Okay, good morning, everyone. It's my pleasure to welcome you to the San Diego Heart and Rhythm 2025, 46th meeting of the Heart Rhythm Society. Thanks everyone for coming out so early on this last day. Our session will be entitled Atria Arrhythmia Mechanisms. And at this point, I'd like to call our first speaker, Dr. Liu, who will be talking about diabetes and hypopneumonia promote atrial fibrillation risk by TRPM7 mediated activation of mitochondrial oxidative stress inflammation and SR diastolic calcium leak. Thank you. Thank you. Good morning. Okay. So, this is what I'm going to talk about. Okay. First, introduction. So, atrial fibrillation affects over 60 million people worldwide. And diabetes is recognized as an independent risk factor for AFib. And people with type 2 diabetes are about 40% more likely to develop AFib. Our recent study shows that diabetes can induce cardiomyocyte, mitochondrial oxidative stress, cycloplasmic reticulum calcium leak from re-anti-receptor 2, and atrial inflammation. All these contributes to AFib development in diabetes. And hypomagnesemia is frequently comorbid with diabetes and AFib. It has also been linked to oxidative stress and inflammation for decades. Meanwhile, magnesium supplementation has been used to treat AFib. It also shows that it can improve diabetes-induced cardiac diastolic dysfunction in our work. And another of our studies show that TRIPM7, which is a magnesium transporter with the kinase domain, it can actually mediate hypomagnesemia-induced oxidative stress and inflammation. So in this work, we investigate whether hypomagnesemia can increase AFib incidence through TRIPM7 kinase activation. So method. Here we have three sets of animals. First is diabetic mice. We fed with high-fat diet for 30 weeks. And for magnesium supplementation, we gave them magnesium sulfate in the drinking water for six weeks. And then we have wild-type and transgenic TRIPM7K1646R mice, which had no kinase function but normal channel function. And for hypomagnesemia model, we gave them low-magnesium diet for six weeks. And for diabetic model, we gave them high-fat diet for 30 weeks. And then we measured AFib inducibility, protein expression, mitochondrial rust, et cetera. Results. So first, I want to show you here that diabetic mice had hypomagnesemia with decreased plasma magnesium levels. And magnesium supplementation can reverse these changes. And also, magnesium supplementation can decrease the AFib inducibility in diabetic mice. So this suggests that hypomagnesemia contributes to AFib. Then we switched to hypomagnesemia mouse model. So we used a diet to induce hypomagnesemia, which is confirmed by the serum magnesium level in the wild-type mice. And we also observed TRIPM7 over-expression and significantly increased AFib incidence in the wild-type hypomagnesia mice. And in the TRIPF7 mutant mice, which had no kinase function, we still observed hypomagnesemia. But the TRIPM7 over-expression and AFib inducibility were prevented. This indicates that TRIPM7 kinase plays important roles in hypomagnesemia-induced AFib. Next, we measured oxidative stress. So we isolated A2 cardio myocytes and measured the mito-rust levels. And as shown here, the wild-type hypomagnesemia myocytes had increased mito-rust levels. And this can explain that the increased oxidation of chymokinase II that we observed here in the wild-type hypomagnesium A2 tissue. Other people have reported that chymokinase II oxidation can enhance re-annulusceptor II phosphorylation, leading to SR calcium leak and contributing to AFib risk. And indeed, we also observed the elevation of re-annulusceptor II phosphorylation. With mutant mice, we observed decreased mito-rust levels and also reductions of chymokinase II oxidation and re-annulusceptor II phosphorylation. This suggests that TRIPM7 kinase promotes the atrial oxidative stress here. Next, we studied inflammation. We measured the expression of atrial tissue for MCP1, which is a chemo-attractant for microphage infiltration, and NRP3 and L1 beta, which initiate the inflammatory response. And we observed elevation of all three in wild-type mice, and the mutant mice showed recovery of all three chemicals. This tells us that TRIPM7 kinase can activate atrial inflammation. And then we did some tests on diabetic mouse. Here we can see that TRIPM7 is overexpressed in diabetic mouse atrial, and magnesium supplementation can normalize these changes. Also the TRIPM7 mutant mice on high-fat diet showed decreased AFib incidence. This suggests that TRIPM7 kinase