LVSP and LBBP Result in Similar or Improved LV Synchrony and Hemodynamics Compared to BVP.

BACKGROUND: The effect of left ventricular septal myocardial pacing (LVSP) and left bundle branch pacing (LBBP) on ventricular synchrony and left ventricular (LV) hemodynamic status is poorly understood. OBJECTIVES: The aim of this study was to investigate the impact of LVSP and LBBP vs biventricular pacing (BVP) on ventricular electrical synchrony and hemodynamic status in cardiac resynchronization therapy patients. METHODS: In cardiac resynchronization therapy candidates with LV conduction disease, ventricular synchrony was assessed by measuring QRS duration (QRSd) and using ultra-high-frequency electrocardiography. LV electrical dyssynchrony was assessed as the difference between the first activation in leads V1 to V8 to the last from leads V4 to V8. LV hemodynamic status was estimated using invasive systolic blood pressure measurement during multiple transitions between LBBP, LVSP, and BVP. RESULTS: A total of 35 patients with a mean LV ejection fraction of 29% and a mean QRSd of 168 ± 24 ms were included. Thirteen had ischemic cardiomyopathy. QRSd during BVP, LVSP, and LBBP was the same, but LBBP provided shorter LV electrical dyssynchrony than BVP (-10 ms; 95% CI: -16 to -4 ms; P = 0.001); the difference between LVSP and BVP was not significant (-5 ms; 95% CI: -12 to 1 ms; P = 0.10). LBBP was associated with higher systolic blood pressure than BVP (4%; 95% CI: 2%-5%; P < 0.001), whereas LVSP was not (1%; 95% CI: 0%-2%; P = 0.10). Hemodynamic differences during LBBP and LVSP vs BVP were more pronounced in nonischemic than ischemic patients. CONCLUSIONS: Ultra-high-frequency electrocardiography allowed the documentation of differences in LV synchrony between LBBP, LVSP, and BVP, which were not observed by measuring QRSd. LVSP provided the same LV synchrony and hemodynamic status as BVP, while LBBP was better than BVP in both.

Ventricular dyssynchrony imaging, echocardiographic and clinical outcomes of left bundle branch pacing and biventricular pacing.

BACKGROUND: Left bundle branch pacing (LBBP) is a novel physiological pacing technique which may serve as an alternative to cardiac resynchronization therapy (CRT) by biventricular pacing (BVP). This study assessed ventricular activation patterns and echocardiographic and clinical outcomes of LBBP and compared this to BVP. METHODS: Fifty consecutive patients underwent LBBP or BVP for CRT. Ventricular activation mapping was obtained by ultra-high-frequency ECG (UHF-ECG). Functional and echocardiographic outcomes and hospitalization for heart failure and all-cause mortality after one year from implantation were evaluated. RESULTS: LBBP resulted in greater resynchronization vs BVP (QRS width: 170 ± 16 ms to 128 ± 20 ms vs 174 ± 15 to 144 ± 17 ms, p = 0.002 (LBBP vs BVP); e-DYS 81 ± 17 ms to 0 ± 32 ms vs 77 ± 18 to 16 ± 29 ms, p = 0.016 (LBBP vs BVP)). Improvement in LVEF (from 28 ± 8 to 42 ± 10 percent vs 28 ± 9 to 36 ± 12 percent, LBBP vs BVP, p = 0.078) was similar. Improvement in NYHA function class (from 2.4 to 1.5 and from 2.3 to 1.5 (LBBP vs BVP)), hospitalization for heart failure and all-cause mortality were comparable in both groups. CONCLUSIONS: Ventricular dyssynchrony imaging is an appropriate way to gain a better insight into activation patterns of LBBP and BVP. LBBP resulted in greater resynchronization (e-DYS and QRS duration) with comparable improvement in LVEF, NYHA functional class, hospitalization for heart failure and all-cause mortality at one year of follow up.

Ultra-High-Frequency ECG in Cardiac Pacing and Cardiac Resynchronization Therapy: From Technical Concept to Clinical Application.

Identifying electrical dyssynchrony is crucial for cardiac pacing and cardiac resynchronization therapy (CRT). The ultra-high-frequency electrocardiography (UHF-ECG) technique allows instantaneous dyssynchrony analyses with real-time visualization. This review explores the physiological background of higher frequencies in ventricular conduction and the translational evolution of UHF-ECG in cardiac pacing and CRT. Although high-frequency components were studied half a century ago, their exploration in the dyssynchrony context is rare. UHF-ECG records ECG signals from eight precordial leads over multiple beats in time. After initial conceptual studies, the implementation of an instant visualization of ventricular activation led to clinical implementation with minimal patient burden. UHF-ECG aids patient selection in biventricular CRT and evaluates ventricular activation during various forms of conduction system pacing (CSP). UHF-ECG ventricular electrical dyssynchrony has been associated with clinical outcomes in a large retrospective CRT cohort and has been used to study the electrophysiological differences between CSP methods, including His bundle pacing, left bundle branch (area) pacing, left ventricular septal pacing and conventional biventricular pacing. UHF-ECG can potentially be used to determine a tailored resynchronization approach (CRT through biventricular pacing or CSP) based on the electrical substrate (true LBBB vs. non-specified intraventricular conduction delay with more distal left ventricular conduction disease), for the optimization of CRT and holds promise beyond CRT for the risk stratification of ventricular arrhythmias.

Ultra-high-frequency ECG volumetric and negative derivative epicardial ventricular electrical activation pattern.

From precordial ECG leads, the conventional determination of the negative derivative of the QRS complex (ND-ECG) assesses epicardial activation. Recently we showed that ultra-high-frequency electrocardiography (UHF-ECG) determines the activation of a larger volume of the ventricular wall. We aimed to combine these two methods to investigate the potential of volumetric and epicardial ventricular activation assessment and thereby determine the transmural activation sequence. We retrospectively analyzed 390 ECG records divided into three groups-healthy subjects with normal ECG, left bundle branch block (LBBB), and right bundle branch block (RBBB) patients. Then we created UHF-ECG and ND-ECG-derived depolarization maps and computed interventricular electrical dyssynchrony. Characteristic spatio-temporal differences were found between the volumetric UHF-ECG activation patterns and epicardial ND-ECG in the Normal, LBBB, and RBBB groups, despite the overall high correlations between both methods. Interventricular electrical dyssynchrony values assessed by the ND-ECG were consistently larger than values computed by the UHF-ECG method. Noninvasively obtained UHF-ECG and ND-ECG analyses describe different ventricular dyssynchrony and the general course of ventricular depolarization. Combining both methods based on standard 12-lead ECG electrode positions allows for a more detailed analysis of volumetric and epicardial ventricular electrical activation, including the assessment of the depolarization wave direction propagation in ventricles.

Timing matters for accurate identification of the epileptogenic zone.

OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intracranial EEG (iEEG). METHODS: We used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC). RESULTS: On average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323-0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments. CONCLUSIONS: The model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep. SIGNIFICANCE: Random selection of short iEEG segments may give rise to inaccurate localization of the EZ.