Advances in Cardiac Electrophysiology.

Despite the global COVID-19 pandemic, during the past 2 years, there have been numerous advances in our understanding of arrhythmia mechanisms and diagnosis and in new therapies. We increased our understanding of risk factors and mechanisms of atrial arrhythmias, the prediction of atrial arrhythmias, response to treatment, and outcomes using machine learning and artificial intelligence. There have been new technologies and techniques for atrial fibrillation ablation, including pulsed field ablation. There have been new randomized trials in atrial fibrillation ablation, giving insight about rhythm control, and long-term outcomes. There have been advances in our understanding of treatment of inherited disorders such as catecholaminergic polymorphic ventricular tachycardia. We have gained new insights into the recurrence of ventricular arrhythmias in the setting of various conditions such as myocarditis and inherited cardiomyopathic disorders. Novel computational approaches may help predict occurrence of ventricular arrhythmias and localize arrhythmias to guide ablation. There are further advances in our understanding of noninvasive radiotherapy. We have increased our understanding of the role of His bundle pacing and left bundle branch area pacing to maintain synchronous ventricular activation. There have also been significant advances in the defibrillators, cardiac resynchronization therapy, remote monitoring, and infection prevention. There have been advances in our understanding of the pathways and mechanisms involved in atrial and ventricular arrhythmogenesis.

Optogenetic Stimulation Using Anion Channelrhodopsin (GtACR1) Facilitates Termination of Reentrant Arrhythmias With Low Light Energy Requirements: A Computational Study.

Optogenetic defibrillation of hearts expressing light-sensitive cation channels (e.g., ChR2) has been proposed as an alternative to conventional electrotherapy. Past modeling work has shown that ChR2 stimulation can depolarize enough myocardium to interrupt arrhythmia, but its efficacy is limited by light attenuation and high energy needs. These shortcomings may be mitigated by using new optogenetic proteins like Guillardia theta Anion Channelrhodopsin (GtACR1), which produces a repolarizing outward current upon illumination. Accordingly, we designed a study to assess the feasibility of GtACR1-based optogenetic arrhythmia termination in human hearts. We conducted electrophysiological simulations in MRI-based atrial or ventricular models (n = 3 each), with pathological remodeling from atrial fibrillation or ischemic cardiomyopathy, respectively. We simulated light sensitization via viral gene delivery of three different opsins (ChR2, red-shifted ChR2, GtACR1) and uniform endocardial illumination at the appropriate wavelengths (blue, red, or green light, respectively). To analyze consistency of arrhythmia termination, we varied pulse timing (three evenly spaced intervals spanning the reentrant cycle) and intensity (atrial: 0.001-1 mW/mm2; ventricular: 0.001-10 mW/mm2). In atrial models, GtACR1 stimulation with 0.005 mW/mm2 green light consistently terminated reentry; this was 10-100x weaker than the threshold levels for ChR2-mediated defibrillation. In ventricular models, defibrillation was observed in 2/3 models for GtACR1 stimulation at 0.005 mW/mm2 (100-200x weaker than ChR2 cases). In the third ventricular model, defibrillation failed in nearly all cases, suggesting that attenuation issues and patient-specific organ/scar geometry may thwart termination in some cases. Across all models, the mechanism of GtACR1-mediated defibrillation was voltage forcing of illuminated tissue toward the modeled channel reversal potential of -40 mV, which made propagation through affected regions impossible. Thus, our findings suggest GtACR1-based optogenetic defibrillation of the human heart may be feasible with ≈2-3 orders of magnitude less energy than ChR2.

Machine Learning in Arrhythmia and Electrophysiology.

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.

Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

Prospective Assessment of an Automated Intraprocedural 12-Lead ECG-Based System for Localization of Early Left Ventricular Activation.