contributes to diabetes-associated AFib. And we also did some human studies. Our collaborator, Dr. Dobreff in Germany, they tested human right atrial tissue, and they found that patients with diabetes had increased TRIPM7 expression. Also the mitoRAS levels in the isolated atrial cardiomyocytes was increased, and L1 beta level was increased when they compared with non-diabetic people. So this suggests similar pathologic changes in humans and mice. And so conclusions that diabetes could cause hypomagnesemia, which can lead to increased expression of TRIPM7 with elevated kinase activity. Increased TRIPM7 kinase activity can cause cardiomyocyte oxidative stress, which increase oxidation of CAMX2, leading to the subsequent reanusceptor II phosphorylation and SR calcium leak. Increased TRIPM7 kinase activity also enhanced the, promotes the atrial inflammation. All these can contribute to diabetes-associated AFib development. Magnesium supplementation may actually reduce AFib risk by reversing all these changes. And targeting TRIPM7 kinase represents a new strategy to reduce diabetes-associated AFib risk. And this work is done in Dr. Samuel Dutty's lab in University of Minnesota, and these are lab members. And we want to thank our collaborator, Dr. Dobrev, and these are his lab members. We want to thank Dr. Treanor from Department of Biological Sciences, University of Toronto, Canada, for the generous gift of TRIPM7 mutant mouse breeders, and this study was supported by NIH grants. Thank you. Thank you very much. We have time for some questions. Is there a TRIPM7 blocker available? Yes, there is. So for blockers, there are China blockers and kinase blockers. There is one supposed to be a kinase blocker, and we are planning to try it on the mouse. And the channel blocker? The channel blocker, because this, for the mutant mice, the channel function is normal, so we do not expect the channel function to be much. Also, the hypomagnesemia was not improved in this transgenic mice, so we didn't expect it to play significant roles. We did some calcium studies in ventricular mouse sites, not in atrial. Not yet. We will do that. If not, we'll thank our speaker again. There he is, our next speaker, waiting for disclosures, and we start to see a lot of people. Our next speaker is Dr. Jichao Zhao from Auckland. He will be talking about structural determinants of reentrant driver dynamics in atrial fibrillation, insights from digital twins of ex vivo human hearts. Thank you, Ed. Good morning, everyone. My name is Jichao Zhao. I'm a researcher based at the University of Auckland, so I'm presenting here on behalf of my student, Anu. So the talk title is Structural Determinants of Reentrant Driver Dynamics in Atrial Fibrillation, Insights from Digital Twins of Human Heart, ex vivo. This is the last day of HIS, so we will make the background really simple. So we all know that PVA for precision AF is suboptimal. One of the possible reasons is due to lack of understanding of the role of atrial structure in sustaining AF and in precise identification of bleaching target. The aim of this study is systematically examined the influence of heart-specific atrial structures, namely the atrial wall thickness, myofibrillation, and fibrosis. So this talk, we are more specifically on the atrial wall thickness and fibrosis. The first part of the study conducted by our collaborator at Ohio State University led by Vladimir Fedorov. He conducted optical mapping. In this study, we included seven human hearts. Among the seven hearts, we found that four out of seven had red atrial drivers, as you can see indicated by the red arrows in the left or right atria. Among the seven hearts, we also found three hearts had left atrial drivers located in the posterior left atria, as you can see the green color. So this is the optical mapping. After this, we conducted structural imaging using contrast-enhanced MRI using a 9.4 Tesla MRI scan. So this enabled us to achieve, after interpolation, a 180-micron resolution. We also used a coupled PDE approach to estimate 3D wall thickness. As you can see, the figures, the top one showing the red atria, the bottom one shows the left atria. The color coded by the wall thickness, the red color indicates a thicker wall. The blue indicates a thinner wall. So we plotted the wall thickness distribution for the left and the right. This is just for one heart. We also merged a superimposed histogram of the wall thickness distribution for the heart. So we can see that the red color, that's the red atria, so has