Computationally guided personalized targeted ablation of persistent atrial fibrillation

Multi-physics simulations reveal hemodynamic impacts of patient-derived fibrosis-related changes in left atrial tissue mechanics

Stroke is a leading cause of death and disability worldwide. Atrial myopathy, including fibrosis, is associated with an increased risk of ischemic stroke, but the mechanisms underlying this association are poorly understood. Fibrosis modifies myocardial structure, impairing electrical propagation and tissue biomechanics, and creating stagnant flow regions where clots could form. Fibrosis can be mapped non-invasively using late gadolinium enhancement magnetic resonance imaging (LGE-MRI). However, fibrosis maps are not currently incorporated into stroke risk calculations or computational electro-mechano-fluidic models. We present multi-physics simulations of left atrial (LA) myocardial motion and hemodynamics using patient-specific anatomies and fibrotic maps from LGE-MRI. We modify tissue stiffness and active tension generation in fibrotic regions and investigate how these changes affect LA flow for different fibrotic burdens. We find that fibrotic regions and, to a lesser extent, non-fibrotic regions experience reduced myocardial strain, resulting in decreased LA emptying fraction consistent with clinical observations. Both fibrotic tissue stiffening and hypocontractility independently reduce LA function, but together, these two alterations cause more pronounced effects than either one alone. Fibrosis significantly alters flow patterns throughout the atrial chamber, and particularly, the filling and emptying jets of the left atrial appendage (LAA). The effects of fibrosis in LA flow are largely captured by the concomitant changes in LA emptying fraction except inside the LAA, where a multi-factorial behavior is observed. This work illustrates how high-fidelity, multi-physics models can be used to study thrombogenesis mechanisms in patient-specific anatomies, shedding light onto the links between atrial fibrosis and ischemic stroke.

Key points

Left atrial (LA) fibrosis is associated with arrhythmogenesis and increased risk of ischemic stroke; its extent and pattern can be quantified on a patient-specific basis using late gadolinium enhancement magnetic resonance imaging.Current stroke risk prediction tools have limited personalization, and their accuracy could be improved by incorporating patient-specific information like fibrotic maps and hemodynamic patterns.We present the first electro-mechano-fluidic multi-physics computational simulations of LA flow, including fibrosis and anatomies from medical imaging. Mechanical changes in fibrotic tissue impair global LA motion, decreasing LA and left atrial appendage (LAA) emptying fractions, especially in subjects with higher fibrosis burdens. Fibrotic-mediated LA motion impairment alters LA and LAA flow near the endocardium and the whole cavity, ultimately leading to more stagnant blood regions in the LAA.

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

BACKGROUND

In atrial fibrillation (AF) management, understanding left atrial (LA) substrate is crucial. While both electroanatomical mapping (EAM) and late gadolinium enhancement MRI (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

Explore the relationship between LGE regions and unipolar and bipolar-LVAs utilizing multipolar high-density (HD) mapping.

METHODS

20 patients scheduled for AF ablation underwent pre-ablation LGE-MRI. LA segmentation was conducted using a deep learning approach, which subsequently generated a 3D mesh integrating the LGE data. HD-EAM was performed in sinus rhythm for each patient. The EAM map and LGE-MRI mesh were co-registered. LVAs were defined using voltage cut-offs of 0.5mV for bipolar and 2.5mV for unipolar. Correspondence between LGE-areas and LVAs in the LA was analyzed using confusion matrices and performance metrics.

RESULTS

A considerable 87.3% of LGE regions overlapped with unipolar-LVAs, compared to only 16.2% overlap observed with bipolar-LVAs. Across all performance metrics, unipolar-LVAs outperformed bipolar-LVAs in identifying LGE-areas [precision (78.6% vs. 61.1%); sensitivity (87.3% vs. 16.2%); F1 score (81.3% vs. 26.0%); accuracy (74.0% vs. 35.3%)].

CONCLUSION

Our findings demonstrate that unipolar-LVAs highly correlate with LGE regions. These findings support the integration of unipolar mapping alongside bipolar mapping into clinical practice. This would offer a nuanced approach to diagnose and manage atrial fibrillation by revealing critical insights into the complex architecture of the atrial substrate.

Impact of effective refractory period personalization on arrhythmia vulnerability in patient-specific atrial computer models

AIMS

The effective refractory period (ERP) is one of the main electrophysiological properties governing arrhythmia, yet ERP personalization is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. This study evaluates the impact of integrating clinical ERP measurements into personalized in silico models on arrhythmia vulnerability.

METHODS AND RESULTS

Clinical ERP measurements were obtained in seven patients from multiple locations in the atria. Atrial geometries from the electroanatomical mapping system were used to generate personalized anatomical atrial models. The Courtemanche M. et al. cellular model was adjusted to reproduce patient-specific ERP. Four modeling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) ERP distributions. Non-personalized approaches (A and B) were based on literature data, while personalized approaches (C and D) were based on patient measurements. Modeling effects were assessed on arrhythmia vulnerability and tachycardia cycle length, with sensitivity analysis on ERP measurement uncertainty. Mean vulnerability was 3.4 ± 4.0%, 7.7 ± 3.4%, 9.0 ± 5.1%, and 7.0 ± 3.6% for scenarios A-D, respectively. Mean tachycardia cycle length was 167.1 ± 12.6 ms, 158.4 ± 27.5 ms, 265.2 ± 39.9 ms, and 285.9 ± 77.3 ms for scenarios A-D, respectively. Incorporating perturbations to the measured ERP in the range of 2, 5, 10, 20, and 50 ms changed the vulnerability of the model to 5.8 ± 2.7%, 6.1 ± 3.5%, 6.9 ± 3.7%, 5.2 ± 3.5%, and 9.7 ± 10.0%, respectively.

CONCLUSION

Increased ERP dispersion had a greater effect on re-entry dynamics than on vulnerability. Inducibility was higher in personalized scenarios compared with scenarios with uniformly reduced ERP; however, this effect was reversed when incorporating fibrosis informed by low-voltage areas. Effective refractory period measurement uncertainty up to 20 ms slightly influenced vulnerability. Electrophysiological personalization of atrial in silico models appears essential and requires confirmation in larger cohorts.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

Cardiovascular care with digital twin technology in the era of generative artificial intelligence

Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.

