Evaluation of Directed Graph-Mapping in Complex Atrial Tachycardias

Nieuwenhuyse, Enid Van; Strisciuglio, Teresa; Lorenzo, Giuseppe Di; Haddad, Milad El; Goedgebeur, Jan; Cleemput, Nicolas Van; Ley, Christophe; Panfilov, Alexander; Pooter, Jan De; Vandekerckhove, Yves; Tavernier, René; Duytschaever, Mattias; Knecht, Sébastien; Vandersickel, Nele

doi:10.1016/j.jacep.2020.12.013

Cited by 14 publications

(26 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In their work, the authors established a proof-of-concept using in-silico simulations of several activation patterns and clinical data of atrial tachycardia (AT) to demonstrate the wide applicability of directed networks in this domain. In their very recent follow-up study (Van Nieuwenhuyse et al, 2021b ), authors evaluated the diagnostic accuracy of their method in more complex AT cases. They retrospectively analyzed 51 AT cases and compared the diagnoses made by their method with those of the experts based on high-density activation maps.…”

Section: Discussionmentioning

confidence: 99%

“…Vandersickel et al ( 2019 ) demonstrated the wide applicability of directed networks for the detection of driving mechanisms of cardiac arrhythmias (focal or reentrant) in both the atria and ventricles. The same authors applied the method to more complex clinical atrial tachycardia cases (Van Nieuwenhuyse et al, 2021b ). This latter work is very recent and resembles similarities with our work, which we discuss in the section 4.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Atrial Flutter Mechanism Detection Using Directed Network Mapping

et al. 2021

View full text Add to dashboard Cite

Atrial flutter (AFL) is a common atrial arrhythmia typically characterized by electrical activity propagating around specific anatomical regions. It is usually treated with catheter ablation. However, the identification of rotational activities is not straightforward, and requires an intense effort during the first phase of the electrophysiological (EP) study, i.e., the mapping phase, in which an anatomical 3D model is built and electrograms (EGMs) are recorded. In this study, we modeled the electrical propagation pattern of AFL (measured during mapping) using network theory (NT), a well-known field of research from the computer science domain. The main advantage of NT is the large number of available algorithms that can efficiently analyze the network. Using directed network mapping, we employed a cycle-finding algorithm to detect all cycles in the network, resembling the main propagation pattern of AFL. The method was tested on two subjects in sinus rhythm, six in an experimental model of in-silico simulations, and 10 subjects diagnosed with AFL who underwent a catheter ablation. The algorithm correctly detected the electrical propagation of both sinus rhythm cases and in-silico simulations. Regarding the AFL cases, arrhythmia mechanisms were either totally or partially identified in most of the cases (8 out of 10), i.e., cycles around the mitral valve, tricuspid valve and figure-of-eight reentries. The other two cases presented a poor mapping quality or a major complexity related to previous ablations, large areas of fibrotic tissue, etc. Directed network mapping represents an innovative tool that showed promising results in identifying AFL mechanisms in an automatic fashion. Further investigations are needed to assess the reliability of the method in different clinical scenarios.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Atrial Flutter Mechanism Detection Using Directed Network Mapping

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Both approaches determine likely circuits of activation. On one hand, the directed network approach in the study of Vandersickel et al ( 2019 ) assumed a stable re-entry circuit, for example, an atrial tachycardia (Van Nieuwenhuyse et al, 2021 ). On the other hand, our technique also worked for fibrillatory data.…”

Section: Discussionmentioning

confidence: 99%

Time-Averaged Wavefront Analysis Demonstrates Preferential Pathways of Atrial Fibrillation, Predicting Pulmonary Vein Isolation Acute Response

et al. 2021

View full text Add to dashboard Cite

Electrical activation during atrial fibrillation (AF) appears chaotic and disorganised, which impedes characterisation of the underlying substrate and treatment planning. While globally chaotic, there may be local preferential activation pathways that represent potential ablation targets. This study aimed to identify preferential activation pathways during AF and predict the acute ablation response when these are targeted by pulmonary vein isolation (PVI). In patients with persistent AF (n = 14), simultaneous biatrial contact mapping with basket catheters was performed pre-ablation and following each ablation strategy (PVI, roof, and mitral lines). Unipolar wavefront activation directions were averaged over 10 s to identify preferential activation pathways. Clinical cases were classified as responders or non-responders to PVI during the procedure. Clinical data were augmented with a virtual cohort of 100 models. In AF pre-ablation, pathways originated from the pulmonary vein (PV) antra in PVI responders (7/7) but not in PVI non-responders (6/6). We proposed a novel index that measured activation waves from the PV antra into the atrial body. This index was significantly higher in PVI responders than non-responders (clinical: 16.3 vs. 3.7%, p = 0.04; simulated: 21.1 vs. 14.1%, p = 0.02). Overall, this novel technique and proof of concept study demonstrated that preferential activation pathways exist during AF. Targeting patient-specific activation pathways that flowed from the PV antra to the left atrial body using PVI resulted in AF termination during the procedure. These PV activation flow pathways may correspond to the presence of drivers in the PV regions.

show abstract

“…Van Nieuwenhuyse et al [147] have shown the advantage of using directed graphs to determine reentry circuits during atrial flutter. However, the algorithm provides global direction of the wave front and macroreentry paths.…”

Section: Discussionmentioning

confidence: 99%

“…Determining the direction of the wavefront is essential to understand the behavior of atrial electrical activity. Van Nieuwenhuyse et al [147] have recently shown the advantage of using directed graphs to understand the wavefront direction; however, the method relies on the annotation of electrograms, which is not a trivial task under AF due to the complexity of the signal. In neuroscience and more specifically in the study of the electroencephalogram, statistical approaches have emerged as a powerful tool to understand the electrical activity of the brain [148].…”

Section: Determination Of Wavefront Direction Using Transfer Entropymentioning

confidence: 99%

A Multiscale in Silico Study to Characterize the Atrial Electrical Activity of Patients With Atrial Fibrillation. A Translational Study to Guide Ablation Therapy

Arciniegas¹,

Patricio²

View full text Add to dashboard Cite

show abstract

Evaluation of Directed Graph-Mapping in Complex Atrial Tachycardias

Cited by 14 publications

References 10 publications

Atrial Flutter Mechanism Detection Using Directed Network Mapping

Atrial Flutter Mechanism Detection Using Directed Network Mapping

Time-Averaged Wavefront Analysis Demonstrates Preferential Pathways of Atrial Fibrillation, Predicting Pulmonary Vein Isolation Acute Response

A Multiscale in Silico Study to Characterize the Atrial Electrical Activity of Patients With Atrial Fibrillation. A Translational Study to Guide Ablation Therapy

Contact Info

Product

Resources

About