Abstract:

Background and Aims: The effective refractory period is one of the main electrophysiological properties governing arrhythmia maintenance, yet effective refractory period personalisation is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. The aim of this study is to evaluate the impact of incorporating clinical effective refractory period measurements when creating in silico personalised models on arrhythmia vulnerability. Methods: Clinical effective refractory period measurements were obtained in seven patients from multiple locations in the atria. The atrial geometries from the electroanatomical mapping system were used to generate personalised anatomical atrial models. To reproduce patient-specific refractory period measurements, the Courtemanche cellular model was gradually reparameterised from control conditions to a setup representing atrial fibrillation-induced remodelling. Four different modelling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) distribution of effective refractory period. The first two configurations were non-personalised based on literature data, the latter two were personalised based on patient measurements. We evaluated the effect of each modelling approach by quantifying arrhythmia vulnerability and tachycardia cycle length. We performed a sensitivity analysis to assess the influence of effective refractory period measurement uncertainty on arrhythmia vulnerability. Results: The mean vulnerability was 3.4±4.0 %, 7.7±3.4 %, 9.0±5.1 %, 7.0±3.6 % for scenarios A to D, respectively. The mean tachycardia cycle length was 167.1±12.6ms, 158.4±27.5ms, 265.2±39.9ms, and 285.9±77.3ms for scenarios A to D, respectively. Incorporating perturbations to the measured effective refractory period in the range of 2, 5, 10 and 20ms, had an impact on the vulnerability of the model of 5.8±2.7 %, 6.1±3.5 %, 6.9±3.7 %, 5.2±3.5 %, respectively. Conclusion: Increased dispersion of the effective refractory period had a greater effect on reentry dynamics than on mean vulnerability values. The incorporation of personalised effective refractory period in the form of gradients had a greater impact on vulnerability than had a homogeneously reduced effective refractory period. Effective refractory period measurement uncertainty up to 20ms slightly influences arrhythmia vulnerability. Electrophysiological personalisation of atrial in silico models appears essential and warrants confirmation in larger cohorts.

Abstract:

IntroductionThe role of the right atrium (RA) in atrial fibrillation (AF) has long been overlooked. Computer models of the atria can aid in assessing how the RA influences arrhythmia vulnerability and in studying the role of RA drivers in the induction of AF, both aspects challenging to assess in living patients until now. It remains unclear whether the incorporation of the RA influences the propensity of the model to reentry induction. As personalized ablation strategies rely on non-inducibility criteria, the adequacy of left atrium (LA)-only models for developing such ablation tools is uncertain.AimTo evaluate the effect of incorporating the RA in 3D patient-specific computer models on arrhythmia vulnerability.MethodsImaging data from 8 subjects were obtained to generate patient-specific computer models. For each subject, we created 2 models: one monoatrial consisting only of the LA, and one biatrial model consisting of both the RA and LA. We considered 3 different states of substrate remodeling: healthy (H), mild (M), and severe (S). The Courtemanche et al. cellular model was modified from control conditions to a setup representing AF-induced remodeling with 0%, 50%, and 100% changes for H, M, and S, respectively. Conduction velocity along the myocyte preferential direction was set to 1.2, 1.0, and 0.8m/s for each remodeling level. To incorporate fibrotic substrate, we manually placed six seeds on each biatrial model, 3 in the LA and 3 in the RA, corresponding to regions with the most frequent enhancement (IIR>1.2) in LGE-MRI. The extent of the fibrotic substrate corresponded to the Utah 2 (5-20%) and Utah 4 (>35%) stages, for M and S respectively, while the H state was modeled without fibrosis. Electrical propagation in the atria was modeled using the monodomain equation solved with openCARP. Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different points separated by 2cm. A point was classified as inducing arrhythmia if reentry was initiated and maintained for at least 1s. The vulnerability ratio was defined as the number of inducing points divided by the number of stimulation points. The mean tachycardia cycle length (TCL) of the induced arrhythmia was assessed at the stimulation site. We compared the vulnerability ratio of the LA in monoatrial and biatrial configurations.ResultsThe incorporation of the RA increased the mean LA vulnerability ratio by 115.79% (0.19±0.13 to 0.41±0.22, p=0.033) in state M, and 29.03% in state S (0.31±0.14 to 0.40±0.15, p=0.219) as illustrated in Figure 1. No arrhythmia was induced in the H models. RA inclusion increased the TCL of LA reentries by 5.51% (186.9±13.3ms to 197.2±18.3ms, p=0.006) in M scenario, and decreased it by 7.17% (224.3±27.6ms to 208.2±34.8ms, p=0.010) in scenario S. RA inclusion resulted in an elevated LA inducibility, revealing 4.9±3.3 additional points per patient in the LA for the biatrial model that did not induce reentry in the monoatrial model. ConclusionsThe LA vulnerability in a biatrial model differs from the LA vulnerability in a monoatrial model. Incorporating the RA in patient-specific computational models unmasked potential inducing points in the LA. The RA had a substrate-dependent effect on reentry dynamics, altering the TCL of LA-induced reentries. Our results provide evidence for an important role of the RA in the maintenance and induction of arrhythmia in patient-specific computational models, thus suggesting the use of biatrial models.