Functional substrate in patients with atrial fibrillation is predictive of recurrences after catheter ablation

BACKGROUND

Enhanced characterization of the atrial electrical substrate may lead to better comprehension of atrial fibrillation (AF) pathophysiology.

OBJECTIVE

With the use of high-density substrate mapping, we sought to investigate the occurrence of functional electrophysiologic phenomena in the left atrium and to assess potential association with arrhythmia recurrences after catheter ablation.

METHODS

Sixty-three consecutive patients with AF referred for ablation were enrolled. Analysis of conduction abnormalities relied on 2 acquired left atrial electroanatomic maps (sinus and atrial paced rhythm). We classified conduction abnormalities as fixed (if these were present in both rhythms) or functional rhythm dependent (if unmasked in 1 of the 2 rhythms). Esophagus and aorta locations were recorded to check the correspondence with abnormal conduction sites.

RESULTS

There were 234 conduction abnormalities detected, of which 125 (53.4%) were functional rhythm dependent. The most frequent anatomic site of functional phenomena was the anterior wall, followed by the posterior wall, in sinus rhythm and the pulmonary venous antra in paced rhythm. Sites of functional phenomena in 82.6% of cases corresponded with extracardiac structures, such as sinus of Valsalva of ascending aorta anteriorly and the esophagus posteriorly. Most (88%) areas with functional phenomena had normal bipolar voltage. After pulmonary vein ablation, the number of residual functional phenomena is an independent predictor of AF recurrence (hazard ratio, 2.539 [1.458-4.420]; P = .001) with a risk of recurrences at multivariable Cox analysis.

CONCLUSION

Dual high-density mapping (during sinus and paced rhythms) is able to unmask functional, rhythm-dependent phenomena that are predictive of AF recurrences during follow-up.

2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society, the Asia Pacific Heart Rhythm Society, and the Latin American Heart Rhythm Society.

Influence of genetic mutations to atria vulnerability to atrial fibrillation: An in-silico 3D human atria study

BACKGROUND AND OBJECTIVE

Personalized 3D computer models of atria have been extensively implemented in the last yearsas a tool to facilitate the understanding of the mechanisms underlying different forms of arrhythmia, such as atrial fibrillation (AF). Meanwhile, genetic mutations acting on potassium channel dynamics were demonstrated to induce fibrillatory episodes in asymptomatic patients. This research study aims at assessing the effects and the atrial susceptibility to AF of three gain-of-function mutations - namely, KCNH2 T895M, KCNH2 T436M, and KCNE3-V17M - associated with AF outbreaks, using highly detailed 3D atrial models with realistic wall thickness and heterogenous histological properties.

METHODS

The 3D atrial model was generated by reconstructing segmented anatomical structures from CT scans of an AF patient. Modified versions of the Courtemanche human atrial myocyte model were used to reproduce the electrophysiological activity of the WT and of the three mutant cells. Ectopic foci (EF) were simulated in sixteen locations across the atrial mesh using an S1-S2 protocol with two S2 basic cycle lengths (BCL) and eleven coupling intervals in order to induce arrhythmias.

RESULTS

The three genetic mutations at 3D level reduced the APD90. The KCNE3-V17M mutation provoked the highest shortening (55 % in RA and LA with respect to WT), followed by KCNH2 T895M (14 % in RA and 18 % LA with respect to WT)and KCNH2 T436M (7 % in RA and 9 % LA with respect to WT). The KCNE3-V17M mutation led to arrhythmia in 67 % of the cases simulated and in 94 % of ectopic foci considered, at S2 BCL equal to 100 ms. The KCNH2 T436M and KCNH2 T895M mutations increased the vulnerability to AF in a similar way, leading to arrhythmic episodes in 7 % of the simulated conditions, at S2 BCL set to 160 ms. Overall, 60 % of the arrhythmic events generated arise in the left atrium. Spiral waves, multiple rotors and disordered electrical pattern were elicited in the presence of the KCNE3-V17M mutation, exhibiting an instantaneous mean frequency of 7.6 Hz with a mean standard deviation of 1.12 Hz. The scroll waves induced in the presence of the KCNH2 T436M and KCNH2 T895M mutations showed steadiness and regularity with an instantaneous mean frequencies in the range of 4.9 - 5.1 Hz and a mean standard deviation within 0.19 - 0.53 Hz.

CONCLUSIONS

The pro-arrhythmogenicity of the KCNE3-V17M, KCNH2 T895M and KCNH2 T436M mutations was studied and proved on personalized 3D cardiac models. The three genetic mutations were demonstrated to increase the predisposition of atrial tissue to the formation of AF-susceptible substrate in different ways based on their effects on electrophysiological properties of the atria.

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.

METHODS

Biatrial DTs were generated from late gadolinium enhancement-magnetic resonance images of 37 consecutive PsAF patients, and the fibrotic substrate locations in the DT capable of sustaining reentries were determined. These locations were ablated in DTs by representing a single compound region of ablation with normal power (SSA), and postablation iAT occurrence was determined. At locations of iAT, ablation at the same DT target was repeated, but applying multiple lesions of reduced-strength (MRA) instead of SSA.

RESULTS

Eighty-three locations in the fibrotic substrates of 28 personalized biatrial DTs were capable of sustaining reentries and were thus targeted for SSA ablation. Of these ablations, 45 resulted in iAT. Repeating the ablation at these targets with MRA instead of SSA resulted in the prevention of iAT occurrence at 15 locations (18% reduction in the rate of iAT occurrence).

CONCLUSIONS

Personalized atrial DTs enable preprocedure prediction of iAT occurrence after ablation in the fibrotic substrate. It also suggests MRA could be a potential strategy for preventing postablation AT.

2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society (HRS), the Asia Pacific HRS, and the Latin American HRS.

Guidelines for mechanistic modeling and analysis in cardiovascular research

Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.

