Skip to main content

Atrial Fibrillation, Fibrosis and Rotors: New Insights from Imaging and Computational Modeling

Periodic Reporting for period 1 - AFIBROTIC (Atrial Fibrillation, Fibrosis and Rotors: New Insights from Imaging and Computational Modeling)

Reporting period: 2015-10-01 to 2017-09-30

Atrial fibrillation (AF) is the most common form of arrhythmia worldwide. In the EU, the prevalence of AF (2%) has doubled since the last decade and it is estimated that number of AF patients by 2030 will be 14–17 million. AF is associated with substantial morbidity and mortality. In particular, patients with AF have a five-fold higher risk of stroke. Management of AF and AF-related complications has a high burden on the social security systems (~10b€ in 2004).

Despite the high clinical and societal priority, clinical management of AF is still suboptimal. AF is typically treated by pulmonary veins (PV’s) isolation by catheter ablation. However, success rates of PV isolation in non-paroxysmal patients (i.e. roughly the two thirds of AF patients) are as low as 28% for a single procedure. Indeed, non-paroxysmal AF is sustained by complex electrical sources, such as rotors focal sources and multiple wavelets, which persist after PV isolation. Hereto, ablation schemes that address such electrical features (e.g. rotor-driven ablation) have gained popularity. Yet, the initial enthusiasm is mitigated by growing skepticism due to the difficulty in replicating such protocols in multicenter studies. Overall, ablation of non-paroxysmal AF remains an unstandardized procedure with low success rates (42%).

Moreover, stroke risk is treated either pharmaceutically (by anticoagulant drugs) or procedurally (by left clamping the left atrial appendage). Yet, stroke risk stratification indices, such as the CHADS score, are based on extremely generic parameters (e.g. age, hypertension, diabetes mellitus) and their predictive power remains low.

The overall objective of AFIBROTIC was therefore to study the key mechanisms of atrial fibrillation, in particular connected to electrical conduction and blood flow dynamics, by using patient-specific computational modelling. Computational modelling provides a unique framework to study the response of a biological system to given solicitations (boundary conditions) in a fully controlled reproducible and non-invasive way. Ultimately, the developed technology aims to leads toward personalized optimal therapy delivery for the atrial fibrillation patient.
Electro-anatomical maps (EAM’s) are used to guide ablation towards meaningful regions (e.g. the pulmonary veins). However, anatomical reconstruction in EAM systems is known to be poorly accurate and, as such, the clinician might be provided with a misleading feedback. To solve this issue, we have developed a framework to correct the errors in the EAM reconstruction by using pre-procedurally acquired anatomical scans. With the proposed system, the clinician will have at the same time precise measurements of electrical activity and of the underlying anatomy.

Besides the errors in the navigation system, a second important factor affecting the success rates of atrial ablation is the lack of standardization. Indeed, indeed, it is still relatively unknown which are the electrical sources sustaining the fibrillation or how ablation can interrupt them. Hereto, ablation schemes including the elimination of rotors were very recently introduced and claimed superior to standard ablation schemes based on the electrical isolation of the pulmonary veins (PV’s) alone. In rotor-based ablation, multielectrode sensing catheters are used to reconstruct rotor position. Yet, the initial findings could not be replicated in multicenter studies. In AFIBROTIC, we used computational modelling to test some of the underlying assumptions of rotor-driven ablation and benchmark basket-guided ablation: if the rotor core can be reliably localized by a grid of electrodes; how the number/resolution of electrodes and the distance from the atrial wall can affect such localization; if simulated ablation is able per se to terminate AF. Concerning the suitability of the multielectrode catheter, we evaluated the effect of two factors: i) the relative distance between electrodes in the sensing catheter and ii) the distance between the catheter and the atrial wall. As such, we were able to set an upper bound on both settings above which rotor detection becomes unreliable. Additionally, the developed computational model was used as an in-silico testing environment for ablation. Hereto, ablation was implemented by setting zero conductivity to the ablated tissue in the numerical model. Our very preliminary findings seem to question the effectiveness of rotor-driven ablation but need further verification.

A further objective consisted in the study of intra-atrial blood flow in atrial fibrillation. AF, due to atrial enlargement and reduced mechanical function, might induce blood stasis and, therefore, favor the formation of blood clots. Yet, no hemodynamic parameter is taken into account by modern stroke risk stratification indices. This can explain in part the low predictive power of such indices. The objective of this activity was therefore use computational fluid dynamics (CFD) to understand and quantify anatomical and functional modulators of intra-atrial blood flow in atrial fibrillation on a subject specific basis. Hereto, an image processing personalization pipeline was developed to extract patient-specific atrial anatomy and motion over time from dynamic CT data. The study showed that atrial fibrillation is responsible for an overall reduction in the blood flow velocity inside the atrium and especially in the left atrial appendage. Moreover, blood particles were shown to remain longer in the atrial appendage during atrial fibrillation episodes. These result point towards a key role of mechanical remodeling in AF in explaining blood stasis and, potentially, stroke risk. In the longer run, we aim to provide a pipeline for the optimal patient-specific assessment of stroke risk in AF.
AFIBROTIC delivered a set of biomedical-engineering tools that can enable better patient-specific treatment of atrial fibrillation. Namely:

The framework for automatic non-rigid alignment of pre-procedural anatomical scans and intra-procedural EP measurements could enable more effective ablation procedures. Moreover, it could be beneficial for more basic research in that will enable establishing precise spatial correspondence between anatomical/structural features extracted from the pre-procedural images and electrical features measured by the intracardiac catheters during the procedure.

The computational study on rotor driven ablation showed that the output of such catheter cannot be used “blindly” but that a series of conditions have to be satisfied in order for rotor detection to be reliable. These findings could be included in a quality assurance pipeline for rotor tracking algorithm with multi electrode catheters. Moreover, the same computational pipeline could be employed for comparing ablation patterns and choosing the optimal one for the specific patient.

The study on atrial flow-dynamics is pioneering. This is the first effort towards accurate patient specific modelling of hemodynamics in AF. The proposed framework could be used to predict the likelihood to develop stroke on a patient specific basis by considering key AF related remodeling mechanisms. This would be a fundamental step forward as compared to clinically employed indices.