Abstract:

Atrial fibrillation is the most prevalent cardiac arrhythmia in the adult population associated with an elevated risk of cardiovascular events and sudden cardiac death. In 2020, more than 50 million people worldwide were estimated to have atrial fibrillation, and its prevalence is expected to double by 2060. Despite significant progress in diagnosing and treating atrial fibrillation, current therapies often fail to prevent adverse outcomes due to their one-size-fits-all approach, ignoring patient variability. Patient-specific atrial computer models, also known as atrial digital twins, have emerged to improve our understanding of the pathophysiology of atrial fibrillation and to address the growing public health burden posed by atrial fibrillation today. The vision of atrial digital twins is to serve as a tool supporting the evaluation of different treatment strategies and selecting the most appropriate one to address the specific needs of each patient.Personalization refers to the process of incorporating patient data, such as anatomical, functional, and substrate-related, into model parameters that reflect specific physical properties of the cardiac cells, tissue, or heart of the individual. Currently, there is no consensus on the methodology for constructing a digital twin to inform atrial fibrillation treatment. Some studies have developed methodologies using only non-invasive pre-procedural data, while others have employed invasive procedural data or a combination of both. The overall effect of the selected input data on the behavior of the patient-specific model is currently unknown.In this thesis, arrhythmia vulnerability and tachycardia cycle length were quantified to assess the impact of different input data on the behavior of the patient-specific model. Arrhythmia vulnerability was defined as the ratio of the number of inducing points divided by the number of stimulation points on the atrial surface. Tachycardia cycle length was measured at the stimulation location and defined as the average time between peaks of the dV/dt of induced reentries. In particular, the effect of three types of clinical data was evaluated: 1) anatomical personalization by comparing monoatrial versus biatrial models, 2) functional personalization by comparing models with personalized refractory period versus non-personalized models, and 3) functional and substrate personalization by comparing pre-procedural versus procedural data. Finally, a larger cohort of 22 patient-specific biatrial computer models was developed to train a machine learning classifier model for predicting arrhythmia vulnerability and evaluating the importance of personalized features on the prediction.The results of the first project showed that incorporating the right atrium increased the mean vulnerability of the left atrium and revealed new induction points per patient model, which did not induce reentry in the monoatrial model. The right atrium had a substrate state-dependent effect on arrhythmia dynamics.In the second project, the non-personalized scenario with homogeneous effective refractory period distribution was the least vulnerable to arrhythmia, while the regional personalized scenario was the most vulnerable. Heterogeneities in the form of regions promote unidirectional blocks, thereby increasing vulnerability, while the homogeneous scenario makes it less likely to induce reentry even with a shorter effective refractory period. Incorporating the effective refractory period as a continuous distribution slightly decreased vulnerability compared to the state-of-the-art heterogeneous non-personalized scenario. Increased dispersion of the effective refractory period in personalized scenarios has a greater effect on reentry dynamics than on the absolute value of vulnerability. Tachycardia cycle length of the personalized vs the non-personalized scenarios was significantly slower.In the third project, total activation times and patterns were markedly different between invasive and non-invasive modalities. Arrhythmia vulnerability was more influenced by the extent of fibrosis than by the activation patterns. Finally, the machine learning classifier achieved a moderate accuracy for the prediction of arrhythmia vulnerability. Fibrosis density measured at 10\,mm from the stimulation points and global conduction velocity were the features showing the highest impact on point inducibility prediction.The results presented in this thesis provide evidence that the selection of input data affects the behavior of the patient-specific computer model. The right atrium plays an important role in the maintenance and induction of arrhythmia, thus the use of biatrial models seems advisable. Personalization of the effective refractory period has a greater effect on reentry dynamics than on the absolute value of vulnerability. Substrate-related personalization was the feature with the highest influence on vulnerability, therefore, further detection methods are needed to ensure its correct representation. The machine learning classifier may offer a fast alternative reducing the need of expensive computations of virtual pacing protocols, thus aiding the transition to clinical applications. The use of patient-specific models with highly detailed anatomy, function, and substrate may improve the development of tools for therapy planning for atrial fibrillation.