I welcome everyone we're having a webinar today sponsored by our partner pacemate and we're gonna get started in just a second so we're just letting some people log on so let's just give it about another minute we'll get started Okay, we have people logging on, we have a good attendance today, so let me get it started. Today's presentation is Leveraging Analytics to Drive Clinical and Operational Strategies for VT Heart Failure Therapy and Lead Management. Again, it is sponsored by our partner PaceMate. Couple of housekeeping items. Down at the bottom, you're going to see a couple of different links. One is the chat function. The chat is really intended for you to be able to access today's presentation. You can view it and download it for later reference. If you have any questions, you would pose them through the Q&A button. Those questions are going to be monitored. If there are a couple of relevant presentations, there will be three today. If there are any couple of really relevant questions, we'll try to answer those between the presentations, but by and large, we're going to save the questions for after all three presentations. So, again, during the presentation, if you'd like to submit your questions, please do so through the Q&A function. So today, I'd like to introduce Dr. Anne Croman. Dr. Croman is a DO and a PhD, and she is currently a practicing cardiac electrophysiologist and assistant professor of medicine at the Medical University of South Carolina, MUSC. Dr. Croman holds leadership positions at MUSC as the director of the electrophysiology laboratories, director of the cardiac device program and device clinic, and the director of the lead management and lead extraction program. Outside of MUSC, she holds positions in global societies, such as the American College of Cardiology, the Heart Rhythm Society, growth and leadership of women in electrophysiology, technical aspects of lead extraction symposium, and women in extraction group. In addition to her regular responsibilities, she is involved in advancing the medical education of electrophysiology fellows, general cardiology fellows, and internal medicine residents worldwide. I'll let Dr. Croman introduce the other panelists today. So with that, let me turn this over to Dr. Croman. Thank you so much, Chris, for your kind introduction, and I'd like to welcome everyone to our great webinar today. Again, it's on leveraging analytics to drive both clinical and operational strategies for ventricular tachycardia, heart failure, as well as lead management. And I'm thrilled to be able to moderate this webinar. This is a topic that is near and dear to my heart and one that I'm passionate about. And it is one that we are going to see a huge increase and a need for more research and more conversation such as this in the field of electrophysiology. As our patient populations with implantable cardiac devices has continued to grow, and our devices have become more and more sophisticated, we're getting all sorts of new data, new information from these devices that not only we need to manage just clinically what we're seeing, but also try to figure out how we can use this valuable data to really work into our clinical operational strategies and how to really leapfrog and be sure that we're making the most of this data for the patient's patient outcome, as well as for our institutions and programs to really start to drive the needle forward and provide better patient care overall and better ways of identifying patients with particular problems who would benefit from different therapies. So I think all three of our talks today really sort of highlight three very exciting novel new ways that we're starting to use device-driven data to really leverage this intelligent way that we can look at it to really get the best patient clinical outcomes that we can. And I'm thrilled to have all three of the speakers that are here presenting today with excellent talks. They are three phenomenal people and some of my three favorite people in the field of electrophysiology as well. So it is with the sincere and honest pleasure that I'm so thrilled to be able to introduce them. So our first speaker today, and I'll introduce all three, and then we'll go into our first talk. Our first speaker is Dr. George Waits. Dr. George is completing his electrophysiology fellowship here with me at Medical University of South Carolina. And again, he's under the program directorship of Drs. Jeff Winterfield and Marcus Warden here at MUSC. Dr. Waits' background is actually in biomedical engineering, and he graduated from the University of Alabama, completed his internship at Wake Forest and cardiology fellowship at Houston Methodist. Following his fellowship completion in electrophysiology, we're sad to be losing George, but he's going to be joining the staff at Novant Health Cardiology. Apart from his love of cardiology, George also spends time and enjoying spending time with his beautiful wife, Charlotte, and he enjoys the outdoors, live music, chasing, and also being chased by his three absolutely beautiful and adorable children. Our second speaker is another one who is, we are proud to claim, former fellow of MUSC is Dr. Amah Reza Karimianpour. Dr. Reza is a clinical cardiac electrophysiologist, and he's the director of the Comprehensive VT Center at Piedmont Heart Institute in Atlanta, Georgia. Dr. Karimianpour completed his residency training at Cleveland Clinic, followed by fellowship in cardiovascular disease and clinical cardiac electrophysiology at Medical University of South Carolina, where he served as chief fellow in both training programs. His clinical interests are catheter ablation of complex arrhythmia, as well as cardiac device implantation and management. He has authored several peer-reviewed publications, book