A left bundle branch block activation sequence and ventricular pacing influence voltage amplitudes: anin vivoandin silicostudy

Abstract

Aims he aim of this study was to investigate the influence of the activation sequence on voltage amplitudes by evaluating regional voltage differences during a left bundle branch block (LBBB) activation sequence vs. a normal synchronous activation sequence and by evaluating pacing-induced voltage differences. Methods and results wenty-one patients and three computer models without scar were studied. Regional voltage amplitudes were evaluated in nine LBBB patients who underwent endocardial electro-anatomic mapping (EAM). Pacing-induced voltage differences were evaluated in 12 patients who underwent epicardial EAM during intrinsic rhythm and right ventricular (RV) pacing. Three computer models customized for LBBB patients were created. Changes in voltage amplitudes after an LBBB (intrinsic), a normal synchronous, an RV pacing, and a left ventricular pacing activation sequence were assessed in the computer models. Unipolar voltage amplitudes in patients were approximately 4.5 mV (4.4-4.7 mV, 33%) lower in the septum when compared with other segments. A normal synchronous activation sequence in the computer models normalized voltage amplitudes in the septum. Pacinginduced differences were larger in electrograms with higher voltage amplitudes during intrinsic rhythm and furthermore larger and more variable at the epicardium [mean absolute difference: 3.6-6.2 mV, 40-53% of intrinsic value; interquartile range (IQR) differences: 53-63% of intrinsic value] compared to the endocardium (mean absolute difference: 3.3-3.8 mV, 28-30% of intrinsic value; IQR differences: 37-40% of intrinsic value). Conclusion In patients and computer models without scar, lower septal unipolar voltage amplitudes are exclusively associated with an LBBB activation sequence. Pacing substantially affects voltage amplitudes, particularly at the epicardium.

Influence of Heart Rate and Change in Wavefront Direction through Pacing on Conduction Velocity and Voltage Amplitude in a Porcine Model: A High-Density Mapping Study

“…Most previous studies focused on the LV, where we found a lowering effect of RA pacing and no effect of RV or LV pacing. Our result for RV pacing in the LV is comparable to a study by Nguyên et al on patients with left bundle branch blocks, where RV pacing did not change the overall unipolar VA in the epicardial LV [11]. Similarly to our study, the absolute values varied considerably.…”

“…Substrate mapping is employed in this context to identify areas such as local abnormal ventricular activities (LAVA) [2], zones of slow conduction [3], low voltage areas [4,5], and complex fractionated atrial electrograms (CFAE) [6]. The visibility of these crucial regions often depends on the strategy of pacing, [7][8][9][10][11][12], as conduction is typically faster along the cardiac fibers than it is across them [13,14]. To accurately interpret the changes induced by pacing, understanding the physiological variations in VC and VA during this process is essential.…”

The influence of scar on the spatio-temporal relationship between electrical and mechanical activation in heart failure patients

“…Briefly, the centres of mass of LGE and AEMM surfaces were aligned using a three-dimensional translation, then an iterative closest-point approach was used to minimize the Euclidean distance between the electrodes and the endocardial contours traced on CMR images. 13 What's new?…”

“…Invasive (Q-LV, RV-LV interval, and ECG imaging) seems more useful, especially when combined with scar imaging in order to avoid positioning of the lead in a scarred region. 13 The growing availability and adoption of ECG-imaging technologies may facilitate the accurate identification of the site of latest electrical activation. However, the benefit of these non-invasive electrical approaches needs to be supported by future trials.…”

Computer Modeling of the Heart for ECG Interpretation—A Review

“…Modeling Challenge References healthy heart-QRS modeling the Purkinje tree [1][2][3][4][5][6][7][8][9] healthy heart-T-wave modeling heterogeneity of repolarization [10][11][12][13] healthy heart-P-wave modeling sinus node excitation and pathways from right to left atrium, anatomical variability [14][15][16][17][18][19][20][21][22] ischemia and infarction modeling the effect of hyperkalemia, acidosis, hypoxia and cell-to-cell uncoupling [23][24][25][26][27][28][29][30] ventricular ectopic beats localization with 12-lead ECG [31][32][33][34] ventricular tachycardia localization of exit points with 12-lead ECG [29,35] cardiomyopathy modeling typical changes of QRS-and T-wave [36] bundle branch blocks LBBB and RBBB modeling asynchrony [37][38][39][40] atrial ectopic beats localization with 12-lead ECG [32][33][34] atrial tachycardia, flutter modeling all types of flutter…”