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Activation maps, following the same convention as Fig. 4, highlighting two cases in which the same pacing sequence applied in the same model led to the initiation of AF driven by an RD in the same atrial region, regardless of the variability in APD/CV. (a) RDs located in region 1 (left pulmonary veins) of P02 model. (b) RDs located in region 5 (inferior right atrium) of P04 model.
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Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, causing morbidity and mortality in millions worldwide. The atria of patients with persistent AF (PsAF) are characterized by the presence of extensive and distributed atrial fibrosis, which facilitates the formation of persistent reentrant drivers (RDs, i.e., spiral waves), wh...
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... observation was reflective of a common trend in the subset of cases where APD/CV vari- ability had only minor effects on RD localization (35%-79% depending on the EP variant): despite the fact that RD trajecto- ries overlapped considerably, there were consistent differences in the peripheral activation sequence outside the AF driver region. Figure 5 highlights two cases in which the same stimulus induced AF under all five electrophysiological conditions tested (EP avg , APD 610% , and CV 610% ). In the first example [ Fig. 5(a)], all five panels show AF episodes driven by an RD located in the posterior LA, near the left inferior pulmonary ...
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... despite the fact that RD trajecto- ries overlapped considerably, there were consistent differences in the peripheral activation sequence outside the AF driver region. Figure 5 highlights two cases in which the same stimulus induced AF under all five electrophysiological conditions tested (EP avg , APD 610% , and CV 610% ). In the first example [ Fig. 5(a)], all five panels show AF episodes driven by an RD located in the posterior LA, near the left inferior pulmonary ...
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... (2017) vein . Similar to cases discussed above, differences between activation sequences distal to RD locations in Figs. 5(a) and 5(b) show aspects of organ-scale excitation distal to the RD, and in these cases, they were affected by EP parameter modulation [e.g., transient reentry inferior to left inferior pulmonary vein in the APD þ10% panel of Fig. 5(a); regions of block near RA appendage in the APD þ10% panel of Fig. 5(b); heterogeneous excitation patterns, including conduc- tion block and transient reentry, in APD À10% and CV À10% panels of Fig. 5(b)]. Moreover, in these cases, we observed examples where RD locations were the same as in simulations with EP avg , but RD chirality was ...
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... sequences distal to RD locations in Figs. 5(a) and 5(b) show aspects of organ-scale excitation distal to the RD, and in these cases, they were affected by EP parameter modulation [e.g., transient reentry inferior to left inferior pulmonary vein in the APD þ10% panel of Fig. 5(a); regions of block near RA appendage in the APD þ10% panel of Fig. 5(b); heterogeneous excitation patterns, including conduc- tion block and transient reentry, in APD À10% and CV À10% panels of Fig. 5(b)]. Moreover, in these cases, we observed examples where RD locations were the same as in simulations with EP avg , but RD chirality was reversed [e.g., APD þ10% in Fig. 5(a); APD À10% , CV þ10% , and CV ...
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... cases, they were affected by EP parameter modulation [e.g., transient reentry inferior to left inferior pulmonary vein in the APD þ10% panel of Fig. 5(a); regions of block near RA appendage in the APD þ10% panel of Fig. 5(b); heterogeneous excitation patterns, including conduc- tion block and transient reentry, in APD À10% and CV À10% panels of Fig. 5(b)]. Moreover, in these cases, we observed examples where RD locations were the same as in simulations with EP avg , but RD chirality was reversed [e.g., APD þ10% in Fig. 5(a); APD À10% , CV þ10% , and CV À10% in Fig. ...
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... near RA appendage in the APD þ10% panel of Fig. 5(b); heterogeneous excitation patterns, including conduc- tion block and transient reentry, in APD À10% and CV À10% panels of Fig. 5(b)]. Moreover, in these cases, we observed examples where RD locations were the same as in simulations with EP avg , but RD chirality was reversed [e.g., APD þ10% in Fig. 5(a); APD À10% , CV þ10% , and CV À10% in Fig. ...
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... 5(b); heterogeneous excitation patterns, including conduc- tion block and transient reentry, in APD À10% and CV À10% panels of Fig. 5(b)]. Moreover, in these cases, we observed examples where RD locations were the same as in simulations with EP avg , but RD chirality was reversed [e.g., APD þ10% in Fig. 5(a); APD À10% , CV þ10% , and CV À10% in Fig. ...