a wider wall thickness dispersion given that the red atria has a major myeloband dose. So the observation across all seven hearts, we found that the atrial wall was thicker and varies more in the red than the left. So we published this in Interface Focus 2027, sorry, 2023. We also look at, among seven hearts, we also, as mentioned earlier, we have four hearts had red atrial drivers. So we compared the four hearts with optical capture drivers, compared with three red atrials without optical capture drivers. Again, we plotted the wall thickness distributions. As you can see on your left hand, the wall thickness distribution for the two groups, the wall thickness. So what we observed is that the red atria with optical capture drivers were thicker, with more variable wall thickness than the red atria without optical capture drivers. So that's, we believe, the cardiac modeling leads to the changes in the red atria. On the other hand, we found pretty interesting, we have data showing that remodeling renders the left atria too thin, particularly in the left atria driver regions. Again, you can refer our previous publication for details. So here we show a little bit of the modeling in terms of the impact of wall thickness on AF driver dynamic. So this is one of the studies done earlier, 2018, showing in the uniform wall thickness, we see a very stable entrance. As you can see, the red color flower type, that is showing the singularity of the entrance driver. They're very stationary. And you can see on these figures we're showing, we introduced stepwise the wall thickness. The dashed white line indicates the boundary of the wall thickness, half of them thinner wall, half of them thicker wall. So what we see is this entrance driver is not stationary anymore. They start drifting along this wall thickness gradient. And very important is actually the entrance driver always stay in the thinner part, they become a safety factor. So this is simple geometry. We will look at this realistic 3D human atria geometry. So here is showing a uniform wall thickness model. We construct from realistic endocardial surface of human right atria, and with a uniform wall. And this red dot showing the pacing side. So each chamber we use in 12 to 16 pacing locations. So after pacing, we observe the AF dynamics. So we see is, you can see the, I'm not sure you can see my cursor or not. So you can see the red dot with solid line indicated drifting and localized entrance drivers. And the red dot with dashed line indicated drifting entrance driver. The red dot only indicated the entrance we introduced and localized at initial location. The blue indicated anatomic entrance. The green indicated the entrance driver self-terminated after one second simulation. So we can see overall the pacing side, the entrance drivers are quite stationary from at least 12 to 16 locations. That's using in the uniform wall thickness. When we use realistic wall thickness, we can see the start have quite a different pattern. We see quite a lot of blue that's indicated anatomic entrance. Also more importantly, we see they are drifting. They are drifting along the critical term analysis, the bundles. Then we put on the other side, you can see the wall 3D, wall thickness map of the radiation. So observation for the radiation dynamics we see is that random drivers drift along the wall thickness variation, the gradient, and localized in the thinner region. Again, the color map, the blue means the thinner area of the wall. So this is what we observed for the red area. So what happened to the left? So here's the two figures compared with the simulation using the uniform wall thickness and realistic wall thickness. Again, the patterns are consistent. Again, we see that the renton drivers drift along the wall thickness variations. And for the left feature, particularly the anchored manual location leads to anchored anatomical structure. For left, it's the four permanent means. So that's the wall thickness. We also look at the impact of the fibrosis on EF driver dynamic. So here's just one heart with two pieces inside to demonstrate the point. So the yellow indicates the singularities which result fibrosis in the modeling. The purple color indicates the singularities with fibrosis in computer modeling. So we can see in these two particular pieces inside, the yellow and purple, they're overlapping. So that's indicated in the red area. The fibrosis has a limited impact on the renton driver dynamic. What happened to the left? As you can see the bottom, the two