below 40 μV STA utilized a summation lead (X + Y + Z) and quantitated four parameters: interslice correlation mean, interslice correlation standard deviation, low slice correlation ratio, and spectral entropy. High-pass filters of 40 Hz and 25 Hz were used to study the total patient population with noise levels ≤1 μV and a subset of 67 patients with noise levels ≤ 0.5 μV, The techniques compared their effectiveness as measured by their positive predictive values (PPV), negative predictive values (NPV), sensitivity (Sn), and specificity (Sp). In RBBB, STA was uniformly a more powerful tool utilizing either filter at both noise levels. In LBBB, STA was consistently more powerful at both noise levels at 40 Hz and, generally, more powerful at 25 Hz with isolated exceptions. In conduction defects in which QRS was > 100 ms but < 120 ms, TDA was equal to or more effective than STA, with the exception of PPV and Sp at 40 Hz at 1-μV noise level and the Sp at 0.5 μV. The addition of ejection fraction data to STA score resulted in further overall improvement in performance, but above conclusions were unchanged.