Fusion Imaging of Non-Invasive and Invasive Cardiac Electroanatomic Mapping in Patients with Ventricular Ectopic Beats: A Feasibility Analysis in a Case Series.

In patients with premature ventricular contractions (PVCs), non-invasive mapping could locate the PVCs' origin on a personalized 3-dimensional (3D) heart model and, thus, facilitate catheter ablation therapy planning. The aim of our report is to evaluate its accuracy compared to invasive mapping in terms of assessing the PVCs' early activation zone (EAZ). For this purpose, non-invasive electrocardiographic imaging (ECGI) was performed using the Amycard 01C system (EP Solutions SA, Switzerland) in three cases.

Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy.

The 30-50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance D between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude.

Electrocardiographic imaging to guide ablation of ventricular arrhythmias and agreement between two different systems.

Background And Aim: A recent study using an epicardial-only electrocardiographic imaging (ECGI), suggests that the agreement of ECGI activation mapping and that of the contact mapping for ventricular arrhythmias (VA) is poor. The aim of this study was to assess the diagnostic value of two endo-epicardial ECGI systems using different cardiac sources and the agreement between them.

Methods: We performed 69 ECGI procedures in 52 patients referred for ablation of VA at our center.

Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data.

Up to 30-50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge. The main goal of our study is to develop a predictive model of CRT outcome using a combination of clinical data recorded in patients before CRT and simulations of the response to biventricular (BiV) pacing in personalized computational models of the cardiac electrophysiology.

His-Optimized Cardiac Resynchronization Therapy With Ventricular Fusion Pacing for Electrical Resynchronization in Heart Failure.

Objectives: This study sought to evaluate the effectiveness of His-optimized cardiac resynchronization therapy (HOT-CRT) for reducing left ventricular activation time (LVAT) compared to His bundle pacing (HBP) and biventricular (BiV) pacing (including multipoint pacing [MPP]), using electrocardiographic (ECG) imaging.

Background: HBP may correct bundle branch block (BBB) and has shown encouraging results for providing CRT. However, HBP does not correct BBB in all patients and may be combined with univentricular or BiV fusion pacing to deliver HOT-CRT to maximize resynchronization.

Electrocardiographic imaging (ECGI): What is the minimal number of leads needed to obtain a good spatial resolution?

Aims: Assess the minimal number of ECGI leads needed to obtain a good spatial resolution.

Methods: We enrolled 20 patients that underwent ablation of premature ventricular or atrial contractions using Carto and ECGI with AMYCARD. We evaluated the agreement regarding the site of origin of the arrhythmia between the ECGI and Carto, the area and diameter of the earliest activation site obtained with the ECGI (EASa and EASd).

Non-invasive electrocardiographic imaging in patients with idiopathic premature ventricular contractions from the right ventricular outflow tract: New insights into arrhythmia substrate.

Aims: The aim of this study was to use non-invasive electrocardiographic imaging (ECGI) to study the electrophysiological properties of right ventricular outflow tract (RVOT) in patients with frequent premature ventricular contractions (PVCs) from the RVOT and in controls.

Methods: ECGI is a combined application of body surface electrocardiograms and computed tomography or magnetic resonance imaging data. Unipolar electrograms are reconstructed on the epicardial and endocardial surfaces.

Combination of lead-field theory with cardiac vector direction: ECG imaging of septal ventricular activation.

Background: Despite the tremendous progress recently reported in ECG imaging (ECGI), some fundamental challenges are still hindering this non-invasive technology from meeting rising clinical expectations. In the present work, we address one of the major ECGI shortcomings in reconstruction of ventricular activation - the limited accuracy of endocardial and particularly septal mapping.

Methods: Ten CRT patients (five female, median (min-max) age - 61 (27-78) years) with previously implanted CRT devices underwent ECGI with isolated right ventricular (RV) pacing.

ECG Adapted Fastest Route Algorithm to Localize the Ectopic Excitation Origin in CRT Patients.

Although model-based solution strategies for the ECGI were reported to deliver promising clinical results, they strongly rely on some a priori assumptions, which do not hold true for many pathological cases. The fastest route algorithm (FRA) is a well-established method for noninvasive imaging of ectopic activities. It generates test activation sequences on the heart and compares the corresponding test body surface potential maps (BSPMs) to the measured ones.