slides, the two figures, the two pieces inside. Again, for the left area, the different color showing without fibrosis. You can see some location has overlapping, but they're not quite the same locations. So that means in the left area, fibrosis plays more important role to alter the renton driver anchoring locations. So here's to see the impact of the interact between the fibrosis and the wall thickness. So this gray color showing the averaged wall thickness in the left area and the right area. So you can see it averaged quite a thicker four and five. And the green indicated the renton drivers without fibrosis in the modeling. So you see the quite a thinner region. When we add fibrosis into this equation, we found that the fibrosis attenuate this impact, which means they are not localized kind of global size of wall thickness in the region. There can be low, there can be regional thin H-wall region. So here's the come to our conclusion slides. So our computer simulation suggests the H-wall substrate are chamber specific. We also found is that the wall thickness is pretty important. Actually it's the impact on the AF driver dynamic is global. We found that a renton driver drift along the region which wall thickness varies and localize the region with thin H-wall in both H-wall chambers. We found that fibrosis probably play a more important role in the left H-wall, given the wall thickness in the left is a more relatively more uniform, where the H-wall wall thickness is more important in the right. So this is what we learned from the X-view of human heart. So currently in my lab, we also try to translate what we learned from the X-view heart to the in vivo LG RMI. In 2018, towards this goal, we hold the first segmentation challenge in Mekai, one of the premier machine learning conference. So we got quite a many team participating in challenge. And later on we publish in a global benchmarking study in medical image analysis. Then last year we hold another challenge, because we all know that atrial fibrillation, the biatric chamber disease, really not just look at the left, we also doing the biatric chamber. So we hold a big data and we're going to test a multi-class AI approach for both atrial and left atrial and right atrial cavity and also atrial wall directly from LG RMI to improve target ablation. Apparently we had a final last year in Morocco. And just more recently in my group we use UK Biobank and also the Utah data and also our local hospital data. We develop an AI approach and also extract the biomarkers. That's pretty much all my talk. Thank you much for your attention. Thank you very much. Any questions? So, if I understand correctly, atrial myopathy is basically everywhere in acute stage of fibrillation. And your data suggests anywhere there's fibrosis, you can have a re-entrant driver. Is that correct? Not really. From our simulation, we actually found the atrial wall thickness is more important, has a global impact. Fibrosis, they play some roles, especially in the left atrial. In the right atrial, I think it's the re-entrant driver is really more, the location of the re-entrant driver is more determined by the wall thickness. Some of the fibrosis, yes, they may be play a kind of regional role or local roles, but some of them maybe don't have any impact due to other structure factors. They're not the same. So, how many re-entrant drivers were on the right-hand side and on the left-hand side in your human experiment? Yeah, that's conducted by our collaborator at Ohio State University using optic mapping. So, in this seventh human heart, I'm showing you one of the slides, I think we found seven, I think we have seven right atrial drivers versus four left atrial drivers from my memory. So, we have more right atrial drivers from our optic mapping of the human heart. Thank you. Thank you. Thank you very much. Yeah, so that's your study for the, I think I read your paper. Yeah, that's a really good question. One quick last question from Dr. Bohut. Yeah, that's a really, really good question. Here, actually, this I'm showing results is actually done by my PhD student, Anu. She's done for four years. We also have quite a lot of fibrosis, myofibrillation impact. But I didn't show you here. The reason is a little bit hard to see by eyes. Yes, there will be some impact. But I think it's because, especially for the right atria, mainly driven by myobundal, the wall thickness, they are kind of full. I will say they will have some impact, but it probably won't be that pronounced. That's why I