Mechanisms of stretch-induced electro-anatomical remodeling and atrial arrhythmogenesis

Atrial fibrillation (AF) is the most common cardiac rhythm disorder, often occurring in the setting of atrial distension and elevated myocardialstretch. While various mechano-electrochemical signal transduction pathways have been linked to AF development and progression, the underlying molecular mechanisms remain poorly understood, hampering AF therapies. In this review, we describe different aspects of stretch-induced electro-anatomical remodeling as seen in animal models and in patients with AF. Specifically, we focus on cellular and molecular mechanisms that are responsible for mechano-electrochemical signal transduction and the development of ectopic beats triggering AF from pulmonary veins, the most common source of paroxysmal AF. Furthermore, we describe structural changes caused by stretch occurring before and shortly after the onset of AF as well as during AF progression, contributing to longstanding forms of AF. We also propose mechanical stretch as a new dimension to the concept "AF begets AF", in addition to underlying diseases. Finally, we discuss the mechanisms of these electro-anatomical alterations in a search for potential therapeutic strategies and the development of novel antiarrhythmic drugs targeted at the components of mechano-electrochemical signal transduction not only in cardiac myocytes, but also in cardiac non-myocyte cells.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

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. Among these, a portion needs to be ablated to render the substrate not inducible for rotors, but the rest (37%) lose rotor-attracting capabilities when another location is ablated. Leveraging digital twin mechanistic insights, we suggest ablation targets that eliminate arrhythmia propensity with minimum lesions while also minimizing the risk of iatrogenic tachycardia and AF recurrence. Our findings provide further evidence regarding the appropriate substrate ablation targets in persistent AF, opening the door for effective strategies to mitigate patients' AF burden.

The beating heart: artificial intelligence for cardiovascular application in the clinic

Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.

In Silico TRials guide optimal stratification of ATrIal FIbrillation patients to Catheter Ablation and pharmacological medicaTION: the i-STRATIFICATION study

AIMS

Patients with persistent atrial fibrillation (AF) experience 50% recurrence despite pulmonary vein isolation (PVI), and no consensus is established for secondary treatments. The aim of our i-STRATIFICATION study is to provide evidence for stratifying patients with AF recurrence after PVI to optimal pharmacological and ablation therapies, through in silico trials.

METHODS AND RESULTS

A cohort of 800 virtual patients, with variability in atrial anatomy, electrophysiology, and tissue structure (low-voltage areas, LVAs), was developed and validated against clinical data from ionic currents to electrocardiogram. Virtual patients presenting AF post-PVI underwent 12 secondary treatments. Sustained AF developed in 522 virtual patients after PVI. Second ablation procedures involving left atrial ablation alone showed 55% efficacy, only succeeding in the small right atria (<60 mL). When additional cavo-tricuspid isthmus ablation was considered, Marshall-PLAN sufficed (66% efficacy) for the small left atria (<90 mL). For the bigger left atria, a more aggressive ablation approach was required, such as anterior mitral line (75% efficacy) or posterior wall isolation plus mitral isthmus ablation (77% efficacy). Virtual patients with LVAs greatly benefited from LVA ablation in the left and right atria (100% efficacy). Conversely, in the absence of LVAs, synergistic ablation and pharmacotherapy could terminate AF. In the absence of ablation, the patient's ionic current substrate modulated the response to antiarrhythmic drugs, being the inward currents critical for optimal stratification to amiodarone or vernakalant.

CONCLUSION

In silico trials identify optimal strategies for AF treatment based on virtual patient characteristics, evidencing the power of human modelling and simulation as a clinical assisting tool.

Digital twins for cardiac electrophysiology: state of the art and future challenges

Cardiac arrhythmias remain a major cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as a promising strategy to realize the vision of precision cardiology. While there is no uniform definition of a digital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides a short overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable a significant precision medicine-based advancement in cardiac arrhythmia management.

A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.

Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology

Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.

Digital twinning of the human ventricular activation sequence to Clinical 12-lead ECGs and magnetic resonance imaging using realistic Purkinje networks for in silico clinical trials

Cardiac in silico clinical trials can virtually assess the safety and efficacy of therapies using human-based modelling and simulation. These technologies can provide mechanistic explanations for clinically observed pathological behaviour. Designing virtual cohorts for in silico trials requires exploiting clinical data to capture the physiological variability in the human population. The clinical characterisation of ventricular activation and the Purkinje network is challenging, especially non-invasively. Our study aims to present a novel digital twinning pipeline that can efficiently generate and integrate Purkinje networks into human multiscale biventricular models based on subject-specific clinical 12-lead electrocardiogram and magnetic resonance recordings. Essential novel features of the pipeline are the human-based Purkinje network generation method, personalisation considering ECG R wave progression as well as QRS morphology, and translation from reduced-order Eikonal models to equivalent biophysically-detailed monodomain ones. We demonstrate ECG simulations in line with clinical data with clinical image-based multiscale models with Purkinje in four control subjects and two hypertrophic cardiomyopathy patients (simulated and clinical QRS complexes with Pearson's correlation coefficients > 0.7). Our methods also considered possible differences in the density of Purkinje myocardial junctions in the Eikonal-based inference as regional conduction velocities. These differences translated into regional coupling effects between Purkinje and myocardial models in the monodomain formulation. In summary, we demonstrate a digital twin pipeline enabling simulations yielding clinically consistent ECGs with clinical CMR image-based biventricular multiscale models, including personalised Purkinje in healthy and cardiac disease conditions.

Computer based method for identification of fibrotic scars from electrograms and local activation times on the epi- and endocardial surfaces of the ventricles

Cardiac fibrosis stands as one of the most critical conditions leading to lethal cardiac arrhythmias. Identifying the precise location of cardiac fibrosis is crucial for planning clinical interventions in patients with various forms of ventricular and atrial arrhythmias. As fibrosis impedes and alters the path of electrical waves, detecting fibrosis in the heart can be achieved through analyzing electrical signals recorded from its surface. In current clinical practices, it has become feasible to record electrical activity from both the endocardial and epicardial surfaces of the heart. This paper presents a computational method for reconstructing 3D fibrosis using unipolar electrograms obtained from both surfaces of the ventricles. The proposed method calculates the percentage of fibrosis in various ventricular segments by analyzing the local activation times and peak-to-peak amplitudes of the electrograms. Initially, the method was tested using simulated data representing idealized fibrosis in a heart segment; subsequently, it was validated in the left ventricle with fibrosis obtained from a patient with nonischemic cardiomyopathy. The method successfully determined the location and extent of fibrosis in 204 segments of the left ventricle model with an average error of 0.0±4.3% (N = 204). Moreover, the method effectively detected fibrotic scars in the mid-myocardial region, a region known to present challenges in accurate detection using electrogram amplitude as the primary criterion.