chapters, and has presented award-winning work at multiple national professional society meetings. He has a special interest in catheter ablation of ventricular tachycardia and with structural heart disease. Dr. Eric Kiel is our third speaker and is also a wonderful addition to this panel. He is a cardiac electrophysiologist with Centra Cardiology Specialists. Dr. Kiel serves as the electrophysiology research director for Centra Cardiovascular Research Institute and the Division of Cardiovascular Medicine for the Eastern Virginia Medical School in Norfolk, Virginia. Prior to joining Centra, Dr. Kiel completed his fellowship in cardiovascular disease and electrophysiology at the Cleveland Clinic, where he also served as chief electrophysiology fellow. He is an active research investigator and has authored and co-authored several works and notable peer-reviewed publications. He is an active member of the Heart Rhythm Society and has recently served in the guideline writing committee for the 2023 HRS, APHRS, and LAHRS guidelines on cardiac physiologic pacing and the avoidance of and mitigation of heart failure. So I'm thrilled to have all three of these speakers presenting on topics today. The three presentations that you're going to see are the real-life experiences of how MUSC, Piedmont, and Centra have leveraged real-world clinical data to justify programs and proactively identify patients requiring possible clinical engagement. These works are all in various stages of publication. So it is with my pleasure, I'm going to now turn the floor over to Dr. George Waite, who's going to be speaking to us about identifying patients for further heart failure therapies with the barostim therapy modality. Take it away, Dr. Waites. All right. Well, thank you, Dr. Croman. It's a great privilege to be here today, certainly a great privilege to be training where I am now for electrophysiology under your guidance and your colleagues, and what a great opportunity it is to be here today. We've got a big group here, and I understand multiple different roles that are being played in health care. And it's just a great opportunity to talk about how we used this tool to identify patients that could potentially benefit from other types of care. So I have no disclosures to share. If we could go to the next slide, please. I'm glad today to talk about treatment for heart failure and a use of the Pacemate data management system to help us use our electronic health record in such a way that we can identify patients who could potentially benefit from a particular type of therapy, which is barostim, baroreflex leveraging therapy for heart failure. If you can go to the next slide, please. And so before I go into the specifics of this presentation, I think a lot of this, and I think what we'll probably see as a common theme through a lot of these initiatives is, you know, we, you know, we all understand, you know, clinic is busy and clinical care is complex. Patients are complex, and we all have roles to play, but limited time to do our jobs. And so when we think about the electronic health record, in many ways, it can be sort of a two-edged sword. It might be something that adds a lot of additional complexity, a lot of additional data that may not be as helpful and may be distracting and may bury important, you know, facts that we need to have in order to help patients. But on the other hand, if it's appropriately designed and if it is, you know, leveraged in such a way, you can find the information you're looking for, you can streamline information that helps you, and it allows you to do a better job, you know, in your role. And so I think, you know, in my role as a clinician, seeing patients through clinic, I want to be able to see who can, you know, who may qualify for particular types of therapy. And you know, as much as I'd like to think I'm a smart guy, you know, there's clearly there's a limit to our bandwidth and sort of the attention that we can dedicate to, you know, to the time that we're having, our limits. So these kinds of tools can help us. And so here I'll explain this particular example. What we wanted to do is automate identification of patients who could benefit from this Barrow Stem Therapy. This was done last fall. We started the initiative. We looked at our patients who already had devices, primarily those with cardiac implantable devices, and we searched throughout our system, and you can see a description here of Medical University of South Carolina. You know, we have several, you know, many physicians over several specialties. In particular, we have nine device implanting electrophysiologists and just under 100,000 patients with wearable or implantable, you know, rhythm technologies that exist within a database that Pacemate has helped us manage for reporting, for alert reviews. And we also saw the opportunity that we could take our electronic health record, which we used, as you see here, we use Epic as many others do, and Pacemate has allowed us to sort of find, you know, particular data points and allow us to extract those data points in order to identify, you know, opportunities for care. So if you can go to the next slide, please. So to kind of lay out the role that we anticipated to play. So you kind of see a schematic in the right side. We're talking about looking for opportunities to treat patients with heart failure. And of course, those of you familiar with the guidelines know that, you know, there's a long list of medications and therapies and things that have shown evidence for benefit in patients with heart failure. In particular, those with severe reduction in their LD function, less than 35%, and those in that sort of middle categories of functional decline, so NYHA class two and class three, you know, you always want to know what's their conduction