didn't also show you here. But we have one paper under review, in revision. So hopefully we can publish soon so you can see some of the results there. Yeah, thank you. That's really, really good question. Thank you very much. Thank the speaker very much. Okay, we're moving on to the third talk in this. Hajira Mokhtar. No files, I guess. The speaker is not here. Is the next speaker in the house? Okay, we'll wait a couple minutes just to resynchronize things. I guess we'll wait a few minutes. No. No, I guess not. Okay, let's bring the next speaker up. Okay, our next speaker is Neko Yamaguchi. He'll be talking about atrial fibrillation susceptibility gene ERBB4 contributes to pathological atrial remodeling. Thank you. Go ahead, doctor. Good morning. My name is Neko Yamaguchi. I'm a research fellow in David Park Lab at NYU. I'm talking about how ERBB4 contributes to atrial remodeling. I have no relevant disclosures. Atrial fibrillation, AF, is the most common atrial arrhythmia worldwide. Its prevalence is estimated at 52.5 million globally, projected further to grow. Atrial remodeling is central to AF pathogenesis. It includes pathological changes such as altered electrical structure and contractile function in the left atrium. Recently, genetic components of AF have been studied aggressively. Genome-wide association studies for AF have been performed in more than 1 million people from multiple countries. GWAS studies identified interleukin variant in ERBB4 as a locus associated with AF. In the reported AF risk genes, we focused on ERBB4 because of its importance in cardiac development. So what is ERBB4? ERBB4 is a tyrosine kinase receptor expressed on the membrane of the cardiomyocyte. Neuregulin-1 is one of the major growth factor released from endothelial cells to bind to ERBB4. The ligand binding turns on the tyrosine kinase activity in the cardiomyocyte. Neuregulin-1 ERBB4 signaling is required during cardiac development to regulate biological processes such as ventricular trabeculation, cardiomyocyte differentiation, proliferation, and conduction system development. ERBB4 knockout mice die during embryonic stages due to severe defect in ventricular trabeculation. Although the role of ERBB4 in atrial development has not been studied, adult human heart, the highest expression of ERBB4, is actually in the atrial cardiomyocyte, suggesting the important role of ERBB4 in maintaining atrial physiology. So to see the relationship between ERBB4 and AF, we analyzed left atrial RNA sequencing from 251 patients from Cleveland Clinic Biobank. We found ERBB4 expression is reduced in the patient with AF compared to sinus rhythm. We further dissected AF status and found lower ERBB4 expression is associated with persistent AF compared to no-AF or paroxysmal AF. We next conducted expression-quantitative tolateralocyte EQTEL analysis to integrate ERBB4 expression and AF GWAS report. This is the locus zoom plot of ERBB4 on chromosome 2. We found the reported AF risk SNP is associated with reduced ERBB4 expression in the left atria. To study the role of ERBB4 in atrial physiology, we created cardiomyocyte-specific ERBB4 heterozygous knockout mouse model. Controllers are ERBB4 FluxHet and MLC2 AcryHet. ERBB4 het mouse has both mutant alleles. ERBB4 het shows reduced ERBB4 expression in the left atria at protein level compared to controllers. Surface electrocardiography shows prolonged PWAVE duration in ERBB4 het, indicating the impaired atrial conduction. This is in the absence of left atrial enlargement by echocardiography. To see the molecular basis underlying the impaired atrial conduction, we performed RNA sequencing in left atrial tissue in the mouse model. Following individual comparison between ERBB4 het versus controllers, we obtained 760 differentially expressed genes. Using these DEGs, we performed functional analysis. These are curated lists of significant gobiological processes. Upregulated are apoptosis, fibrosis, inflammation, and coagulation. Downregulated are metabolism, contraction, and conduction. In the human dataset, we sought to answer how varying levels of ERBB4 impact genes of interest, specifically those associated with conduction, metabolism, and fibrosis. Plotted here are the correlations between ERBB4 het and homologous DEGs from the mouse model. Remarkably, when overlapped with the DEGs from the mouse model, 72% of the homologous genes of DEGs are behaving in an expected fashion. In other words, in the mouse model, in RB4 hat has lower RB4 with less GJ5. With less GJ5 in human data