For a list of publications related to DGM, please check the publications section at the bottom of this page.
Starting out with Atrial Tachycardia, we have expanded our research into other arrhythmias. Below you can find an overview of our past and current studies about AT, AF, VT, VF and TdP.
Furthermore, we run simulations on a daily base to test and verify our novel ideas.
Reentry is an underlying mechanism in many cases of atrial tachycardia (AT). Loops of reentrant electrical activation form around anatomical or functional 'holes' in the electrophysiological substrate and dominate the atrial cycle. Terminating such a 'full' loop may cause previously 'suppressed' loops around another hole to become a dominant full loop. Interpretation of electroanatomical maps can therefore be tedious: while a full loop may be identified easily, predicting suppressed loops requires complex interpretation. Here we propose that topological classification may aid identification of suppressed loops in complex AT and improve ablation procedures.
The anatomy of the left atrium can be topologically described as a closed surface with three orifices ('holes'): mitral valve, left and right pulmonary veins, and similar for the right atrium. Non-conductive tissue may constitute additional topological holes in the substrate. Anatomical reentry may occur around any of the holes described above.
We define two types of reentry. The true reentry, which is a full rotation around a (set of) hole(s) and which will be responsible for the cycle length of the AT. We also define a suppressed reentry as a rotation around a (set of) hole(s) which becomes a true reentry when the original true reentries are ablated.
Therefore, we can uniquely categorize an AT by identifying the number of holes in combination with the presence of full and suppressed loops. The Figure below shows the finite number of possible combinations for three and four holes - where at least one reentry is present. The optimal ablation strategy should terminate the full as well as the suppressed loops, by connecting their corresponding holes.
We extended DGM to perform a topological classification of a given AT. The full reentry loops will correspond to the cycles detected by DGM in the network, while suppressed loops are characterized by a wave propagating around a hole for at least a certain percentage of its circumference (percentage still under investigation). In the Figure below, an example of a suppressed loop is shown.
We showed for the first time that suppressed loops play a pivotal role as not ablating them merely leads to slower AT. Benefits include preventing redo maps, shorter and operator independent procedures, and possibly reducing AT recurrence.
Our work on AT is performed in collaboration with Prof. Mattias Duytschaever and Prof. Sebastian Knecht from the hospital AZ Sint-Jan, Bruges, Belgium, who work with the CARTO system (Biosense Webster) and Dr. Armin Luik and Ing. Annika Haas from the Städtisches Klinikum Karlsruhe, who mainly work with RHYTHMIA (Boston Scientific). We have analyzed over 110 clinical cases and our results will soon be submitted as a scientific paper.
We analyzed a simulated basket catheter data of a meandering spiral wave in the right atrium. When this data was analyzed with phase mapping, many false rotors appeared due to interpolation and far field effects*. By changing the parameters of DGM, we were able to differentiate the false rotors from the true rotors. Our manuscript on this study is published in Computers in Biology and Medicine4. Although a meandering spiral is not representative of AF, this was our first step in the direction of non-regular data.
We are currently analyzing several simulated as well as experimental datasets of AF. To better understand AF is one of our main goals in the near future.[*] Factors affecting basket catheter detection of real and phantom rotors in the atria: A computational study
We haved tested DGM on mapped VTs with CARTO (Biosense Webster) as similar algorithms can be used for VTs as for ATs. We found that DGM is a rapid automated algorithm that has a strong level of agreement with tradional mapping for manually re-annotated VT maps. This research was performed in collaboration with Associate professor and Electrophysiologist Geoff Lee and Dr. and Electrophysiology Fellow Josh Hawson from the Royal Melbourne Hospital. Our manuscript on this study is published in JACC: Clinical Electrophysiology 6
We are currently testing DGM on a mapped VFs with a socket electrode system and comparing our results with phase mapping. This research is performed in collaboration with Prof. Richard Clayton from Sheffield.
The first application of DGM was the analysis of intramural needle data of the CAVB dog model developed in the lab of Prof. M.Vos (Utrecht Medical Center). It remained unclear if TdP is perpetuated by focal activity or by reentry. A first analysis of the data (with a very rudimentary version of DGM) showed that for short episodes, TdP is maintained by focal activity, while for longer episodes, reentry appears. Non-terminating TdPs always are maintained by reentry. The results of this research were published here3.
After the results were published, an editoral was written on our work*. The question was raised how the reentry exactly manifests itself. Is it functional or anatomical? Are there multiple reentries? Is there meandering of the spiral core. The latest software of DGM allows us to answer these questions and is currently the topic of a master thesis within our group.[*] Twisting and Turning to Find an Explanation for Torsades de Pointes
DGM can be used to analyze simulated datasets. Usually phase mapping is used to find rotors (functional reentry). However, phase mapping cannot detect macro-reentry. The advantage of DGM is that it can automatically analyze any type in reentry, including macro-reentry. Therefore, DGM can be an excellent tool to analyze any type of dataset. We are currently also adding phase mapping to DGM for easy comparison of both methods.
A second advantage is that DGM can work with any type of input. It can be a 2D grid, 3D data, regular spaced data, irregular spaced data etc. DGM only needs the LATs and the coordinates of the electrodes.
The different types of reentry were simulated and analyzed in our first general methods paper1. In addition, we are also collaborating several groups worldwide to analyze simulated datasets, and the intermediate results look promising!
In our first study, we have compared DGM with phase mapping for a simulated stable rotor (functional reentry) whereby we put an regular electrode system on top of the rotor. We systematically added Gaussian noise to the LATs, and compares DGM with phase mapping. We found that phase mapping is very sensitive to noise, while DGM is much more robust.
In collaboration with Prof. Saiz and Dr. Martínez Mateu we have also compared phase mapping and DGM for the analysis of simulated basket catheter data in the right atrium for a meandering spiral. We found that DGM overcomes some of the limitations of phase mapping, which often finds false rotors4.
Finally, we are currently comparing phase mapping with DGM for rotors in the presence of fibrotic tissue. If DGM shows to be more stable than phase mapping, DGM could be a valuable tool to analyze AF. We wil provide the link of the study, when ready.