status, because if they've got a wide left bundle branch block, you can anticipate that those patients may do very well from cardiac resynchronization therapy. That's well established in our guidelines. And so those patients are then appropriately identified and referred for that therapy. But, you know, there are another large group of patients. Here it's estimated by CBRX's internal estimates up to 70%, but, you know, we all know that there are plenty of patients who have heart failure and yet do not meet criteria for CRT because their QRS is narrow, or perhaps it's an IVCD or a right bundle or some other reason why you don't anticipate them benefiting from a CRT. And so is there an opportunity to give them some improvement? And so this is where the idea for Barostim technology arose. And, you know, very simply, if you see the schematic on the right, there's a pulse generator that gets implanted and a lead that overlies the carotid body. And through that system, a series of electrical impulses are sent to that carotid body so as to stimulate afferent, you know, stimulation through the baroreflex leading to a reduction in sympathetic outflow and an increase of parasympathetic outflow, both to the heart and systemically. And as we know in heart failure, you know, it's a multi-system process and many of the deleterious sort of, you know, functions of heart failure occur through, you know, dysautonomia. And so the Barostim was designed and implanted to act against those autonomic trends in heart failure with some benefit. And so we were looking at, you know, of these patients that wouldn't qualify for CRT who otherwise meet these criteria, you know, how many of those patients do we have and who might benefit from Barostim? And so that was the impetus for designing sort of this analytic approach to finding among the patients we're already caring for, you know, who could benefit. So if you go to the next slide, I'll explain more. So here we set out to use the pacemate system to identify these patients, as I mentioned, and my colleagues are listed here under Dr. Croman's guidance. We set out to identify, and these were the, you can see on the left panel, both exclusion and inclusion criteria for patients. This comes directly from the IDE trials for Barostim prior to its FDA approval, you know, that showed, you know, which patients would most likely benefit. So you're looking for folks with reduced EF, less than 35, those that, you know, whose prognosis is probably not too far gone. So therefore you've got an NT-proBNP cutoff as well. And then obviously you want patients who don't have an anatomic exclusion. So perhaps they've got severe carotid plaque or they have known autonomic neuropathy such that their baroreflex is not intact. And, you know, those who are already receiving advanced heart therapy. So this is, as I said, all taken directly from, you know, the trials establishing the role for Barostim. And we codified that in our own extraction algorithm. So we look to exclude those patients in whom the trials excluded, and we look to include the patients who had the appropriate functional class and appropriate EF cutoffs. And so once you design the query, the query could be run in, you know, just a matter of several seconds, and we knew that it wouldn't be perfect, but we thought, well, maybe it'll be pretty good. And so we, you know, through manual review, we could get a sense of how good is it and where did the algorithm sort of fail, where did it fail to exclude patients that it should have. And, you know, what we saw was about what we expected. It was pretty good. We had an 86% identification of candidates who we would have said, yes, they could potentially benefit from a Barostim discussion to see if that's something that they would be interested in, something that would be appropriate for them. And then here you see, you know, reasons why the algorithm didn't quite identify everyone and why we manually excluded them from consideration. And, you know, in three cases, there was a presence of left bundle branch block, which just couldn't account for, and, you know, prospectively there was one patient who happened to already have a Barostim, a couple who had a reduced EF, but then subsequently recovered. And then a few patients who were already receiving advanced heart failure treatments, such as LVAD and heart transplant, and one other patient was a hundred percent already paced. And of course we thought that would be more appropriate for a CRT upgrade, but on the whole, you know, the algorithm was pretty good at identifying those who we could then prompt for a discussion to see if this could potentially be a good treatment for them. So that will go to the next slide. And so, you know, why Barostim, you know, here I've just displayed the clinical values and the operational values. So, you know, while it's not been shown to have any direct benefit for mortality, this study was just published. This is the long-term outcomes from the, from the BHF trial, which is a multicenter perspective randomized control trial showing the benefits of baroreflex activation therapy. And here you see there were, you know, there were statistically significant findings of, you know, increase in exercise capacity, quality of life and functional status. You know, regrettably the, there was a hard end point for heart failure hospitalizations and mortality composite did not quite rise to the level of statistical significance. I think the confidence interval was 1.01. It was very, you know, certainly trended towards significance for even, even heart outcomes. So certainly a clinical value that's, that's been recognized in these trials. And then you can see, you know, the operational values there as well, both in an ambulatory context as well as in an inpatient context. And