set. Similarly, those with less RB4 have significantly lower GJ5. Using the concordantly correlated genes, we perform functional analysis. Curated lists show the concordantly regulated RB4 correlated genes annotated in the same biological processes which are contraction, conduction, metabolism, fibrosis, apoptosis, inflammation, and coagulation. To validate the RNA sequencing data, we did fibrosis assessment in the leptoid area in mouse model. RB4 hat shows increased fibrosis. We next performed a apoptosis assay in the leptoid area using tunnel staining. Derpy represent nuclei. Tunnel represent apoptotic DNA fragment. RB4 hat shows increased apoptosis. We also performed mitochondrial reactive oxygen species assay in left atrial cardiomyocyte. Mitotox represent superoxide, a type of ROS. Mitotracker represent mitochondria in a living cell. RB4 hat shows increased superoxide, indicating altered metabolism. Our results demonstrate RB4 haploinsufficiency leads to atrial remodeling, including abnormal conduction, increased fibrosis, inflammation, altered metabolism, and reduced contractility. In summary, RB4 expression is reduced in the left atrial patient with persistent AF. The reported AF SNP associated with lower left atrial RB4 expression. RB4 hat mice reproduce key aspects of atrial remodeling, including conduction defect, fibrosis, apoptosis, and metabolic abnormalities. RB4 regulates a panel of genes associated with atrial contraction, conduction, metabolism, fibrosis, and inflammation in mice and humans. Thank you for your attention. Yeah, the question was if I see that any arrhythmia in this mouse model. So, is the gene downregulated in people at risk for AFib or only in people that have AFib? In other words, is AFib causing the gene alterations or is the gene alteration actually causing AFib? I'm just showing the relationship. Thank you for the talk. Is there any homology between ERD4 and ERD2 which is involved in other types of cancer? What's that, homology? Is there a co-association between ERD2 and ERD4? ERB and ERB2? ERB2 and ERB4. Homology. That's right? A close homologue of this is HER2 in breast cancer. I wonder if there's anything about remodeling and fibrosis that you can compare between that mechanism. Those two, ERB2 and ERB4, everything. So, ERB2... So, ERB2 and ERB4 are critical co-heterodimerization partners. Actually, if you knock out... So, the difference between ERB2 is that... Can you hear me? So, ERB2 cannot bind. It doesn't have a ligand binding domain. ERB4 does. But ERB4, once noregulin binds, because it's a receptor tyrosine kinase, not only does it phosphorylate itself, but also provides a docking system for ERB2. They phosphorylate each other and then activate multiple cascades, including the RASMAP kinase cascade and many others. But they're critical components together. One without the other does not function. So, if you knock out any component, same phenotype. There's a study showing that ERB4 loss causes the heterodilation and impaired hetero conduction in the embryo, but not adult. Any more questions? Okay, let's thank the speaker very much. Thank you so much. Thank you. And our final talk for something a little bit different, but nonetheless welcomed. Our last talk is by... Okay. Dr. Jonathan Smerling, who will be talking about non-sustained ventricular tachycardia on long-term halter monitor in previously healthy pediatric population. Thank you. Good morning. Thank you everyone for coming and allowing me to change our perspective a little bit. I would like to share some of the things that we've been working on, specifically looking at non-sustained ventricular tachycardia on long-term halter monitoring in the previously healthy pediatric population. Non-sustained ventricular tachycardia, I'll be referring to as NSVT. I have no disclosures to report. Our study evaluated the XeoMonitor, which is a long-term halter monitor worn up to 14 days that we use at our institution. Xeo was not involved in the design or implementation of the study. Our project was supported by the Heart Institute Research Corps at Cincinnati Children's Hospital Medical Center. Use of long-term halter monitoring in clinical practice is increasing, and its implication on incidental arrhythmia findings is unclear. The incidence of NSVT among undiagnosed pediatric patients is limited. The HRS 2020 guidelines state that MRI, programmed electrical stimulation, and monitoring are reasonable in patients with frequent ventricular ectopy. The 2014 PACES consensus statement stated that patients