A transferable in-silico augmented ischemic model for virtual myocardial perfusion imaging and myocardial infarction detection

This paper proposes an innovative approach to generate a generalized myocardial ischemia database by modeling the virtual electrophysiology of the heart and the 12-lead electrocardiography projected by the in-silico model can serve as a ready-to-use database for automatic myocardial infarction/ischemia (MI) localization and classification. Although the virtual heart can be created by an established technique combining the cell model with personalized heart geometry to observe the spatial propagation of depolarization and repolarization waves, we developed a strategy based on the clinical pathophysiology of MI to generate a heterogeneous database with a generic heart while maintaining clinical relevance and reduced computational complexity. First, the virtual heart is simplified into 11 regions that match the types and locations, which can be diagnosed by 12-lead ECG; the major arteries were divided into 3-5 segments from the upstream to the downstream based on the general anatomy. Second, the stenosis or infarction of the major or minor coronary artery branches can cause different perfusion drops and infarct sizes. We simulated the ischemic sites in different branches of the arteries by meandering the infarction location to elaborate on possible ECG representations, which alters the infraction's size and changes the transmembrane potential (TMP) of the myocytes associated with different levels of perfusion drop. A total of 8190 different case combinations of cardiac potentials with ischemia and MI were simulated, and the corresponding ECGs were generated by forward calculations. Finally, we trained and validated our in-silico database with a sparse representation classification (SRC) and tested the transferability of the model on the real-world Physikalisch Technische Bundesanstalt (PTB) database. The overall accuracies for localizing the MI region on the PTB data achieved 0.86, which is only 2% drop compared to that derived from the simulated database (0.88). In summary, we have shown a proof-of-concept for transferring an in-silico model to real-world database to compensate for insufficient data.

2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society, the Asia Pacific Heart Rhythm Society, and the Latin American Heart Rhythm Society .

Digital twins in medicine

Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.

Spatially Conserved Spiral Wave Activity During Human Atrial Fibrillation

BACKGROUND

Atrial fibrillation is the most common cardiac arrhythmia in the world and increases the risk for stroke and morbidity. During atrial fibrillation, the electric activation fronts are no longer coherently propagating through the tissue and, instead, show rotational activity, consistent with spiral wave activation, focal activity, collision, or partial versions of these spatial patterns. An unexplained phenomenon is that although simulations of cardiac models abundantly demonstrate spiral waves, clinical recordings often show only intermittent spiral wave activity.

METHODS

In silico data were generated using simulations in which spiral waves were continuously created and annihilated and in simulations in which a spiral wave was intermittently trapped at a heterogeneity. Clinically, spatio-temporal activation maps were constructed using 60 s recordings from a 64 electrode catheter within the atrium of N=34 patients (n=24 persistent atrial fibrillation). The location of clockwise and counterclockwise rotating spiral waves was quantified and all intervals during which these spiral waves were present were determined. For each interval, the angle of rotation as a function of time was computed and used to determine whether the spiral wave returned in step or changed phase at the start of each interval.

RESULTS

In both simulations, spiral waves did not come back in phase and were out of step." In contrast, spiral waves returned in step in the majority (68%; P=0.05) of patients. Thus, the intermittently observed rotational activity in these patients is due to a temporally and spatially conserved spiral wave and not due to ones that are newly created at the onset of each interval.

CONCLUSIONS

Intermittency of spiral wave activity represents conserved spiral wave activity of long, but interrupted duration or transient spiral activity, in the majority of patients. This finding could have important ramifications for identifying clinically important forms of atrial fibrillation and in guiding treatment.

Human sodium current voltage-dependence at physiological temperature measured by coupling a patch-clamp experiment to a mathematical model

Voltage-gated Na+ channels are crucial to action potential propagation in excitable tissues. Because of the high amplitude and rapid activation of the Na+ current, voltage-clamp measurements are very challenging and are usually performed at room temperature. In this study, we measured Na+ current voltage-dependence in stem cell-derived cardiomyocytes at physiological temperature. While the apparent activation and inactivation curves, measured as the dependence of current amplitude on voltage, fall within the range reported in previous studies, we identified a systematic error in our measurements. This error is caused by the deviation of the membrane potential from the command potential of the amplifier. We demonstrate that it is possible to account for this artifact using computer simulation of the patch-clamp experiment. We obtained surprising results through patch-clamp model optimization: a half-activation of -11.5 mV and a half-inactivation of -87 mV. Although the half-activation deviates from previous research, we demonstrate that this estimate reproduces the conduction velocity dependence on extracellular potassium concentration. KEY POINTS: Voltage-gated Na+ currents play a crucial role in excitable tissues including neurons, cardiac and skeletal muscle. Measurement of Na+ current is challenging because of its high amplitude and rapid kinetics, especially at physiological temperature. We have used the patch-clamp technique to measure human Na+ current voltage-dependence in human induced pluripotent stem cell-derived cardiomyocytes. The patch-clamp data were processed by optimization of the model accounting for voltage-clamp experiment artifacts, revealing a large difference between apparent parameters of Na+ current and the results of the optimization. We conclude that actual Na+ current activation is extremely depolarized in comparison to previous studies. The new Na+ current model provides a better understanding of action potential propagation; we demonstrate that it explains propagation in hyperkalaemic conditions.

The role of computational methods in cardiovascular medicine: a narrative review

Background and Objective

Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data with physical knowledge of the processes underlying the heart function. These models have been increasingly employed in clinical practice to understand pathological mechanisms and their progression, design medical devices, support clinicians in improving therapies. Hinging upon a long-year experience in cardiovascular modeling, we have recently constructed a computational multi-physics and multi-scale integrated model of the heart for the investigation of its physiological function, the analysis of pathological conditions, and to support clinicians in both diagnosis and treatment planning. This narrative review aims to systematically discuss the role that such model had in addressing specific clinical questions, and how further impact of computational models on clinical practice are envisaged.

Methods

We developed computational models of the physical processes encompassed by the heart function (electrophysiology, electrical activation, force generation, mechanics, blood flow dynamics, valve dynamics, myocardial perfusion) and of their inherently strong coupling. To solve the equations of such models, we devised advanced numerical methods, implemented in a flexible and highly efficient software library. We also developed computational procedures for clinical data post-processing-like the reconstruction of the heart geometry and motion from diagnostic images-and for their integration into computational models.

