Periodic Reporting for period 2 - SMARTHEART (Directed networks as a novel approach for improving the management of cardiac arrhythmias)
Reporting period: 2022-08-01 to 2024-01-31
The management of cardiac arrhythmia remains the largest problem in cardiac electrophysiology. The prevalence of the most frequent arrhythmia, atrial fibrillation (AF), is expected to rise steeply due to the ageing population. In spite of intensive research, the mechanism of atrial fibrillation remains unclear, leading to poor results in its treatment. Ablation of AF often results in complex atrial tachycardia (AT), which are difficult to treat. Also ventricular tachycardias (VT) and fibrillations (VF) are a major cause of sudden cardiac death. Again, eliminating VTs with ablation has achieved only modest success in complex cases.
Therefore, there is an urgent need to better understand and localize the sources of arrhythmia in order to improve its treatment. I propose a radical new approach of applying network theory to study the mechanisms of AT, VT and AF. Currently, network theory is known for being the basis for the Google search engine other online social networks, and has myriad applications throughout biology, physics, and social sciences. However, it has never been applied to the heart. In this proposal, based on my invention and preliminary work, I propose to apply network theory to clinical data of cardiac arrhythmia, backed-up by in-silico simulations. A new set of research tools will be created to automatically detect the source of the arrhythmia for complex AT and AF, which will identify possible ablation targets. For VT a substrate analysis is proposed, in order to reveal the structure of the heart to also determine the ablation target. My preliminary results already show that network analysis is able to automatically predict sites of ablation, prior to surgery in AT, largely exceeding the most recent technologies currently used in clinics. Therefore, this translational project will not only provide novel insights into the mechanism of cardiac arrhythmia, but will actually lead to an improved treatment for the patient.
All information on this project can be found on www.dgmapping.com
Therefore, there is an urgent need to better understand and localize the sources of arrhythmia in order to improve its treatment. I propose a radical new approach of applying network theory to study the mechanisms of AT, VT and AF. Currently, network theory is known for being the basis for the Google search engine other online social networks, and has myriad applications throughout biology, physics, and social sciences. However, it has never been applied to the heart. In this proposal, based on my invention and preliminary work, I propose to apply network theory to clinical data of cardiac arrhythmia, backed-up by in-silico simulations. A new set of research tools will be created to automatically detect the source of the arrhythmia for complex AT and AF, which will identify possible ablation targets. For VT a substrate analysis is proposed, in order to reveal the structure of the heart to also determine the ablation target. My preliminary results already show that network analysis is able to automatically predict sites of ablation, prior to surgery in AT, largely exceeding the most recent technologies currently used in clinics. Therefore, this translational project will not only provide novel insights into the mechanism of cardiac arrhythmia, but will actually lead to an improved treatment for the patient.
All information on this project can be found on www.dgmapping.com
The primary objective of this project is to use network theory to investigate the underlying mechanisms of three major cardiac arrhythmia: atrial tachycardia (AT), ventricular tachycardia (VT), and atrial fibrillation (AF) and advance ablation therapy. To achieve this goal, we have undertaken the development of a software package named "Directed Graph Mapping" (DGM), specifically designed for the analysis of various cardiac arrhythmias.
1. Development of DGM
The development of DGM has been significantly advanced through the recruitment of a team of highly skilled programmers. Our overarching aim is to transform DGM into a universally accessible tool within the scientific community, capable of analyzing diverse electro-anatomical datasets related to cardiac arrhythmias, originating from computational simulations, experimental studies, or clinical data. To enhance user-friendliness, DGM has been converted into a collection of Python packages. It offers flexibility by allowing users to tailor the software according to their specific requirements, or to opt for a comprehensive graphical user interface (GUI).
The current version of DGM already surpasses the initially set project goals, featuring a wide array of functionalities that facilitate in-depth analysis and interpretation of cardiac arrhythmias. We are happy to report that two external partners have actively engaged with the software for analyzing computational data. These collaborations are expected to yield valuable scientific outputs within the upcoming year.
Looking ahead, we are actively working towards a major software release in the fall of 2023, specifically aimed at enabling external usage. Our ultimate ambition is to establish DGM as a standard tool for analyzing reentry in electro-anatomical datasets related to cardiac arrhythmias. As we move forward, it is important to emphasize that the DGM package has been instrumental in making substantial progress within each of the distinct work packages (WPs).
2. Ablation of AT
One central objective was to develop a groundbreaking ablation strategy for complex AT by automating the identification of their sources. We have surpassed the original goal of this workpackage, as we found a novel theory which can revolutionize the approach to AT ablation. We developed a unified classification system for ATs by incorporating topological analysis and viewing the left or right atrium as a sphere with holes, where the holes represent either anatomical gaps or non-conduction scar tissue. The prevailing theory posited that ATs were mainly driven by a single reentry circuit. However, our research has unveiled a paradigm-shifting revelation: single reentry loops do not exist in ATs. Instead, they consistently manifest as pairs of two reentry loops around the holes in the atria. Remarkably, this observation of paired reentry loops has been documented in published research for over three decades, known as the "index theorem." Strikingly, despite this knowledge, the clinical application and translation of this theorem were somehow overlooked.
We translate this theorem as follows. Every reentry loop can be classified as either a true reentry loop or a suppressed reentry loop. True reentry loops are typically identified and acknowledged as the driving force behind ATs. However, our pioneering work has introduced the concept of suppressed loops, which remarkably remained overlooked for the past 30 years. These suppressed loops play a crucial role in the manifestation of ATs, and not ablating them has been identified as the reason for the recurrent emergence of slower ATs post-ablation, an enigma that had eluded a satisfactory explanation until now.