with PVC burdens greater than 10% should be followed longitudinally. Those with complex ectopy should undergo exercise testing, and an MRI may be useful. There are currently no guidelines for the workup of incidental findings of NSVT on long-term monitoring. We had three objectives for our study. Assess the incidence of NSVT in the previously undiagnosed pediatric population. Assess the association of NSVT on cardiac disease, including arrhythmias, cardiomyopathy, or structural heart disease. And evaluate the utility of clinical testing prompted by finding NSVT on these monitors. We conducted a retrospective chart review of all Xeo monitors placed at CCHMC between August 2021 and September 2024. Patients with ICD-10 codes representing congenital heart disease, pathologic arrhythmias, and channelopathies prior to monitoring were excluded, as well as patients older than 18 at the time of monitoring. NSVT was defined as four or more consecutive beats arising from the ventricle lasting less than 30 seconds. The morphology of NSVT was reviewed by two clinical providers. Patients with identified SVT with aberrancy were excluded from the study. Each patient with NSVT was reviewed for subsequent clinical testing and diagnosis. Variables were tested via Wilcoxon-Rank sum test, Pearson's chi-squared test, Fisher's exact test, and Wilcoxon-Rank sum exact test as appropriate. Cathlin-Meyer curves were then generated to compare monitor wear time in the time to earliest NSVT episode. This is a table comparing patients with and without NSVT. Overall, there were 2,207 monitors included in our study. 62 patients, or about 3%, had incidental findings of NSVT. When comparing patients with and without NSVT, patients with NSVT were slightly older and had a female predominance. Their monitors were more often ordered by electrophysiologists than non-cardiologists, and there was no significant difference based on race. On that same table, comparing patients with and without NSVT, patients with NSVT had slightly slower average heart rates. Patients with NSVT wore the monitor for significantly longer, and patients with NSVT had more ventricular ectopy. This is a table representing what testing was done after the monitor was completed. Due to practice variation, there is heterogeneity among the various testing. 51 patients were scheduled for outpatient cardiology follow-up, 29 had completed EKGs at the time of data collection, 15 had MRIs, 27 had ECHOs, 13 had exercise tests, and 16 had repeat monitors. The green table demonstrates the number of abnormal results, including abnormalities that may not be related. On EKG, 1 patient had pre-excitation and 4 had ventricular ectopy. There were otherwise nonspecific findings. On exercise testing, 7 had ventricular ectopy. There was no VT. One had T-wave inversions that normalized during exercise testing, and 8 patients had abnormal functional capacity, which was likely unrelated. On ECHO, 4 patients had dilation and or dysfunction, 3 of which were confirmed by MRI, and conversely on MRI, 4 patients had dilation and dysfunction, 3 of which were identified by ECHO. On repeat monitoring, 4 patients had NSVT, 2 had SVT, and 4 others had other ventricular ectopy. Of patients with NSVT, we compared those with normal and abnormal testing. There was no significant difference in patient age, sex, ordering provider, race, symptoms, or wear time. There was also no significant difference in the average heart rate, the duration of longest NSVT episode, the rate of the fastest NSVT episode, or NSVT morphology. This is a table comparing normal and abnormal testing among patients with NSVT. Patients with abnormal testing had more ventricular ectopy. Theirs had a shorter duration to the earliest NSVT episode. We then compared patients with NSVT and dilation or dysfunction on ECHO and MRI to those with NSVT alone. There was no significant difference in patient age, race, ordering provider, symptoms, or wear time. We did find that 4 to 5 patients with dilation or dysfunction were male compared to the 33% without dilation or dysfunction. And as before, there was no significant difference in the average heart rate, duration of longest NSVT episode, rate of the fastest NSVT episode, or NSVT morphology. This is a table comparing patients with NSVT and dilation or dysfunction on ECHO and MRI to those without. Patients with dilation or dysfunction had significantly more ventricular ectopy. Patients with dilation or dysfunction also had a shorter time to earliest NSVT episode. This is a Kaplan-Meier