Key Content and Findings

Our integrated computational model of the heart function provides non-invasive measures of indicators characterizing the heart function and dysfunctions, and sheds light on its underlying processes and their coupling. Moreover, thanks to the close collaboration with several clinical partners, we addressed specific clinical questions on pathological conditions, such as arrhythmias, ventricular dyssynchrony, hypertrophic cardiomyopathy, degeneration of prosthetic valves, and the way coronavirus disease 2019 (COVID-19) infection may affect the cardiac function. In multiple cases, we were also able to provide quantitative indications for treatment.

Conclusions

Computational models provide a quantitative and detailed tool to support clinicians in patient care, which can enhance the assessment of cardiac diseases, the prediction of the development of pathological conditions, and the planning of treatments and follow-up tests.

Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity

Large-cohort studies using cardiovascular imaging and diagnostic datasets have assessed cardiac anatomy, function, and outcomes, but typically do not reveal underlying biological mechanisms. Cardiac digital twins (CDTs) provide personalized physics- and physiology-constrained in-silico representations, enabling inference of multi-scale properties tied to these mechanisms. We constructed 3464 anatomically-accurate CDTs using cardiac magnetic resonance images from UK biobank and personalised their myocardial conduction velocities (CVs) from electrocardiograms (ECG), through an automated framework. We found well-known sex-specific differences in QRS duration were fully explained by myocardial anatomy, as CV remained consistent across sexes. Conversely, significant associations of CV with ageing and increased BMI suggest myocardial tissue remodelling. Novel associations were observed with left ventricular ejection fraction and mental-health phenotypes, through a phenome-wide association study, and CV was also linked with adverse clinical outcomes. Our study highlights the utility of population-based CDTs in assessing intersubject variability and uncovering strong links with mental health.

A Semi-automatic Pipeline for Generation of Large Cohorts of Four-Chamber Heart Meshes

Computational models for cardiac electro-mechanics have been increasingly used to further understand heart function. Small cohort and single patient computational studies provide useful insight into cardiac pathophysiology and response to therapy. However, these smaller studies have limited capability to capture the high level of anatomical variability seen in cardiology patients. Larger cohort studies are, on the other hand, more representative of the study population, but building several patient-specific anatomical meshes can be time-consuming and requires access to larger datasets of imaging data, image processing software to label anatomical structures and tools to create high fidelity anatomical meshes. Limited access to these tools and data might limit advances in this area of research. In this chapter, we present our semi-automatic pipeline to build patient-specific four-chamber heart meshes from CT imaging datasets, including ventricular myofibers and a set of universal ventricular and atrial coordinates. This pipeline was applied to CT images from both heart failure patients and healthy controls to generate cohorts of tetrahedral meshes suitable for electro-mechanics simulations. Both cohorts were made publicly available in order to promote computational studies employing large virtual cohorts.

Computational Modeling and Simulation of the Fibrotic Human Atria

Patient-specific modeling of atrial electrical activity enables the execution of simulations that can provide mechanistic insights and provide novel solutions to vexing clinical problems. The geometry and fibrotic remodeling of the heart can be reconstructed from clinical-grade medical scans and used to inform personalized models with detail incorporated at the cell- and tissue-scale to represent changes in image-identified diseased regions. Here, we provide a rubric for the reconstruction of realistic atrial models from pre-segmented 3D renderings of the left atrium with fibrotic tissue regions delineated, which are the output from clinical-grade systems for quantifying fibrosis. We then provide a roadmap for using those models to carry out patient-specific characterization of the fibrotic substrate in terms of its potential to harbor reentrant drivers via cardiac electrophysiology simulations.

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. In precision cardiology, the concepts and initial applications of heart digital twins have slowly been gaining popularity and the trust of the clinical community. In this article, we review the advancement in heart digital twinning and its initial translation to the management of heart rhythm disorders.

Introduction to Computational Biomedicine

The domain of computational biomedicine is a new and burgeoning one. Its areas of concern cover all scales of human biology, physiology, and pathology, commonly referred to as medicine, from the genomic to the whole human and beyond, including epidemiology and population health. Computational biomedicine aims to provide high-fidelity descriptions and predictions of the behavior of biomedical systems of both fundamental scientific and clinical importance. Digital twins and virtual humans aim to reproduce the extremely accurate duplicate of real-world human beings in cyberspace, which can be used to make highly accurate predictions that take complicated conditions into account. When that can be done reliably enough for the predictions to be actionable, such an approach will make an impact in the pharmaceutical industry by reducing or even replacing the extremely laboratory-intensive preclinical process of making and testing compounds in laboratories, and in clinical applications by assisting clinicians to make diagnostic and treatment decisions.

Constructing bilayer and volumetric atrial models at scale

To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale (https://github.com/pcmlab/atrialmtk).

Multi-modal characterization of the left atrium by a fully automated integration of pre-procedural cardiac imaging and electro-anatomical mapping

Background

The combination of information obtained from pre-procedural cardiac imaging and electro-anatomical mapping (EAM) can potentially help to locate new ablation targets. In this study we developed and evaluated a fully automated technique to align left atrial (LA) anatomies obtained from CT- and MRI-scans with LA anatomies obtained from EAM.

Methods

Twenty-one patients scheduled for a pulmonary vein (PV) isolation with a pre-procedural MRI were enrolled. Additionally, a recent computed tomography (CT) scan was available in 12 patients. LA anatomies were segmented from MRI-scans using ADAS-AF (Galgo Medical, Barcelona) and from the CT-scans using Slicer3D. MRI and CT anatomies were aligned with the EAM anatomy using an iterative closest plane-to-plane algorithm. Initially, the algorithm included the PVs, LA appendage and mitral valve anulus as they are the most distinctive landmarks. Subsequently, the algorithm was applied again, excluding these structures, with only three iterative steps to refine the alignment of the true LA surface. The result of the alignments was quantified by the Euclidian distance between the aligned anatomies after excluding PVs, LA appendage and mitral anulus.

Results

Our algorithm successfully aligned 20/21 MRI anatomies and 11/12 CT anatomies with the corresponding EAM anatomies. The average median residual distances were 1.9 ± 0.6 mm and 2.5 ± 0.8 mm for MRI and CT anatomies respectively. The average LA surface with a residual distance less than 5.00 mm was 89 ± 9% and 89 ± 10% for MRI and CT anatomies respectively.

Conclusion

An iterative closest plane-to-plane algorithm is a reliable method to automatically align pre-procedural cardiac images with anatomies acquired during ablation procedures.