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Citations
... One argument for using RTT as a surrogate instead of personalized CV restitution and ERP values is that the latter are rarely available in clinical settings. While most in silico studies do not personalize these variables but rather rely on literature references, some clinical and in silico studies have explored the effect of personalized CV restitution and ERP values on reentry patterns [48][49][50][51]. The DREAM in contrast can incorporate personalized CV restitution and ERP values extracted from patient measurements when available by adjusting parameters in the embedded ionic model and the COHERENCE() function. ...
Background: Computer models for simulating cardiac electrophysiology are valuable tools for research and clinical applications. Traditional reaction-diffusion (RD) models used for these purposes are computationally expensive. While eikonal models offer a faster alternative, they are not well-suited to study cardiac arrhythmias driven by reentrant activity. The present work extends the diffusion-reaction eikonal alternant model (DREAM), incorporating conduction velocity (CV) restitution for simulating complex cardiac arrhythmias. Methods: The DREAM modifies the fast iterative method to model cyclical behavior, dynamic boundary conditions, and frequency-dependent anisotropic CV. Additionally, the model alternates with an approximated RD model, using a detailed ionic model for the reaction term and a triple-Gaussian to approximate the diffusion term. The DREAM and monodomain models were compared, simulating reentries in 2D manifolds with different resolutions. Results: The DREAM produced similar results across all resolutions, while experiments with the monodomain model failed at lower resolutions. CV restitution curves obtained using the DREAM closely approximated those produced by the monodomain simulations. Reentry in 2D slabs yielded similar results in vulnerable window and mean reentry duration for low CV in both models. In the left atrium, most inducing points identified by the DREAM were also present in the high-resolution monodomain model. DREAM's reentry simulations on meshes with an average edge length of 1600$\mu$m were 40x faster than monodomain simulations at 200$\mu$m. Conclusion: This work establishes the mathematical foundation for using the accelerated DREAM simulation method for cardiac electrophysiology. Cardiac research applications are enabled by a publicly available implementation in the openCARP simulator.
... 81,82 Parameter sensitivity: existing models depend on numerous parameters and small variations in these parameters can lead to significant differences in model predictions. 79,83 Accurate parameterisation can be challenging. Further details on available parametrisation strategies have been discussed at length in the next section (Approaches to Model Calibration), which addresses model calibration techniques. ...
... 19,134 For example, Deng et al. found that simulated driver locations changed when action potential duration and CV were changed within a small range of ± 10%. 83 As an extension to this, Macheret et al. showed that persistent AF simulations better matched clinical data when different conductivity values were simulated. 135 This broadly demonstrates both the variability of these metrics between and within patients, and the importance of these metrics on personalised model predictions, and yet most studies do not include calibration to EP measurements. ...
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.
... Multiple studies have observed increased fibrosis in AF patients [9,10]. Fibrosis can lead to localized slowing of the excitation wavefront, functional conduction block, wavefront breakup, and potentially the generation of transient or sustained re-entrant circuits [4,[11][12][13]; indeed, multiple studies have demonstrated that the specific patterns of arrhythmia can strongly depend on individual geometry and fibrosis distribution [6,14,15]. Thus, fibrosis has been comprehensively suggested to play a large part in the arrhythmia substrate associated with AF. from various factors, including cellular spontaneous calcium release events (SCREs). SCRE can result in a whole-cell spontaneous calcium transient that activates the sodium-calcium exchanger (NCX), causing an inward current that can lead to a delayed after depolarization (DAD). ...
Fibrosis has been mechanistically linked to arrhythmogenesis in multiple cardiovascular conditions, including atrial fibrillation (AF). Previous studies have demonstrated that fibrosis can create functional barriers to conduction which may promote excitation wavebreak and the generation of re-entry, while also acting to pin re-entrant excitation in stable rotors during AF. However, few studies have investigated the role of fibrosis in the generation of AF triggers in detail. We apply our in-house computational framework to study the impact of fibrosis on the generation of AF triggers and trigger–substrate interactions in two- and three-dimensional atrial tissue models. Our models include a reduced and efficient description of stochastic, spontaneous cellular triggers as well as a simple model of heterogeneous inter-cellular coupling. Our results demonstrate that fibrosis promotes the emergence of focal excitations, primarily through reducing the electrotonic load on individual fibre strands. This enables excitation to robustly initiate within these single strands before spreading to neighbouring strands and inducing a full tissue focal excitation. Enhanced conduction block can allow trigger–substrate interactions that result in the emergence of complex, re-entrant excitation patterns. This study provides new insight into the mechanisms by which fibrosis promotes the triggers and substrate necessary to induce and sustain arrhythmia.