BACKGROUND: To facilitate ablation of ventricular tachycardia (VT), an automated localization system to identify the site of origin of left ventricular activation in real time using the 12-lead ECG was developed. The objective of this study was to prospectively assess its accuracy. METHODS: The automated site of origin localization system consists of 3 steps: (1) localization of ventricular segment based on population templates, (2) population-based localization within a segment, and (3) patient-specific site localization. Localization error was assessed by the distance between the known reference site and the estimated site. RESULTS: In 19 patients undergoing 21 catheter ablation procedures of scar-related VT, site of origin localization accuracy was estimated using 552 left ventricular endocardial pacing sites pooled together and 25 VT-exit sites identified by contact mapping. For the 25 VT-exit sites, localization error of the population-based localization steps was within 10 mm. Patient-specific site localization achieved accuracy of within 3.5 mm after including up to 11 pacing (training) sites. Using 3 remotes (67.8±17.0 mm from the reference VT-exit site), and then 5 close pacing sites, resulted in localization error of 7.2±4.1 mm for the 25 identified VT-exit sites. In 2 emulated clinical procedure with 2 induced VTs, the site of origin localization system achieved accuracy within 4 mm. CONCLUSIONS: In this prospective validation study, the automated localization system achieved estimated accuracy within 10 mm and could thus provide clinical utility.

Accurate Conduction Velocity Maps and Their Association With Scar Distribution on Magnetic Resonance Imaging in Patients With Postinfarction Ventricular Tachycardias.

BACKGROUND: Characterizing myocardial conduction velocity (CV) in patients with ischemic cardiomyopathy (ICM) and ventricular tachycardia (VT) is important for understanding the patient-specific proarrhythmic substrate of VTs and therapeutic planning. The objective of this study is to accurately assess the relation between CV and myocardial fibrosis density on late gadolinium-enhanced cardiac magnetic resonance imaging (LGE-CMR) in patients with ICM. METHODS: We enrolled 6 patients with ICM undergoing VT ablation and 5 with structurally normal left ventricles (controls) undergoing premature ventricular contraction or VT ablation. All patients underwent LGE-CMR and electroanatomic mapping (EAM) in sinus rhythm (2960 electroanatomic mapping points analyzed). We estimated CV from electroanatomic mapping local activation time using the triangulation method that provides an accurate estimate of CV as it accounts for the direction of wavefront propagation. We evaluated the association between LGE-CMR intensity and CV with multilevel linear mixed models. RESULTS: Median CV in patients with ICM and controls was 0.41 m/s and 0.65 m/s, respectively. In patients with ICM, CV in areas with no visible fibrosis was 0.81 m/s (95% CI, 0.59-1.12 m/s). For each 25% increase in normalized LGE intensity, CV decreased by 1.34-fold (95% CI, 1.25-1.43). Dense scar areas have, on average, 1.97- to 2.66-fold slower CV compared with areas without dense scar. Ablation lesions that terminated VTs were localized in areas of slow conduction on CV maps. CONCLUSIONS: CV is inversely associated with LGE-CMR fibrosis density in patients with ICM. Noninvasive derivation of CV maps from LGE-CMR is feasible. Integration of noninvasive CV maps with electroanatomic mapping during substrate mapping has the potential to improve procedural planning and outcomes. Visual Overview: A visual overview is available for this article.

Epicardial Conduction Speed, Electrogram Abnormality, and Computed Tomography Attenuation Associations in Arrhythmogenic Right Ventricular Cardiomyopathy.

OBJECTIVES: This study sought to evaluate the association between contrast-enhanced multidetector computed tomography (CE-MDCT) attenuation and local epicardial conduction speed (ECS) and electrographic abnormalities in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) and ventricular tachycardia (VT). BACKGROUND: CE-MDCT is a widely available and fast imaging technology with high spatial resolution that is less prone to defibrillator generator-related safety issues and image artifacts. However, the association between hypoattenuation on MDCT and VT substrates in ARVC remains unknown. METHODS: Patients with ARVC who underwent CE-MDCT followed by endocardial (n = 30) and epicardial (n = 21) electroanatomical mapping (EAM) and VT ablation were prospectively enrolled. Right ventricular (RV) mid-myocardial attenuation was calculated from 3-dimensional MDCT images and registered to EAM. Local ECS was calculated by averaging the ECS between each point and 5 adjacent points with concordant wave front direction. RESULTS: A total of 17,311 epicardial and 5,204 endocardial points were included. In multivariable regression analysis clustered by patient, RV myocardial attenuation was associated with epicardial bipolar voltage amplitude (2.5% decrease in amplitude per 10 HU decrease in attenuation; p < 0.001), with endocardial unipolar voltage amplitude (0.9% decrease in amplitude per 10 HU decrease in attenuation; p < 0.001), and with ECS (0.4% decrease in ECS per 10 HU decrease in attenuation; p = 0.001). CONCLUSIONS: CE-MDCT attenuation distribution is associated with regional ECS and electrographic amplitude in ARVC. Regions with low attenuation likely reflect fibro-fatty involvement in the RV and may serve as important VT substrates in patients with ARVC who are undergoing VT ablation.

