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.
DGM has been optimized to work with data from AT. For this, we have extensively tested over 100 clinical cases of Atrial Tachycardia including complex cases2,3. Via a plug-in, DGM can load files from CARTO (Biosense Webster) or from RHYTHMIA (Boston Scientific). We also want to implement the EnSite NavX system (Abbott).
An unsolved question for AT is when the LAT maps suggest a double loop, whether this is a true double loop (both reentry circuit are equally dominant), or a dominant loop with a bystander loop. In case of double, currently in the clinical practice, entrainment mapping can be performed to reveal the true circuit. DGM is more likely to always show both loops. We have added a novel feature to the software, which shows the region of collision (ROC) of the two loops. If the ROC touches one of the loops, is means that this loop is a bystander loop. We are currently testing this feature in a clinical setting and will provide updates on its performance.
Most of 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).
We are also collaborating with Dr. Nicolas Derval, Lyric, Bordeaux for the implementation of RHYTHMIA cases (Boston Scientific).
No clinical data of AF was yet analyzed with DGM. However, this is one of our main goals in the future. If you have datasets which you would like to have analyzed, do not hesitate to contact us.
Once DGM has been optimized for AF, a new version will be released.
We did analyze 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 currently accepted in Computers in Biology and Medicine4.[*] Factors affecting basket catheter detection of real and phantom rotors in the atria: A computational study
We are currently testing and optimizing DGM on mapped VTs with CARTO (Biosense Webster) as similar algorithms can be used for VTs as for ATs. This research is performed in collaboration with Associate professor and Electrophysiologist Geoff Lee and Dr. and Electrophysiology Fellow Josh Hawson from the Royal Melbourne Hospital.
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.
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 with Dr. Cesare Corrado and Prof. Steve Niederer from King’s College Londen 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 a simulated meandering rotor, and for a rotor in the presence of fibrotic tissue. We wil provide the link of the study, when ready.
Van Nieuwenhuyse, E., Martinez-Mateu, L., Saiz, J., Panfilov, A. V., & Vandersickel, N. (2021)
DGM applied to a simulated dataset of a basket catheter with a meandering spiral in the right atrium. In 2 of the 3 catheter positions, DGM was able to eliminate false positives found by phase mapping.
Van Nieuwenhuyse, E., Strisciuglio, T., Lorenzo, G., El Haddad, M., Goedgebeur, J., Van Cleemput, N., ... & Vandersickel, N. (2021)
DGM can compete with the latest mapping system of CARTO upon analysing complex ATs.
Vandersickel, N., Van Nieuwenhuyse, E., Van Cleemput, N., Goedgebeur, J., El Haddad, M., De Neve, J., ... & Panfilov, A. V. (2019)
First general implementation of DGM. Applied to clinical ATs, simulated reentries (macro and functional) and simulated focal sources. DGM was also compared with phase mapping by adding Gaussian noise to a simulated functional reentry. DGM was shown to be more robust.
Vandersickel, N., Bossu, A., De Neve, J., Dunnink, A., Meijborg, V. M., van der Heyden, M. A., ... & Panfilov, A. V. (2017)
First rudimentary implementation of DGM to analyze experimental needle data of the CAVB dog model to unravel the mechanism the Torsade de Pointes. The method was not yet named as DGM.