Personalized Heart Digital Twins Detect Substrate Abnormalities in Scar-Dependent Ventricular Tachycardia.

Background: Current outcomes from catheter ablation for scar-dependent ventricular tachycardia (VT) are limited by high recurrence rates and long procedure durations. Personalized heart digital twin technology presents a noninvasive method of predicting critical substrate in VT, and its integration into clinical VT ablation offers a promising solution. The accuracy of the predictions of digital twins to detect invasive substrate abnormalities is unknown.

Unipolar voltage electroanatomic mapping detects structural atrial remodeling identified by LGE-MRI.

Background: In atrial fibrillation (AF) management, understanding left atrial (LA) substrate is crucial. While both electroanatomic mapping (EAM) and late gadolinium enhancement magnetic resonance imaging (LGE-MRI) are accepted methods for assessing the atrial substrate and are associated with ablation outcome, recent findings have highlighted discrepancies between low-voltage areas (LVAs) in EAM and LGE areas.

Objective: The purpose of this study was to explore the relationship between LGE regions and unipolar and bipolar LVAs using multipolar high-density mapping.

Optimizing the Distribution of Ablation Lesions to Prevent Postablation Atrial Tachycardia: A Personalized Digital-Twin Study.

Background: Although targeting atrial fibrillation (AF) drivers and substrates has been used as an effective adjunctive ablation strategy for patients with persistent AF (PsAF), it can result in iatrogenic scar-related atrial tachycardia (iAT) requiring additional ablation. Personalized atrial digital twins (DTs) have been used preprocedurally to devise ablation targeting that eliminate the fibrotic substrate arrhythmogenic propensity and could potentially be used to predict and prevent postablation iAT.

Objectives: In this study, the authors sought to explore possible alternative configurations of ablation lesions that could prevent iAT occurrence with the use of biatrial DTs of prospectively enrolled PsAF patients.

Assessing the arrhythmogenic propensity of fibrotic substrate using digital twins to inform a mechanisms-based atrial fibrillation ablation strategy.

Atrial fibrillation (AF), the most common heart rhythm disorder, may cause stroke and heart failure. For patients with persistent AF with fibrosis proliferation, the standard AF treatment-pulmonary vein isolation-has poor outcomes, necessitating redo procedures, owing to insufficient understanding of what constitutes good targets in fibrotic substrates. Here we present a prospective clinical and personalized digital twin study that characterizes the arrhythmogenic properties of persistent AF substrates and uncovers locations possessing rotor-attracting capabilities.

Cardiac MRI Oversampling in Heart Digital Twins Improves Preprocedure Ventricular Tachycardia Identification in Postinfarction Patients.

Background: Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients after myocardial infarction. Radiofrequency catheter ablation (RFA) is a modestly effective treatment of VT, but it has limitations and risks. Cardiac magnetic resonance (CMR)-based heart digital twins have emerged as a useful tool for identifying VT circuits for RFA treatment planning.

Up digital and personal: How heart digital twins can transform heart patient care.

Precision medicine is the vision of health care where therapy is tailored to each patient. As part of this vision, digital twinning technology promises to deliver a digital representation of organs or even patients by using tools capable of simulating personal health conditions and predicting patient or disease trajectories on the basis of relationships learned both from data and from biophysics knowledge. Such virtual replicas would update themselves with data from monitoring devices and medical tests and assessments, reflecting dynamically the changes in our health conditions and the responses to treatment.

Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes.

Predicting Ventricular Tachycardia Circuits in Patients with Arrhythmogenic Right Ventricular Cardiomyopathy using Genotype-specific Heart Digital Twins.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes.

LASSNet: A Four Steps Deep Neural Network for Left Atrial Segmentation and Scar Quantification.

Accurate quantification of left atrium (LA) scar in patients with atrial fibrillation is essential to guide successful ablation strategies. Prior to LA scar quantification, a proper LA cavity segmentation is required to ensure exact location of scar. Both tasks can be extremely time-consuming and are subject to inter-observer disagreements when done manually.

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias.

Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning-based workflow for reconstructing personalized computational electrophysiological heart models to guide patient-specific treatment of VT.

Computational Re-Entry Vulnerability Index Mapping to Guide Ablation in Patients With Postmyocardial Infarction Ventricular Tachycardia.

Background: Ventricular tachycardias (VTs) in patients with myocardial infarction (MI) are often treated with catheter ablation. However, the VT induction during this procedure does not always identify all of the relevant activation pathways or may not be possible or tolerated. The re-entry vulnerability index (RVI) quantifies regional activation-repolarization differences and can detect multiple regions susceptible to re-entry without the need to induce the arrhythmia.

Fat infiltration in the infarcted heart as a paradigm for ventricular arrhythmias.

Infiltrating adipose tissue (inFAT) has been recently found to co-localize with scar in infarcted hearts and may contribute to ventricular arrhythmias (VAs), a life-threatening heart rhythm disorder. However, the contribution of inFAT to VA has not been well-established. We investigated the role of inFAT versus scar in VA through a combined prospective clinical and mechanistic computational study.

Arrhythmia in hypertrophic cardiomyopathy: Risk prediction using contrast enhanced MRI, T1 mapping, and personalized virtual heart technology.

Background: Hypertrophic cardiomyopathy (HCM), a disease with myocardial fibrosis manifestation, is a common cause of sudden cardiac death (SCD) due to ventricular arrhythmias (VA). Current clinical risk stratification criteria are inadequate in identifying patients who are at risk for VA and in need of an implantable cardioverter defibrillator (ICD) for primary prevention.

Objective: We aimed to develop a risk prediction approach based on imaging biomarkers from the combination of late gadolinium contrast-enhanced (LGE) MRI and T1 mapping.