Impact of noise on the instability of spiral waves in stochastic 2D mathematical models of human atrial fibrillation

Sustained spiral waves, also known as rotors, are pivotal mechanisms in persistent atrial fibrillation (AF). Stochasticity is inevitable in nonlinear biological systems such as the heart; however, it is unclear how noise affects the instability of spiral waves in human AF. This study presents a stochastic two-dimensional mathematical model of human AF and explores how Gaussian white noise affects the instability of spiral waves. In homogeneous tissue models, Gaussian white noise may lead to spiral-wave meandering and wavefront break-up. As the noise intensity increases, the spatial dispersion of phase singularity (PS) points increases. This finding indicates the potential AF-protective effects of cardiac system stochasticity by destabilizing the rotors. By contrast, Gaussian white noise is unlikely to affect the spiral-wave instability in the presence of localized scar or fibrosis regions. The PS points are located at the boundary or inside the scar/fibrosis regions. Localized scar or fibrosis may play a pivotal role in stabilizing spiral waves regardless of the presence of noise. This study suggests that fibrosis and scars are essential for stabilizing the rotors in stochastic mathematical models of AF. Further patient-derived realistic modeling studies are required to confirm the role of scar/fibrosis in AF pathophysiology.

Integrative human atrial modelling unravels interactive protein kinase A and Ca2+/calmodulin-dependent protein kinase II signalling as key determinants of atrial arrhythmogenesis

AIMS

Atrial fibrillation (AF), the most prevalent clinical arrhythmia, is associated with atrial remodelling manifesting as acute and chronic alterations in expression, function, and regulation of atrial electrophysiological and Ca2+-handling processes. These AF-induced modifications crosstalk and propagate across spatial scales creating a complex pathophysiological network, which renders AF resistant to existing pharmacotherapies that predominantly target transmembrane ion channels. Developing innovative therapeutic strategies requires a systems approach to disentangle quantitatively the pro-arrhythmic contributions of individual AF-induced alterations.

METHODS AND RESULTS

Here, we built a novel computational framework for simulating electrophysiology and Ca2+-handling in human atrial cardiomyocytes and tissues, and their regulation by key upstream signalling pathways [i.e. protein kinase A (PKA), and Ca2+/calmodulin-dependent protein kinase II (CaMKII)] involved in AF-pathogenesis. Populations of atrial cardiomyocyte models were constructed to determine the influence of subcellular ionic processes, signalling components, and regulatory networks on atrial arrhythmogenesis. Our results reveal a novel synergistic crosstalk between PKA and CaMKII that promotes atrial cardiomyocyte electrical instability and arrhythmogenic triggered activity. Simulations of heterogeneous tissue demonstrate that this cellular triggered activity is further amplified by CaMKII- and PKA-dependent alterations of tissue properties, further exacerbating atrial arrhythmogenesis.

CONCLUSIONS

Our analysis reveals potential mechanisms by which the stress-associated adaptive changes turn into maladaptive pro-arrhythmic triggers at the cellular and tissue levels and identifies potential anti-AF targets. Collectively, our integrative approach is powerful and instrumental to assemble and reconcile existing knowledge into a systems network for identifying novel anti-AF targets and innovative approaches moving beyond the traditional ion channel-based strategy.

Effects of hydroxychloroquine on atrial electrophysiology in in silico wild-type and PITX2+/- atrial cardiomyocytes

BACKGROUND

Hydroxychloroquine (HCQ) is commonly used in the treatment of autoimmune diseases and increases the risk of QT interval prolongation. However, it is unclear how HCQ affects atrial electrophysiology and the risk of atrial fibrillation (AF).

METHODS

We quantitatively examined the potential atrial arrhythmogenic effects of HCQ on AF using a computational model of human atrial cardiomyocytes. We measured atrial electrophysiological markers after systematically varying HCQ concentrations.

RESULTS

The HCQ concentrations were positively correlated with the action potential duration (APD), resting membrane potential, refractory period, APD alternans threshold, and calcium transient alternans threshold (p < 0.05). By contrast, HCQ concentrations were inversely correlated with the maximum upstroke velocity and calcium transient amplitude (p < 0.05). When the therapeutic concentration (Cmax) of HCQ was applied, HCQ increased APD90 by 1.4% in normal sinus rhythm, 1.8% in wild-type AF, and 2.6% in paired-like homeodomain transcription factor 2 (PITX2)+/- AF, but did not affect the alternans thresholds. The overall in silico results suggest no significant atrial arrhythmogenic effects of HCQ at Cmax, instead implying a potential antiarrhythmic role of low-dose HCQ in AF. However, at an HCQ concentration of fourfold Cmax, a rapid pacing rate of 4 Hz induced prominent APD alternans, particularly in the PITX2+/- AF model.

CONCLUSION

Our in silico analysis suggests a potential antiarrhythmic role of low-dose HCQ in AF. Concomitant PITX2 mutations and high-dose HCQ treatments may increase the risk of AF, and this potential genotype/dose-dependent arrhythmogenic effect of HCQ should be investigated further.

Comparing Inducibility of Re-Entrant Arrhythmia in Patient-Specific Computational Models to Clinical Atrial Fibrillation Phenotypes

BACKGROUND

Computational models of fibrosis-mediated, re-entrant left atrial (LA) arrhythmia can identify possible substrate for persistent atrial fibrillation (AF) ablation. Contemporary models use a 1-size-fits-all approach to represent electrophysiological properties, limiting agreement between simulations and patient outcomes.

OBJECTIVES

The goal of this study was to test the hypothesis that conduction velocity (ϴ) modulation in persistent AF models can improve simulation agreement with clinical arrhythmias.

METHODS

Patients with persistent AF (n = 37) underwent ablation and were followed up for ≥2 years to determine post-ablation outcomes: AF, atrial flutter (AFL), or no recurrence. Patient-specific LA models (n = 74) were constructed using pre-ablation and ≥90 days' post-ablation magnetic resonance imaging data. Simulated pacing gauged in silico arrhythmia inducibility due to AF-like rotors or AFL-like macro re-entrant tachycardias. A physiologically plausible range of ϴ values (±10 or 20% vs. baseline) was tested, and model/clinical agreement was assessed.

RESULTS

Fifteen (41%) patients had a recurrence with AF and 6 (16%) with AFL. Arrhythmia was induced in 1,078 of 5,550 simulations. Using baseline ϴ, model/clinical agreement was 46% (34 of 74 models), improving to 65% (48 of 74) when any possible ϴ value was used (McNemar's test, P = 0.014). ϴ modulation improved model/clinical agreement in both pre-ablation and post-ablation models. Pre-ablation model/clinical agreement was significantly greater for patients with extensive LA fibrosis (>17.2%) and an elevated body mass index (>32.0 kg/m2).