4D cardiac electromechanical activation imaging.

Cardiac abnormalities, a major cause of morbidity and mortality, affect millions of people worldwide. Despite the urgent clinical need for early diagnosis, there is currently no noninvasive technique that can infer to the electrical function of the whole heart in 3D and thereby localize abnormalities at the point of care. Here we present a new method for noninvasive 4D mapping of the cardiac electromechanical activity in a single heartbeat for heart disease characterization such as arrhythmia and infarction. Our novel technique captures the 3D activation wave of the heart in vivo using high volume-rate (500 volumes per second) ultrasound with a 32 × 32 matrix array. Electromechanical activation maps are first presented in a normal and infarcted cardiac model in silico and in canine heart during pacing and re-entrant ventricular tachycardia in vivo. Noninvasive 4D electromechanical activation mapping in a healthy volunteer and a heart failure patient are also determined. The technique described herein allows for direct, simultaneous and noninvasive visualization of electromechanical activation in 3D, which provides complementary information on myocardial viability and/or abnormality to clinical imaging.

Field of view of mapping catheters quantified by electrogram associations with radius of myocardial attenuation on contrast-enhanced cardiac computed tomography.

BACKGROUND: Contrast-enhanced cardiac computed tomography (CE-CT) provides useful substrate characterization in patients with ventricular tachycardia (VT). OBJECTIVE: The purpose of this study was to describe the association between endocardial electrogram measurements and myocardial characteristics on CE-CT, in particular the field of view of electrogram features. METHODS: Fifteen patients with postinfarct VT who underwent catheter ablation with preprocedural CE-CT were included. Electroanatomic maps were registered to CE-CT, and myocardial attenuation surrounding each endocardial point was measured at a radius of 5, 10, and 15 mm. The association between endocardial voltage and attenuation was assessed using a multilevel random effects linear regression model, clustered by patient, with best model fit defined by highest log likelihood. RESULTS: A total of 4698 points were included. There was a significant association of bipolar and unipolar voltage with myocardial attenuation at all radii. For unipolar voltage, the best model fit was at an analysis radius of 15 mm regardless of the mapping catheter used. For bipolar voltage, the best model fit was at an analysis radius of 15 mm for points acquired with a conventional ablation catheter. In contrast, the best model fit for points acquired with a multipolar mapping catheter was at an analysis radius of 5 mm. CONCLUSION: Myocardial attenuation on CE-CT indicates a smaller myocardial field of view of bipolar electrograms using multipolar catheters with smaller electrodes in comparison to standard ablation catheters despite similar interelectrode spacing. Smaller electrodes may provide improved spatial resolution for the definition of myocardial substrate for VT ablation.

Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology.

Model personalization requires the estimation of patient-specific tissue properties in the form of model parameters from indirect and sparse measurement data. Moreover, a low-dimensional representation of the parameter space is needed, which often has a limited ability to reveal the underlying tissue heterogeneity. As a result, significant uncertainty can be associated with the estimated values of the model parameters which, if left unquantified, will lead to unknown variability in model outputs that will hinder their reliable clinical adoption. Probabilistic estimation of model parameters, however, remains an unresolved challenge. Direct Markov Chain Monte Carlo (MCMC) sampling of the posterior distribution function (pdf) of the parameters is infeasible because it involves repeated evaluations of the computationally expensive simulation model. To accelerate this inference, one popular approach is to construct a computationally efficient surrogate and sample from this approximation. However, by sampling from an approximation, efficiency is gained at the expense of sampling accuracy. In this paper, we address this issue by integrating surrogate modeling of the posterior pdf into accelerating the Metropolis-Hastings (MH) sampling of the exact posterior pdf. It is achieved by two main components: (1) construction of a Gaussian process (GP) surrogate of the exact posterior pdf by actively selecting training points that allow for a good global approximation accuracy with a focus on the regions of high posterior probability; and (2) use of the GP surrogate to improve the proposal distribution in MH sampling, in order to improve the acceptance rate. The presented framework is evaluated in its estimation of the local tissue excitability of a cardiac electrophysiological model in both synthetic data experiments and real data experiments. In addition, the obtained posterior distributions of model parameters are interpreted in relation to the factors contributing to parameter uncertainty, including different low-dimensional representations of the parameter space, parameter non-identifiability, and parameter correlations.