Wavefront directionality and decremental stimuli synergistically improve identification of ventricular tachycardia substrate: insights from personalized computational heart models.

Aims: Multiple wavefront pacing (MWP) and decremental pacing (DP) are two electroanatomic mapping (EAM) strategies that have emerged to better characterize the ventricular tachycardia (VT) substrate. The aim of this study was to assess how well MWP, DP, and their combination improve identification of electrophysiological abnormalities on EAM that reflect infarct remodelling and critical VT sites.

Methods And Results: Forty-eight personalized computational heart models were reconstructed using images from post-infarct patients undergoing VT ablation.

Biological substrate modification suppresses ventricular arrhythmias in a porcine model of chronic ischaemic cardiomyopathy.

Aims: Cardiomyopathy patients are prone to ventricular arrhythmias (VA) and sudden cardiac death. Current therapies to prevent VA include radiofrequency ablation to destroy slowly conducting pathways of viable myocardium which support re-entry. Here, we tested the reverse concept, namely that boosting local tissue viability in zones of slow conduction might eliminate slow conduction and suppress VA in ischaemic cardiomyopathy.

Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is associated with risk of sudden cardiac death (SCD) due to ventricular arrhythmias (VAs) arising from the proliferation of fibrosis in the heart. Current clinical risk stratification criteria inadequately identify at-risk patients in need of primary prevention of VA. Here, we use mechanistic computational modeling of the heart to analyze how HCM-specific remodeling promotes arrhythmogenesis and to develop a personalized strategy to forecast risk of VAs in these patients.

Spatial dispersion analysis of LGE-CMR for prediction of ventricular arrhythmias in patients with cardiac sarcoidosis.

Background: Patients with cardiac sarcoidosis (CS) are at increased risk of life-threatening ventricular arrhythmias (VA). Current approaches to risk stratification have limited predictive value.

Objectives: To assess the utility of spatial dispersion analysis of late gadolinium enhancement cardiac magnetic resonance (LGE-CMR), as a quantitative measure of myocardial tissue heterogeneity, in risk stratifying patients with CS for VA and death.

Analyzing the Role of Repolarization Gradients in Post-infarct Ventricular Tachycardia Dynamics Using Patient-Specific Computational Heart Models.

Disease-induced repolarization heterogeneity in infarcted myocardium contributes to VT arrhythmogenesis but how apicobasal and transmural (AB-TM) repolarization gradients additionally affect post-infarct VT dynamics is unknown. The goal of this study is to assess how AB-TM repolarization gradients impact post-infarct VT dynamics using patient-specific heart models. 3D late gadolinium-enhanced cardiac magnetic resonance images were acquired from seven post-infarct patients.

Electromechanical modeling of human ventricles with ischemic cardiomyopathy: numerical simulations in sinus rhythm and under arrhythmia.

We developed a novel patient-specific computational model for the numerical simulation of ventricular electromechanics in patients with ischemic cardiomyopathy (ICM). This model reproduces the activity both in sinus rhythm (SR) and in ventricular tachycardia (VT). The presence of scars, grey zones and non-remodeled regions of the myocardium is accounted for by the introduction of a spatially heterogeneous coefficient in the 3D electromechanics model.

Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning.

Rationale: Patients with ischemic cardiomyopathy (ICMP) are at high risk for malignant arrhythmias, largely due to electrophysiological remodeling of the non-infarcted myocardium. The electrophysiological properties of the non-infarcted myocardium of patients with ICMP remain largely unknown.

Objectives: To assess the pro-arrhythmic behavior of non-infarcted myocardium in ICMP patients and couple computational simulations with machine learning to establish a methodology for the development of disease-specific action potential models based on clinically measured action potential duration restitution (APDR) data.

Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier.

Cardiac sarcoidosis (CS), an inflammatory disease characterized by formation of granulomas in the heart, is associated with high risk of sudden cardiac death (SCD) from ventricular arrhythmias. Current "one-size-fits-all" guidelines for SCD risk assessment in CS result in insufficient appropriate primary prevention. Here, we present a two-step precision risk prediction technology for patients with CS.

Impact of augmented-reality improvement in ablation catheter navigation as assessed by virtual-heart simulations of ventricular tachycardia ablation.

Background: Recently, an augmented reality (AR) solution allows the physician to place the ablation catheter at the designated lesion site more accurately during cardiac electrophysiology studies. The improvement in navigation accuracy may positively affect ventricular tachycardia (VT) ablation termination, however assessment of this in the clinic would be difficult. Novel personalized virtual heart technology enables non-invasive identification of optimal lesion targets for infarct-related VT.

Feasibility study shows concordance between image-based virtual-heart ablation targets and predicted ECG-based arrhythmia exit-sites.

Introduction: We recently developed two noninvasive methodologies to help guide VT ablation: population-derived automated VT exit localization (PAVEL) and virtual-heart arrhythmia ablation targeting (VAAT). We hypothesized that while very different in their nature, limitations, and type of ablation targets (substrate-based vs. clinical VT), the image-based VAAT and the ECG-based PAVEL technologies would be spatially concordant in their predictions.

Personalized Digital-Heart Technology for Ventricular Tachycardia Ablation Targeting in Hearts With Infiltrating Adiposity.

Background: Infiltrating adipose tissue (inFAT) is a newly recognized proarrhythmic substrate for postinfarct ventricular tachycardias (VT) identifiable on contrast-enhanced computed tomography. This study presents novel digital-heart technology that incorporates inFAT from contrast-enhanced computed tomography to noninvasively predict VT ablation targets and assesses the capability of the technology by comparing its predictions with VT ablation procedure data from patients with ischemic cardiomyopathy.

Methods: Digital-heart models reflecting patient-specific inFAT distributions were reconstructed from contrast-enhanced computed tomography.