Does spectral turbulence analysis provide better predictive accuracy than time-domain analysis for sustained monomorphic ventricular tachycardia in postinfarction patients with conduction defects?
Spectral turbulence analysis of signal-averaged ECGs offers superior predictive accuracy for sustained monomorphic ventricular tachycardia in postinfarction patients with RBBB or LBBB compared to time-domain analysis.
Signal-averaged electrocardiograms obtained in 86 postinfarction patients with right bundle branch block (RBBB), left bundle branch block (LBBB), or intraventricular conduction defect (IVCD), 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 parameter 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 microV. 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 > or = microV and a subset of 67 patients with noise levels 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-microV noise level and the Sp at 0.5 microV. The addition of ejection fraction data to STA score resulted in further overall improvement in performance, but above conclusions were unchanged.
Flowers et al. (Fri,) studied this question.