Termination of re-entrant atrial tachycardia via optogenetic stimulation with optimized spatial targeting: insights from computational models.

KEY POINTS: Optogenetics has emerged as a potential alternative to electrotherapy for treating heart rhythm disorders, but its applicability for terminating atrial arrhythmias remains largely unexplored. We used computational models reconstructed from clinical MRI scans of fibrotic patient atria to explore the feasibility of optogenetic termination of atrial tachycardia (AT), comparing two different illumination strategies: distributed vs. targeted. We show that targeted optogenetic stimulation based on automated, non-invasive flow-network analysis of patient-specific re-entry morphology may be a reliable approach for identifying the optimal illumination target in each individual (i.e. the critical AT isthmus). The above-described approach yields very high success rates (up to 100%) and requires dramatically less input power than distributed illumination We conclude that simulations in patient-specific models show that targeted light pulses lasting longer than the AT cycle length can efficiently and reliably terminate AT if the human atria can be successfully light-sensitized via gene delivery of ChR2. ABSTRACT: Optogenetics has emerged as a potential alternative to electrotherapy for treating arrhythmia, but feasibility studies have been limited to ventricular defibrillation via epicardial light application. Here, we assess the efficacy of optogenetic atrial tachycardia (AT) termination in human hearts using a strategy that targets for illumination specific regions identified in an automated manner. In three patient-specific models reconstructed from late gadolinium-enhanced MRI scans, we simulated channelrhodopsin-2 (ChR2) expression via gene delivery. In all three models, we attempted to terminate re-entrant AT (induced via rapid pacing) via optogenetic stimulation. We compared two strategies: (1) distributed illumination of the endocardium by multi-optrode grids (number of optrodes, Nopt  = 64, 128, 256) and (2) targeted illumination of the critical isthmus, which was identified via analysis of simulated activation patterns using an algorithm based on flow networks. The illuminated area and input power were smaller for the targeted approach (19-57.8 mm2 ; 0.6-1.8 W) compared to the sparsest distributed arrays (Nopt  = 64; 124.9 ± 6.3 mm2 ; 3.9 ± 0.2 W). AT termination rates for distributed illumination were low, ranging from <5% for short pulses (1/10 ms long) to ∼20% for longer stimuli (100/1000 ms). When we attempted to terminate the same AT episodes with targeted illumination, outcomes were similar for short pulses (1/10 ms long: 0% success) but improved for longer stimuli (100 ms: 54% success; 1000 ms: 90% success). We conclude that simulations in patient-specific models show that light pulses lasting longer than the AT cycle length can efficiently and reliably terminate AT in atria light-sensitized via gene delivery. We show that targeted optogenetic stimulation based on analysis of AT morphology may be a reliable approach for defibrillation and requires less power than distributed illumination.

A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction.

AIM: To predict arrhythmia susceptibility in myocardial infarction (MI) patients with left ventricular ejection fraction (LVEF) >35% using a personalized virtual heart simulation approach. METHODS AND RESULTS: A total of four contrast enhanced magnetic resonance imaging (MRI) datasets of patient hearts with MI and average LVEF of 44.0 ± 2.6% were used in this study. Because of the preserved LVEF, the patients were not indicated for implantable cardioverter defibrillator (ICD) insertion. One patient had spontaneous ventricular tachycardia (VT) prior to the MRI scan; the others had no arrhythmic events. Simulations of arrhythmia susceptibility were blind to clinical outcome. Models were constructed from patient MRI images segmented to identify myocardium, grey zone, and scar based on pixel intensity. Grey zone was modelled as having altered electrophysiology. Programmed electrical stimulation (PES) was performed to assess VT inducibility from 19 bi-ventricular sites in each heart model. Simulations successfully predicted arrhythmia risk in all four patients. For the patient with arrhythmic event, in-silico PES resulted in VT induction. Simulations correctly predicted that VT was non-inducible for the three patients with no recorded VT events. CONCLUSIONS: Results demonstrate that the personalized virtual heart simulation approach may provide a novel risk stratification modality to non-invasively and effectively identify patients with LVEF >35% who could benefit from ICD implantation.

Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia.