so you see those numbers were recently updated this year. And again, just shows that there could be an opportunity to deliver this clinical value in the right patients. If you could go to the next slide. And so, just briefly one more to show our algorithm. We would have loved for our entire process to just be this. One where we just apply the query and then generate a report. Of course, we knew that it wouldn't be perfect. I think this is often an iterative process. And so, if you'll advance to the next segment, we did our own manual review. We pulled the demographics and then did our own checks to make sure that they were bearish to candidates. And if you'll go forward. And then again, as I showed you before, these are the patients that we excluded after our manual review, but still yielded a fairly good 86% yield of appropriate candidates. So, we found that that was kind of a good way to get us started in identifying a patient population that could potentially benefit and could prompt further discussion. So, next slide. So, bottom line takeaways from this particular project is that the pacemate allowed us to identify patients who could benefit from Baristin. Baristin can address and can offer those benefits that I showed, exercise capacity, quality of life. But it has to be in particular patients who don't otherwise qualify for other therapies. So, pacemate helped us sort of decipher those things and identify these patients more quickly than we could have on our own manually. So, it was really, it's been a pleasure to work with them to kind of help us with this initiative and others and it's really, I think, again, going back to the motivation, we're all limited with our time, we're limited with our resources. So, why not help us with good computers so that the computers can compute so that humans can be human? And so, I think that's what makes this, I think, an exciting enterprise and this is what we showed. So, with that, I'll pause here and answer any questions that you all have. Thank you for your attention. Thank you very much, George. That was a great talk. And I think it really kind of brings to highlight how using the clinical data from pacemate can really help to identify patients that may benefit from other types of treatment modalities, especially in the heart failure space. And I know that looking at this data, you only identified 86% were true candidates for barostim, but what kind of jumped out at me as well is that we did also find quite a few other patients who were candidates for upgrade to CRT. So, I just wanted to kind of get your thoughts about the utility of using these data and these queries to identify patients that may benefit from a wide variety of treatment sort in that space, either barostim or upgrade to CRT, or even kind of candidates for LVAD as well. And your kind of thoughts on how this could be applied to other types of modalities for heart failure. Yeah, no, absolutely. I think this entire project shows me that the process is very scalable. And so if you really, for any condition that can be quantified, I think so long as, and this is an important caveat, you have to have a database that is, of course, full of accurate data and discrete data elements. And so as easy as it was for us to identify appropriate candidates for barostim, by that same token, yes, if we can reliably report and if we can have the right crosstalk to say, well, what was the last QRS? Or was there ever a QRS that was greater than 150? Was it ever a bundle morphology? If that data can be appropriately made into discrete and sort of searchable data, then those applications can expand as well. And it does open us up to other applications. Where it gets hard is where the data is not as discrete. And so if you're looking for someone who is intolerant of a drug, and unless it's listed as an allergy, and of course, if there's variable ways in which it's reported, if it's reported only in free text, that information, while it's buried in the EHR, it's not as easy to extract and not as easy to show up in a database query. So it goes back to this deeper question of what's the best way to sort of store data? What's the best way to standardize that? Which can always be difficult across different practice types. But yes, very, very scalable. Other applications are definitely there. Again, to the extent that we can quantify our data input, I think to that same extent, we can expedite the way that we use it to help people quickly. Perfect. Perfect. Wonderful. And that's a great kind of takeaway into the next big area that we absolutely can work to kind of understand patients and get patients and that may benefit from additional therapies. So very exciting way to look at ventricular tachycardia. I'm gonna turn it over to Reza. So Reza, please take it away from here. Thanks so much, Anne. I appreciate the opportunity and the privilege to present today. And thank you for the introduction and congratulations to Dr. Witts for the fantastic work that he has done so far. So let's go ahead and get started. Next slide, please. So I'm a cardiac electrophysiologist at Piedmont Heart Institute. And Piedmont Heart Institute is the primary cardiovascular services provider for Piedmont healthcare. And we are ranked based on 2022 to 2023 data as the number one heart center in the state of Georgia. Piedmont Atlanta Hospital, which is our comprehensive hospital hub is just over 600 beds and growing. We have a multitude of specialties, physicians, APPs. We perform an entire breadth of medical services to the community here in the Atlanta area and also more broadly in the state of Georgia. More importantly, as it relates to what we're discussing here today is in the past year, we've performed more than 80 heart transplantations and more than 60 LVAD implants at Piedmont