AF precedes AFlut in most cases forming the required line of block by fibrillatory conduction. Waldo and Feld (2008) highlighted the inter-relationship between AF and AFlut. AF and AFlut are often even combined endpoints in studies evaluating the success of AF ablation ( Bunch et al., 2016). (2015) observed AF or organized atrial tachycardia in 53% of 300 patients within the first 6 weeks after pulmonary vein (PV) isolation. In the general AF population, 18.5% of patients were diagnosed with AFlut during a median follow-up time of 421 day post ablation ( Gucuk Ipek et al., 2016). 20% of recurrences after AF ablation in elderly are due to AFlut ( Dong et al., 2015). In more than half of the patients, sustained AF is reinitiated within 5 years after ablation or AFlut develops ( Bunch et al., 2016). Besides AF recurrence, the development of post-ablational atrial flutter (AFlut) represents a major problem ( Villacastín et al., 2003 Kobza et al., 2004 Chugh et al., 2005 Patel et al., 2008 Yamada and Kay, 2013 Biviano et al., 2015). The long-term success rate of atrial fibrillation (AF) ablation is unsatisfactory low, particularly in patients suffering from persistent AF. This allows to consider all relevant AFlut pathways when tailoring clinical ablation therapy in order to reduce the development and recurrence of AFlut. In contrast to clinical electrophysiological studies, our computational approach provides the means to identify all possible AFlut pathways and not just the currently dominant one.
Our novel method allows to assess the vulnerability of an individual patient to develop AFlut based on the personal anatomical, electrophysiological, and pharmacological characteristics. Finally, we tested how AFlut vulnerability of these substrates is modulated by exemplary antiarrhythmic drugs (amiodarone, dronedarone). Moreover, we demonstrate the application of tailored models considering disease-specific repolarization properties (healthy, AF-remodeled, potassium channel mutations) as well as applicabiltiy on a clinical dataset. We assessed the sensitivity of the flutter pathway maps with respect to conduction velocity and its anisotropy. In these dynamic simulations, the initial pattern eventually turns into the one driven by the dominant pathway, which is the only pathway that can be observed clinically. Flutter pathways can be instantiated by using an eikonal-diffusion phase extrapolation approach and a dynamic multifront fast marching simulation. In this way, all pathways along which AFlut can be sustained are identified.
Potential flutter pathways are then identified by tracing loops from wave front collision sites and constricting them using a geometric snake approach under consideration of the heterogeneous wavelength condition. The fast marching scheme is employed to compute activation times for stimuli from all parts of the atria.
We build a personalized model of atrial excitation propagation considering the anatomy as well as the spatial distribution of anisotropic conduction velocity and repolarization characteristics based on a combination of a priori knowledge on the population level and information derived from measurements performed in the individual patient.
Here, we present a novel method based on personalized computational models to identify pathways along which AFlut can be sustained in an individual patient. Indeed, substrate modifications during AF ablation can increase the likelihood to develop AFlut and it is clinically not feasible to reliably and sensitively test if a patient is vulnerable to AFlut. 4Faculty of Medicine, Albert-Ludwigs University, Freiburg, GermanyĪtypical atrial flutter (AFlut) is a reentrant arrhythmia which patients frequently develop after ablation for atrial fibrillation (AF).3Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg Bad Krozingen, Freiburg, Germany.2Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany.1Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.Axel Loewe 1 *, Emanuel Poremba 1, Tobias Oesterlein 1, Armin Luik 2, Claus Schmitt 2, Gunnar Seemann 1,3,4 and Olaf Dössel 1
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