: Atrial arrhythmias involving a fibrotic substrate are an important cause of morbidity and mortality. In many cases, effective treatment of such rhythm disorders is severely hindered by a lack of mechanistic understanding relating features of fibrotic remodelling to dynamics of re-entrant arrhythmia. With the advent of clinical imaging modalities capable of resolving the unique fibrosis spatial pattern present in the atria of each individual patient, a promising new research trajectory has emerged in which personalized computational models are used to analyse mechanistic underpinnings of arrhythmia dynamics based on the distribution of fibrotic tissue. In this review, we first present findings that have yielded a robust and detailed biophysical representation of fibrotic substrate electrophysiological properties. Then, we summarize the results of several recent investigations seeking to use organ-scale models of the fibrotic human atria to derive new insights on mechanisms of arrhythmia perpetuation and to develop novel strategies for model-assisted individualized planning of catheter ablation procedures for atrial arrhythmias.

Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms.

AIMS: Catheter ablation is an effective technique for terminating atrial arrhythmia. However, given a high atrial fibrillation (AF) recurrence rate, optimal ablation strategies have yet to be defined. Computer modelling can be a powerful aid but modelling of fibrosis, a major factor associated with AF, is an open question. Several groups have proposed methodologies based on imaging data, but no comparison to determine which methodology best corroborates clinically observed reentrant behaviour has been performed. We examined several methodologies to determine the best method for capturing fibrillation dynamics. METHODS AND RESULTS: Patient late gadolinium-enhanced magnetic resonance imaging data were transferred onto a bilayer atrial computer model and used to assign fibrosis distributions. Fibrosis was modelled as conduction disturbances (lower conductivity, edge splitting, or percolation), transforming growth factor-ß1 ionic channel effects, myocyte-fibroblast coupling, and combinations of the preceding. Reentry was induced through pulmonary vein ectopy and the ensuing rotor dynamics characterized. Non-invasive electrocardiographic imaging data of the patients in AF was used for comparison. Electrograms were computed and the fractionation durations measured over the surface. Edge splitting produced more phase singularities from wavebreaks than the other representations. The number of phase singularities seen with percolation was closer to the clinical values. Addition of fibroblast coupling had an organizing effect on rotor dynamics. Simple tissue conductivity changes with ionic changes localized rotors over fibrosis which was not observed with clinical data. CONCLUSION: The specific representation of fibrosis has a large effect on rotor dynamics and needs to be carefully considered for patient specific modelling.

Association of left atrial epicardial adipose tissue with electrogram bipolar voltage and fractionation: Electrophysiologic substrates for atrial fibrillation.

BACKGROUND: Epicardial adipose tissue (EAdT) is metabolically active and likely contributes to atrial fibrillation (AF) through the release of inflammatory cytokines into the myocardium or through its rich innervation with ganglionated plexi at the pulmonary vein ostia. The electrophysiologic mechanisms underlying the association between EAdT and AF remain unclear. OBJECTIVE: The purpose of this study was to investigate the association of EAdT with adjacent myocardial substrate. METHODS: Thirty consecutive patients who underwent cardiac computed tomography as well as electroanatomic mapping in sinus rhythm before an initial AF ablation procedure were studied. Semiautomatic segmentation of atrial EAdT was performed and registered anatomically to the voltage map. RESULTS: In multivariable regression analysis clustered by patient, age (-0.01 per year) and EAdT (-0.29) were associated with log bipolar voltage as well as low-voltage zones (<0.5 mV). Age (odds ratio [OR]: 1.02 per year), male gender (OR: 3.50), diabetes (OR: 2.91), hypertension (OR: 2.55), and EAdT (OR: 8.56) were associated with fractionated electrograms, and age (OR: 2.80), male gender (OR: 3.00), and EAdT (OR: 7.03) were associated with widened signals. Age (OR: 1.03 per year) and body mass index (OR: 1.06 per kg/m2) were associated with atrial fat. CONCLUSION: The presence of overlaying EAdT was associated with lower bipolar voltage and electrogram fractionation as electrophysiologic substrates for AF. EAdT was not a statistical mediator of the association between clinical variables and AF substrate. Body mass index was directly associated with the presence of EAdT in patients with AF.

Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern.