Our achievements in WP1 signify a major step forward in the field of cardiac arrhythmia research, by offering a new understanding of ATs, which immediately translate into a more effective ablation therapy. The identification of suppressed reentry loops represents a significant leap in comprehending the complexities of ATs. We firmly believe that our discoveries will ultimately lead to improved clinical outcomes and the establishment of a more informed and precise approach to AT ablation procedures. We plan to submit a white paper before the end of August 2023.
3. Ablation of VT
The central objective of this part was to develop an innovative VT ablation strategy using DGM to identify driving circuits. Collaborating with Prof. Lee, we published findings in JACC EP. Building on insights from our research on ATs, we will now also explore suppressed loops in VTs, promising advancements in ablation techniques.
4. Mechanism of AF
The objective of this part, was to better understand the mechanisms of AF. We published a paper in Computers in Biology and Medicine showcasing DGM's success on in-silico AF data, tracking meandering reentry in basket data. A study comparing DGM with phase mapping is in its final stages, showing DGM's superior stability. Progress is made in directly working with signals, vital for analyzing complex AF datasets instead of the LATs.
1. Development of DGM
The development of DGM has been significantly advanced through the recruitment of a team of highly skilled programmers. Our overarching aim is to transform DGM into a universally accessible tool within the scientific community, capable of analyzing diverse electro-anatomical datasets related to cardiac arrhythmias, originating from computational simulations, experimental studies, or clinical data. To enhance user-friendliness, DGM has been converted into a collection of Python packages. It offers flexibility by allowing users to tailor the software according to their specific requirements, or to opt for a comprehensive graphical user interface (GUI).
The current version of DGM already surpasses the initially set project goals, featuring a wide array of functionalities that facilitate in-depth analysis and interpretation of cardiac arrhythmias. We are happy to report that two external partners have actively engaged with the software for analyzing computational data. These collaborations are expected to yield valuable scientific outputs within the upcoming year.
Looking ahead, we are actively working towards a major software release in the fall of 2023, specifically aimed at enabling external usage. Our ultimate ambition is to establish DGM as a standard tool for analyzing reentry in electro-anatomical datasets related to cardiac arrhythmias. As we move forward, it is important to emphasize that the DGM package has been instrumental in making substantial progress within each of the distinct work packages (WPs).
2. Ablation of AT
One central objective was to develop a groundbreaking ablation strategy for complex AT by automating the identification of their sources. We have surpassed the original goal of this workpackage, as we found a novel theory which can revolutionize the approach to AT ablation. We developed a unified classification system for ATs by incorporating topological analysis and viewing the left or right atrium as a sphere with holes, where the holes represent either anatomical gaps or non-conduction scar tissue. The prevailing theory posited that ATs were mainly driven by a single reentry circuit. However, our research has unveiled a paradigm-shifting revelation: single reentry loops do not exist in ATs. Instead, they consistently manifest as pairs of two reentry loops around the holes in the atria. Remarkably, this observation of paired reentry loops has been documented in published research for over three decades, known as the "index theorem." Strikingly, despite this knowledge, the clinical application and translation of this theorem were somehow overlooked.
We translate this theorem as follows. Every reentry loop can be classified as either a true reentry loop or a suppressed reentry loop. True reentry loops are typically identified and acknowledged as the driving force behind ATs. However, our pioneering work has introduced the concept of suppressed loops, which remarkably remained overlooked for the past 30 years. These suppressed loops play a crucial role in the manifestation of ATs, and not ablating them has been identified as the reason for the recurrent emergence of slower ATs post-ablation, an enigma that had eluded a satisfactory explanation until now.
Our achievements in WP1 signify a major step forward in the field of cardiac arrhythmia research, by offering a new understanding of ATs, which immediately translate into a more effective ablation therapy. The identification of suppressed reentry loops represents a significant leap in comprehending the complexities of ATs. We firmly believe that our discoveries will ultimately lead to improved clinical outcomes and the establishment of a more informed and precise approach to AT ablation procedures. We plan to submit a white paper before the end of August 2023.
3. Ablation of VT
The central objective of this part was to develop an innovative VT ablation strategy using DGM to identify driving circuits. Collaborating with Prof. Lee, we published findings in JACC EP. Building on insights from our research on ATs, we will now also explore suppressed loops in VTs, promising advancements in ablation techniques.
4. Mechanism of AF
The objective of this part, was to better understand the mechanisms of AF. We published a paper in Computers in Biology and Medicine showcasing DGM's success on in-silico AF data, tracking meandering reentry in basket data. A study comparing DGM with phase mapping is in its final stages, showing DGM's superior stability. Progress is made in directly working with signals, vital for analyzing complex AF datasets instead of the LATs.
Merging Topology with Cardiac Arrhythmia was a significant achievement in this project. By integrating topology, a mathematical field that explores properties of objects and spaces under deformation, with the study of cardiac arrhythmia, we found a new concept how AT can be unique classified and analyzed. We have a fundamental theory that single loops do not exist, and that one always has to look for the second loop. This second loop needs to be taken into account for the ablation therapy. If you do not ablate the second loop, a new (slower) AT will arise. We expect these findings to also influence our two other objectives, namely improving ablation on VT and uncovering the mechanism of AF.