... In homogeneous atrial tissue models, the electrical remodeling conditions of PeAF can sustain stable rotor meandering in spatially compacted regions [5]. In the presence of electrophysiological heterogeneities, rotors are frequently found in fibrotic regions or at the boundaries between fibrotic and non-fibrotic tissues [6][7][8]. Additionally, it has been known that pinned spiral waves form extremely stable patterns with respect to external pacing and heterogeneities [9,10]. However, most computational models of human AF numerically solve deterministic partial differential equations (PDEs), ignoring the stochastic nature of complex biological systems. ...
... The pathophysiological importance of fibrosis in AF has been extensively studied. In patient-derived 3D computational models, the PS points are associated with fibrotic regions [7,8]; this spatial relationship between fibrosis and the rotor is robust to the model parameter variability [6]. In addition, fibroblast-myocyte coupling can affect the spiral wave dynamics and extracellular electrograms [28,29]; however, this coupling effect was not incorporated in this study. ...
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.
... 7,30,31 Prior work has suggested that conduction velocity, fibrosis representation, and action potential duration can markedly affect driver localization dynamics. [32][33][34] Thus, to ensure the set of potential driver sites produced by our analysis was fairly comprehensive in this manner, we repeated all simulations in postablation models with 4 additional conductivity sets (see Figure S1), chosen to increase/decrease conduction velocity by ±10% or ±20%. ...
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.
... Several research groups have described processes to create personalized atrial models, these include the use of only pre-procedural information such as magnetic resonance imaging (MRI) and computed tomography scans (CT) [8,9], or the use of procedural data together with noninvasive imaging techniques [1,10,11]. However, it remains unclear whether using non-invasive pre-procedural data is sufficient when creating personalized atrial models for therapy planning or if further activation data is required from invasive recordings [12]. Our method generated synthetic P-waves using LAT maps from invasive and non-invasive data. ...
... While RD dynamics are influenced by fibrosis distribution, other factors play important roles as well. 37 In Deng et al., 37 fibrotic tissue was included in the models based on the IIR and the conduction velocity was modified within the fibrotic regions. Therefore, slow conduction areas were set to be the same as fibrotic tissue. ...
... While RD dynamics are influenced by fibrosis distribution, other factors play important roles as well. 37 In Deng et al., 37 fibrotic tissue was included in the models based on the IIR and the conduction velocity was modified within the fibrotic regions. Therefore, slow conduction areas were set to be the same as fibrotic tissue. ...
... Even in that case, changes in APD/CV enhanced or decreased the probability that an RD anchored to a specific location. 37 Even if using non-invasive clinical data only can be seen as an advantage due to the extended time frame to generate computational models, the level of uncertainty present in patient-specific atrial models reconstructed without any invasive measurements (i.e. incorporating each individual's unique distribution of fibrotic tissue from medical imaging alongside an average representation of AF-remodelled electrophysiology) is sufficiently high that a personalized ablation strategy based on targeting simulation-predicted RD trajectories alone may not be related to the real arrhythmogenic substrate in the patient. ...
Aims
The long-term success rate of ablation therapy is still sub-optimal in patients with persistent atrial fibrillation (AF), mostly due to arrhythmia recurrence originating from arrhythmogenic sites outside the pulmonary veins. Computational modelling provides a framework to integrate and augment clinical data, potentially enabling the patient-specific identification of AF mechanisms and of the optimal ablation sites. We developed a technology to tailor ablations in anatomical and functional digital atrial twins of patients with persistent AF aiming to identify the most successful ablation strategy.