AIMS: The mechanisms underlying persistent atrial fibrillation (AF) in patients with atrial fibrosis are poorly understood. The goal of this study was to use patient-derived atrial models to test the hypothesis that AF re-entrant drivers (RDs) persist only in regions with specific fibrosis patterns. METHODS AND RESULTS: Twenty patients with persistent AF (PsAF) underwent late gadolinium-enhanced MRI to detect the presence of atrial fibrosis. Segmented images were used to construct personalized 3D models of the fibrotic atria with biophysically realistic atrial electrophysiology. In each model, rapid pacing was applied to induce AF. AF dynamics were analysed and RDs were identified using phase mapping. Fibrosis patterns in RD regions were characterized by computing maps of fibrosis density (FD) and entropy (FE). AF was inducible in 13/20 models and perpetuated by few RDs (2.7 ± 1.5) that were spatially confined (trajectory of phase singularities: 7.6 ± 2.3 mm). Compared with the remaining atrial tissue, regions where RDs persisted had higher FE (IQR: 0.42-0.60 vs. 0.00-0.40, P < 0.05) and FD (IQR: 0.59-0.77 vs. 0.00-0.33, P < 0.05). Machine learning classified RD and non-RD regions based on FD and FE and identified a subset of fibrotic boundary zones present in 13.8 ± 4.9% of atrial tissue where 83.5 ± 2.4% of all RD phase singularities were located. CONCLUSION: Patient-derived models demonstrate that AF in fibrotic substrates is perpetuated by RDs persisting in fibrosis boundary zones characterized by specific regional fibrosis metrics (high FE and FD). These results provide new insights into the mechanisms that sustain PsAF and could pave the way for personalized, MRI-based management of PsAF.

Opsin spectral sensitivity determines the effectiveness of optogenetic termination of ventricular fibrillation in the human heart: a simulation study.

KEY POINTS: Optogenetics-based defibrillation, a theoretical alternative to electrotherapy, involves expression of light-sensitive ion channels in the heart (via gene or cell therapy) and illumination of the cardiac surfaces (via implanted LED arrays) to elicit light-induced activations. We used a biophysically detailed human ventricular model to determine whether such a therapy could terminate fibrillation (VF) and identify which combinations of light-sensitive ion channel properties and illumination configurations would be effective. Defibrillation was successful when a large proportion (> 16.6%) of ventricular tissue was directly stimulated by light that was bright enough to induce an action potential in an uncoupled cell. While illumination with blue light never successfully terminated VF, illumination of red light-sensitive ion channels with dense arrays of implanted red light sources resulted in successful defibrillation. Our results suggest that cardiac expression of red light-sensitive ion channels is necessary for the development of effective optogenetics-based defibrillation therapy using LED arrays. ABSTRACT: Optogenetics-based defibrillation has been proposed as a novel and potentially pain-free approach to enable cardiomyocyte-selective defibrillation in humans, but the feasibility of such a therapy remains unknown. This study aimed to (1) assess the feasibility of terminating sustained ventricular fibrillation (VF) via light-induced excitation of opsins expressed throughout the myocardium and (2) identify the ideal (theoretically possible) opsin properties and light source configurations that would maximise therapeutic efficacy. We conducted electrophysiological simulations in an MRI-based human ventricular model with VF induced by rapid pacing; light sensitisation via systemic, cardiac-specific gene transfer of channelrhodopsin-2 (ChR2) was simulated. In addition to the widely used blue light-sensitive ChR2-H134R, we also modelled theoretical ChR2 variants with augmented light sensitivity (ChR2+), red-shifted spectral sensitivity (ChR2-RED) or both (ChR2-RED+). Light sources were modelled as synchronously activating LED arrays (LED radius: 1 mm; optical power: 10 mW mm-2 ; array density: 1.15-4.61 cm-2 ). For each unique optogenetic configuration, defibrillation was attempted with two different optical pulse durations (25 and 500 ms). VF termination was only successful for configurations involving ChR2-RED and ChR2-RED+ (for LED arrays with density ≥ 2.30 cm-2 ), suggesting that opsin spectral sensitivity was the most important determinant of optogenetic defibrillation efficacy. This was due to the deeper penetration of red light in cardiac tissue compared with blue light, which resulted in more widespread light-induced propagating wavefronts. Longer pulse duration and higher LED array density were associated with increased optogenetic defibrillation efficacy. In all cases observed, the defibrillation mechanism was light-induced depolarisation of the excitable gap, which led to block of reentrant wavefronts.

Association of Left Atrial Local Conduction Velocity With Late Gadolinium Enhancement on Cardiac Magnetic Resonance in Patients With Atrial Fibrillation.