plot of hours to earliest NSVT episode. Yellow represents patients with NSVT and no dilation or dysfunction. And blue represents patients with NSVT and dilation and dysfunction. We can see that those with dilation and dysfunction had significantly earlier NSVT compared to those without. Of those with NSVT, one patient is currently undergoing evaluation for suspected cardiomyopathy. Of those with NSVT, one patient is currently undergoing evaluation for suspected cardiomyopathy. Four other patients had dilation or dysfunction and are being followed without a diagnosis. Nine were diagnosed with concomitant SVT, and one was diagnosed with inappropriate sinus tachycardia, as well as one with POTS, though we suspect these are unrelated. The patient undergoing cardiomyopathy evaluation is still completing their evaluation. On their original monitor, they had 16% ventricular ectopy with monomorphic RVOT-PBCs. They had ectopic atrial rhythm on follow-up EKG, but no ventricular ectopy or tachycardia. On echo, they had mild left ventricular dilation and mild mitral valve prolapse. But on MRI, they had moderate right and moderate to severe left ventricular dilation, with mild left ventricular dysfunction. They had reduced functional capacity on exercise testing and continued to have NSVT on repeat monitoring. They underwent an EP study and ablation of RV alpha-attracted PBCs, but had recurrent NSVT and was started on metoprolol. Their cath demonstrated normal cardiac output, high normal RV and LVNI solid pressures, and they had genetic panels that are sent that are still currently pending. In summary, about 3% of previously undiagnosed pediatric patients had NSVT. NSVT was more likely to be captured for patients who wore the monitor for longer, as well as it was associated with ventricular ectopy. Earlier capture of NSVT and increased ventricular ectopy was associated with abnormal testing, and more specifically, dilation or dysfunction. There was no ventricular tachycardia on exercise testing. ECHO identified some, but not all patients with dilation or dysfunction on MRI. And about 1 or about 2% of the undiagnosed patients with NSVT is currently being evaluated for cardiomyopathy. In conclusion, NSVT is a relatively common finding in patients without prior cardiac diagnosis. Practice variation following findings of NSVT makes thorough phenotyping challenging. Short-term monitoring may be sufficient in assessing patients for high-risk NSVT. Exercise testing was not additive in the NSVT evaluation. The presence of NSVT on long-term Holter monitoring for the majority of patients was not associated with the disease, though 8% of patients had some degree of ventricular dilation or dysfunction. Patients with NSVT captured early require thorough evaluation and judicious use of clinical testing to exclude cardiomyopathy, and ECHO is a more accessible method to assess for disease, though may not identify some patients as compared to MRI. For future investigations, we would like to complete the study on a larger scale, as well as with detailed phenotyping of all patients with NSVT. We'd like to analyze similar patients with longer follow-up times to identify clinically significant endpoints, as well as ultimately develop a guideline-directed clinical evaluation of patients with incidental findings of NSVT. Here are some of our references. And thank you for listening. A special thank you to Dr. Czozek, the rest of the EP team, and the HERD teams at CCHMC who helped put this study together. Okay, yeah, questions? Were there some particular features of the NSVT in terms of morphologies or, like, you know, morphology of the PBCs? Yeah, yeah, that's a terrific question. The question was whether there was anything about the morphology of the PBCs or, I'm sorry, of the NSVT that might help us differentiate between the two groups, and unfortunately we didn't find anything. But I'm hoping with a larger sample size, we'll be able to see some more details and more granularity. What was the original reason that we're giving the Holter monitors for these? Oftentimes it was for syncope or palpitations in something that the providers wanted to understand a little bit better. Okay, if there are no more questions, I thank you very much for your talk. And I would like to thank all the speakers. And that will end our session on atrial arrhythmia mechanisms and a little bit more. Thank you.

atrial arrhythmia

diabetes

hypomagnesemia

TRPM7 activation

AFib

atrial structure

ERBB4 gene

ventricular tachycardia

cardiomyopathy