Methods and results
Twenty-nine patient-specific computational models integrating clinical information from tomographic imaging and electro-anatomical activation time and voltage maps were generated. Areas sustaining AF were identified by a personalized induction protocol at multiple locations. State-of-the-art anatomical and substrate ablation strategies were compared with our proposed Personalized Ablation Lines (PersonAL) plan, which consists of iteratively targeting emergent high dominant frequency (HDF) regions, to identify the optimal ablation strategy. Localized ablations were connected to the closest non-conductive barrier to prevent recurrence of AF or atrial tachycardia. The first application of the HDF strategy had a success of >98% and isolated only 5–6% of the left atrial myocardium. In contrast, conventional ablation strategies targeting anatomical or structural substrate resulted in isolation of up to 20% of left atrial myocardium. After a second iteration of the HDF strategy, no further arrhythmia episode could be induced in any of the patient-specific models.
Conclusion
The novel PersonAL in silico technology allows to unveil all AF-perpetuating areas and personalize ablation by leveraging atrial digital twins.
... We do not have validation of this rule-based inclusion of patient-specific electrophysiology across the data set used in the current study. 18 We only included the left atrium in our simulations; however, performing biatrial simulations [19][20][21] may improve the predictive accuracy of the classifier. Adding features derived from the 12-lead ECG provides additional information on the atria and could further improve the classifier. ...
Background
Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability.
Methods
Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics.
Results
We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14).
Conclusion
A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.
... In the context of AF, current applications of computational models are mainly focus on improving the basic understanding of the arrhythmia mechanisms and the AF clinical care, which are projected to contribute to AF management [14] . Regarding the atrial fibrosis, several works have addressed the problem of the altered electrophysiology during AF [15][16][17][18][19][20][21][22][23][24] . These studies use computational models combining different elements of fibrotic remodeling. ...
The excessive proliferation of fibroblasts causes fibrosis, a hallmark of atrial fibrillation (AF), and leads to alterations in the electrical conduction within the heart. However, the underlying electrophysiological mechanisms behind the multifactorial characteristic of AF fibrosis are not fully understood. This work studies the electrophysiological properties of different fibrosis configurations using computational simulations. For this purpose, the intermingling action of the structural and electrical remodeling due to fibroblasts are implemented in an electrophysiological description of the AF fibrosis. The model is built on the base of complex order operators and a fibroblast ionic formulation. Additionally, three fibrosis textures are considered for designing the atrial tissue representations. The resting and depolarization properties are analyzed by means of information theory and multidimensional scaling. The results evinced that the modulation of cardiomyocytes resting potential, exerted by the fibroblasts, is the mechanism giving rise to emergent electrophysiological properties as a result of the synergetic mathematical formulation of the proposed fibrosis model. The metrics assessing such properties unravel distinctive signatures of each fibrosis texture. Additionally, the multidimensional scaling computational tool reveals clusters specifically determined by the resting and depolarization properties, or by their combination. The observed clusters support an electrophysiological interpretation through the underlying fibrosis configuration, in which the diffuse, patchy and compact textures are relevant in determining the emergent patterns.
... Hence, electrophysiological properties such as APD and CV in the patient-specific computational model may affect optimal ablation targets. Deng et al. (2017) conducted a sensitivity study to investigate the influence of 10% variation of atrial APD or CV on exact locations of RDs in 12 patient-specific computational AF models. They discovered that changes in electrophysiological properties led to variation in the likelihood that RDs would be anchored to a specific site, meaning that RD trajectories based on personalized ablation strategy alone may not produce a satisfactory effect. ...
Atrial fibrillation (AF) is one of the most common arrhythmias, associated with high morbidity, mortality, and healthcare costs, and it places a significant burden on both individuals and society. Anti-arrhythmic drugs are the most commonly used strategy for treating AF. However, drug therapy faces challenges because of its limited efficacy and potential side effects. Catheter ablation is widely used as an alternative treatment for AF. Nevertheless, because the mechanism of AF is not fully understood, the recurrence rate after ablation remains high. In addition, the outcomes of ablation can vary significantly between medical institutions and patients, especially for persistent AF. Therefore, the issue of which ablation strategy is optimal is still far from settled. Computational modeling has the advantages of repeatable operation, low cost, freedom from risk, and complete control, and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance. This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF, from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation. Finally, we summarize current developments and challenges and provide our perspectives and suggestions for future directions.