BACKGROUND: Prior studies have demonstrated regional left atrial late gadolinium enhancement (LGE) heterogeneity on magnetic resonance imaging. Heterogeneity in regional conduction velocities is a critical substrate for functional reentry. We sought to examine the association between left atrial conduction velocity and LGE in patients with atrial fibrillation. METHODS AND RESULTS: LGE imaging and left atrial activation mapping were performed during sinus rhythm in 22 patients before pulmonary vein isolation. The locations of 1468 electroanatomic map points were registered to the corresponding anatomic sites on 469 axial LGE image planes. The local conduction velocity at each point was calculated using previously established methods. The myocardial wall thickness and image intensity ratio defined as left atrial myocardial LGE signal intensity divided by the mean left atrial blood pool intensity was calculated for each mapping site. The local conduction velocity and image intensity ratio in the left atrium (mean ± SD) were 0.98 ± 0.46 and 0.95 ± 0.26 m/s, respectively. In multivariable regression analysis, clustered by patient, and adjusting for left atrial wall thickness, conduction velocity was associated with the local image intensity ratio (0.20 m/s decrease in conduction velocity per increase in unit image intensity ratio, P<0.001). CONCLUSIONS: In this clinical in vivo study, we demonstrate that left atrial myocardium with increased gadolinium uptake has lower local conduction velocity. Identification of such regions may facilitate the targeting of the substrate for reentrant arrhythmias.

Myocardial Infarct Segmentation From Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology.

Accurate representation of myocardial infarct geometry is crucial to patient-specific computational modeling of the heart in ischemic cardiomyopathy. We have developed a methodology for segmentation of left ventricular (LV) infarct from clinically acquired, two-dimensional (2D), late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) images, for personalized modeling of ventricular electrophysiology. The infarct segmentation was expressed as a continuous min-cut optimization problem, which was solved using its dual formulation, the continuous max-flow (CMF). The optimization objective comprised of a smoothness term, and a data term that quantified the similarity between image intensity histograms of segmented regions and those of a set of training images. A manual segmentation of the LV myocardium was used to initialize and constrain the developed method. The three-dimensional geometry of infarct was reconstructed from its segmentation using an implicit, shape-based interpolation method. The proposed methodology was extensively evaluated using metrics based on geometry, and outcomes of individualized electrophysiological simulations of cardiac dys(function). Several existing LV infarct segmentation approaches were implemented, and compared with the proposed method. Our results demonstrated that the CMF method was more accurate than the existing approaches in reproducing expert manual LV infarct segmentations, and in electrophysiological simulations. The infarct segmentation method we have developed and comprehensively evaluated in this study constitutes an important step in advancing clinical applications of personalized simulations of cardiac electrophysiology.

Virtual electrophysiological study of atrial fibrillation in fibrotic remodeling.

Research has indicated that atrial fibrillation (AF) ablation failure is related to the presence of atrial fibrosis. However it remains unclear whether this information can be successfully used in predicting the optimal ablation targets for AF termination. We aimed to provide a proof-of-concept that patient-specific virtual electrophysiological study that combines i) atrial structure and fibrosis distribution from clinical MRI and ii) modeling of atrial electrophysiology, could be used to predict: (1) how fibrosis distribution determines the locations from which paced beats degrade into AF; (2) the dynamic behavior of persistent AF rotors; and (3) the optimal ablation targets in each patient. Four MRI-based patient-specific models of fibrotic left atria were generated, ranging in fibrosis amount. Virtual electrophysiological studies were performed in these models, and where AF was inducible, the dynamics of AF were used to determine the ablation locations that render AF non-inducible. In 2 of the 4 models patient-specific models AF was induced; in these models the distance between a given pacing location and the closest fibrotic region determined whether AF was inducible from that particular location, with only the mid-range distances resulting in arrhythmia. Phase singularities of persistent rotors were found to move within restricted regions of tissue, which were independent of the pacing location from which AF was induced. Electrophysiological sensitivity analysis demonstrated that these regions changed little with variations in electrophysiological parameters. Patient-specific distribution of fibrosis was thus found to be a critical component of AF initiation and maintenance. When the restricted regions encompassing the meander of the persistent phase singularities were modeled as ablation lesions, AF could no longer be induced. The study demonstrates that a patient-specific modeling approach to identify non-invasively AF ablation targets prior to the clinical procedure is feasible.