CONCLUSIONS

Simulations in persistent AF models show a 41% relative improvement in model/clinical agreement when ϴ is modulated. Patient-specific calibration of ϴ values could improve model/clinical agreement and model usefulness, especially in patients with higher body mass index or LA fibrosis burden. This could ultimately facilitate better personalized modeling, with immediate clinical implications.

The simplified Kirchhoff network model (SKNM): a cell-based reaction-diffusion model of excitable tissue

Cell-based models of excitable tissues offer the advantage of cell-level precision, which cannot be achieved using traditional homogenized electrophysiological models. However, this enhanced accuracy comes at the cost of increased computational demands, necessitating the development of efficient cell-based models. The widely-accepted bidomain model serves as the standard in computational cardiac electrophysiology, and under certain anisotropy ratio conditions, it is well known that it can be reduced to the simpler monodomain model. Recently, the Kirchhoff Network Model (KNM) was developed as a cell-based counterpart to the bidomain model. In this paper, we aim to demonstrate that KNM can be simplified using the same steps employed to derive the monodomain model from the bidomain model. We present the cell-based Simplified Kirchhoff Network Model (SKNM), which produces results closely aligned with those of KNM while requiring significantly less computational resources.

The age of computational cardiology and future of long-term ablation target prediction for ventricular tachycardia

Ventricular arrhythmias, particularly ventricular tachycardia, are ubiquitously linked to 300,000 deaths annually. However, the current interventional procedure-the cardiac ablation-predict only short-term responses to treatment as the heart constantly remodels itself post-arrhythmia. To assist in the design of computational methods which focuses on long-term arrhythmia prediction, this review postulates three interdependent prospectives. The main objective is to propose computational methods for predicting long-term heart response to interventions in ventricular tachycardia Following a general discussion on the importance of devising simulations predicting long-term heart response to interventions, each of the following is discussed: (i) application of "metabolic sink theory" to elucidate the "re-entry" mechanism of ventricular tachycardia; (ii) application of "growth laws" to explain "mechanical load" translation in ventricular tachycardia; (iii) derivation of partial differential equations (PDE) to establish a pipeline to predict long-term clinical outcomes in ventricular tachycardia.

Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation

Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice.

AugmentA: Patient-specific augmented atrial model generation tool

Digital twins of patients' hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.

What determines the optimal pharmacological treatment of atrial fibrillation? Insights from in silico trials in 800 virtual atria

The best pharmacological treatment for each atrial fibrillation (AF) patient is unclear. We aim to exploit AF simulations in 800 virtual atria to identify key patient characteristics that guide the optimal selection of anti-arrhythmic drugs. The virtual cohort considered variability in electrophysiology and low voltage areas (LVA) and was developed and validated against experimental and clinical data from ionic currents to ECG. AF sustained in 494 (62%) atria, with large inward rectifier K+ current (IK1 ) and Na+ /K+ pump (INaK ) densities (IK1 0.11 ± 0.03 vs. 0.07 ± 0.03 S mF-1 ; INaK 0.68 ± 0.15 vs. 0.38 ± 26 S mF-1 ; sustained vs. un-sustained AF). In severely remodelled left atrium, with LVA extensions of more than 40% in the posterior wall, higher IK1 (median density 0.12 ± 0.02 S mF-1 ) was required for AF maintenance, and rotors localized in healthy right atrium. For lower LVA extensions, rotors could also anchor to LVA, in atria presenting short refractoriness (median L-type Ca2+ current, ICaL , density 0.08 ± 0.03 S mF-1 ). This atrial refractoriness, modulated by ICaL and fast Na+ current (INa ), determined pharmacological treatment success for both small and large LVA. Vernakalant was effective in atria presenting long refractoriness (median ICaL density 0.13 ± 0.05 S mF-1 ). For short refractoriness, atria with high INa (median density 8.92 ± 2.59 S mF-1 ) responded more favourably to amiodarone than flecainide, and the opposite was found in atria with low INa (median density 5.33 ± 1.41 S mF-1 ). In silico drug trials in 800 human atria identify inward currents as critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics. KEY POINTS: Atrial fibrillation (AF) maintenance is facilitated by small L-type Ca2+ current (ICaL ) and large inward rectifier K+ current (IK1 ) and Na+ /K+ pump. In severely remodelled left atrium, with low voltage areas (LVA) covering more than 40% of the posterior wall, sustained AF requires higher IK1 and rotors localize in healthy right atrium. For lower LVA extensions, rotors can also anchor to LVA, if the atria present short refractoriness (low ICaL ) Vernakalant is effective in atria presenting long refractoriness (high ICaL ). For short refractoriness, atria with fast Na+ current (INa ) up-regulation respond more favourably to amiodarone than flecainide, and the opposite is found in atria with low INa . The inward currents (ICaL and INa ) are critical for optimal stratification of AF patient to pharmacological treatment and, together with the left atrial LVA extension, for accurately phenotyping AF dynamics.

Twenty-five years of research in cardiac imaging in electrophysiology procedures for atrial and ventricular arrhythmias

Catheter ablation is nowadays considered the treatment of choice for numerous cardiac arrhythmias in different clinical scenarios. Fluoroscopy has traditionally been the primary imaging modality for catheter ablation, providing real-time visualization of catheter navigation. However, its limitations, such as inadequate soft tissue visualization and exposure to ionizing radiation, have prompted the integration of alternative imaging modalities. Over the years, advancements in imaging techniques have played a pivotal role in enhancing the safety, efficacy, and efficiency of catheter ablation procedures. This manuscript aims to explore the utility of imaging, including electroanatomical mapping, cardiac computed tomography, echocardiography, cardiac magnetic resonance, and nuclear cardiology exams, in helping electrophysiology procedures. These techniques enable accurate anatomical guidance, identification of critical structures and substrates, and real-time monitoring of complications, ultimately enhancing procedural safety and success rates. Incorporating advanced imaging technologies into routine clinical practice has the potential to further improve clinical outcomes of catheter ablation procedures and pave the way for more personalized and precise ablation therapies in the future.

Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models

Background Postablation arrhythmia recurrence occurs in ~40% of patients with persistent atrial fibrillation. Fibrotic remodeling exacerbates arrhythmic activity in persistent atrial fibrillation and can play a key role in reentrant arrhythmia, but emergent interaction between nonconductive ablation-induced scar and native fibrosis (ie, residual fibrosis) is poorly understood. Methods and Results We conducted computational simulations in pre- and postablation left atrial models reconstructed from late gadolinium enhanced magnetic resonance imaging scans to test the hypothesis that ablation in patients with persistent atrial fibrillation creates new substrate conducive to recurrent arrhythmia mediated by anchored reentry. We trained a random forest machine learning classifier to accurately pinpoint specific nonconductive tissue regions (ie, areas of ablation-delivered scar or vein/valve boundaries) with the capacity to serve as substrate for anchored reentry-driven recurrent arrhythmia (area under the curve: 0.91±0.03). Our analysis suggests there is a distinctive nonconductive tissue pattern prone to serving as arrhythmogenic substrate in postablation models, defined by a specific size and proximity to residual fibrosis. Conclusions Overall, this suggests persistent atrial fibrillation ablation transforms substrate that favors functional reentry (ie, rotors meandering in excitable tissue) into an arrhythmogenic milieu more conducive to anchored reentry. Our work also indicates that explainable machine learning and computational simulations can be combined to effectively probe mechanisms of recurrent arrhythmia.

Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study

This work presents an open-source software pipeline to create patient-specific left atrial models with fibre orientations and a fibrDEFAULTosis map, suitable for electrophysiology simulations, and quantifies the intra and inter observer reproducibility of the model creation. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter and intra-operator variability. Each output model consisted of: (1) a labelled surface mesh open at the pulmonary veins and mitral valve, (2) fibre orientations mapped from a diffusion tensor MRI (DTMRI) human atlas, (3) fibrosis map extracted from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. Reproducibility in our pipeline was evaluated by comparing agreement in shape of the output meshes, fibrosis distribution in the left atrial body, and fibre orientations. Reproducibility in simulations outputs was evaluated in the LAT maps by comparing the total activation times, and the mean conduction velocity (CV). PS maps were compared with the structural similarity index measure (SSIM). The users processed in total 60 cases for inter and 40 cases for intra-operator variability. Our workflow allows a single model to be created in 16.72 ± 12.25 min. Similarity was measured with shape, percentage of fibres oriented in the same direction, and intra-class correlation coefficient (ICC) for the fibrosis calculation. Shape differed noticeably only with users' selection of the mitral valve and the length of the pulmonary veins from the ostia to the distal end; fibrosis agreement was high, with ICC of 0.909 (inter) and 0.999 (intra); fibre orientation agreement was high with 60.63% (inter) and 71.77% (intra). The LAT showed good agreement, where the median ± IQR of the absolute difference of the total activation times was 2.02 ± 2.45 ms for inter, and 1.37 ± 2.45 ms for intra. Also, the average ± sd of the mean CV difference was -0.00404 ± 0.0155 m/s for inter, and 0.0021 ± 0.0115 m/s for intra. Finally, the PS maps showed a moderately good agreement in SSIM for inter and intra, where the mean ± sd SSIM for inter and intra were 0.648 ± 0.21 and 0.608 ± 0.15, respectively. Although we found notable differences in the models, as a consequence of user input, our tests show that the uncertainty caused by both inter and intra-operator variability is comparable with uncertainty due to estimated fibres, and image resolution accuracy of segmentation tools.

Long-term changes in heart rate and electrical remodeling contribute to alternans formation in heart failure: a patient-specific in silico study

Individuals with chronic heart failure (CHF) have an increased risk of ventricular arrhythmias, which has been linked to pathological cellular remodeling and may also be mediated by changes in heart rate. Heart rate typically fluctuates on a timescale ranging from seconds to hours, termed heart rate variability (HRV). This variability is reduced in CHF, and this HRV reduction is associated with a greater risk for arrhythmias. Furthermore, variations in heart rate influence the formation of proarrhythmic alternans, a beat-to-beat alternation in the action potential duration (APD), or intracellular calcium (Ca). In this study, we investigate how long-term changes in heart rate and electrical remodeling associated with CHF influence alternans formation. We measure key statistical properties of the RR-interval sequences from ECGs of individuals with normal sinus rhythm (NSR) and CHF. Patient-specific RR-interval sequences and synthetic sequences (randomly generated to mimicking these statistical properties) are used as the pacing protocol for a discrete time-coupled map model that governs APD and intracellular Ca handling of a single cardiac myocyte, modified to account for pathological electrical remodeling in CHF. Patient-specific simulations show that beat-to-beat differences in APD vary temporally in both populations, with alternans formation more prevalent in CHF. Parameter studies using synthetic sequences demonstrate that increasing the autocorrelation time or mean RR-interval reduces APD alternations, whereas increasing the RR-interval standard deviation leads to higher alternans magnitudes. Importantly, we find that although both the CHF-associated changes in heart rate and electrical remodeling influence alternans formation, variations in heart rate may be more influential.NEW & NOTEWORTHY Using patient-specific data, we show that both the changes in heart rate and electrical remodeling associated with chronic heart failure influence the formation of proarrhythmic alternans in the heart.

Personalized biomechanical insights in atrial fibrillation: opportunities & challenges

INTRODUCTION

Atrial fibrillation (AF) is an increasingly prevalent and significant worldwide health problem. Manifested as an irregular atrial electrophysiological activation, it is associated with many serious health complications. AF affects the biomechanical function of the heart as contraction follows the electrical activation, subsequently leading to reduced blood flow. The underlying mechanisms behind AF are not fully understood, but it is known that AF is highly correlated with the presence of atrial fibrosis, and with a manifold increase in risk of stroke.

AREAS COVERED

In this review, we focus on biomechanical aspects in atrial fibrillation, current and emerging use of clinical images, and personalized computational models. We also discuss how these can be used to provide patient-specific care.

EXPERT OPINION

Understanding the connection betweenatrial fibrillation and atrial remodeling might lead to valuable understanding of stroke and heart failure pathophysiology. Established and emerging imaging modalities can bring us closer to this understanding, especially with continued advancements in processing accuracy, reproducibility, and clinical relevance of the associated technologies. Computational models of cardiac electromechanics can be used to glean additional insights on the roles of AF and remodeling in heart function.