Atlanta. And our cardiac monitoring solution is again, pacemate live in conjunction with our EHR solution, which is Epic Systems. Next slide, please. So before we begin, I think it's important that we recognize that heart failure and structural ventricular tachycardia, structural VT and cardiomyopathy are all on a very complex disease spectrum. And this graphic here shows that patients who have structural heart disease in relation to what medications they're on the stage of their heart failure, what sort of psychological trauma that they may endure and their socioeconomic status, all of that is very tightly tied with each other. And so as they progress throughout their disease course and their disease spectrum, all of these patients must be treated in different ways. They must be dealt in different ways. And so this is the most common problem that I think we have in our healthcare system is that different specialists will focus on different healthcare issues for the patient and without looking at how all of this ties into the entire picture, into the global picture for the patient as they are on this disease spectrum or for any disease spectrum for that matter, you know, what medications they're on, what specialists are seeing, how many times are they in the hospital and the ICU as they progress to eventually in this disease process to advance therapies, heart transplants, LVADs, or certainly, you know, the less favorable outcome. Next slide, please. And so to achieve this, I think that it's very important that we have two components to any sort of a successful VT program. One is that we must have a multidisciplinary outpatient VTC heart team, if you will, that'll consist of the patient's primary electrophysiologist or the cardiologist, our electrophysiology team here within our institution, a nurse who does antiarrhythmic drug monitoring for our patients, our palliative care specialists for those patients who we certainly need to engage to discuss goals of care, our ICD support group, psychiatrists, our device clinic, certainly for management of their device issues, our advanced heart failure colleagues and our cardiac surgeons and so on and so forth. But these are the core members of the group that's necessary to manage these patients under this sort of multidisciplinary heart team approach. Next slide, please. And for those patients, can we advance the slide, please? And for those patients who are sicker and they end up in the ICU in the hospital, we have a modified version of this team. This is the core VT storm response team that includes the primary cardiologist who has known the patient for a long period of time, who will be able to give us insight as to where the patient is on their disease spectrum. And then we have the operators, our APP team that sees the patient on a daily basis, our ICU teams, the palliative care specialists and the advanced heart failure teams. And being able to work cohesively as a unit to make sure that the decisions that are made for these patients who are some of our arguably the sickest patients that we have, that the right steps are taken and the right therapies are provided to these patients. It could be anywhere from escalation of medical therapy to early referral for advanced therapies for heart transplantation and LVAD. Next slide, please. And so when we came up with our comprehensive VT treatment center here at the Piedmont Heart Institute back in November of 2022, the vision was to develop the first in region, three-tiered multidisciplinary comprehensive referral center for ventricular arrhythmia management. And having access to data, retrospective data from cardiac devices integrated with data from the electronic medical record is the core of what this treatment center is. And it allows us to identify patients who are high utilizers of the healthcare system, identify patients who would benefit from therapies for VT such as catheter ablation or even referral to our ICD support group, mental health services, and then furthermore, early referral for advanced heart failure therapies. It also allows us to support on the go clinical decision-making in a busy practice with many patients on the consult service or in clinic. It's important to be able to take all of this data retrospectively and look at it in a way that we can make a prospective decision on what to do with some of these patients that we are encountering and to support our clinical decisions within one glance, know that this is a patient that is better off referred for advanced therapies, or this is a patient we should provide VT ablation to treat their VT or perhaps medical therapy. And so not only does it support this on the go clinical decision-making, but it also allows us to longitudinally assess the patient care quality. How are we doing? How are these patients doing over the course of time? Are they getting better? Are they getting worse? Are they about the same? When we make one intervention, either catheter ablation, medication, or what have you, how did the patient do long-term? Being able to use that data, big data, in a way that's digested and allows us to longitudinally assess our patient care quality is very important. And then finally, in the future, being able to benchmark ourselves against other programs as a measure of program success. Next slide, please. And so I think it's important to actually see what some of these interfaces that we've created look like. This is a screenshot from a sample patient, John Doe, and we can see the patient's demographics followed by their heart failure metrics, what their ejection fraction is. Do they have an LVAD? Are they on certain medications? How is their device programmed? What medications are they on? Have they had an ablation before? What are their latest lab values? And then being able to put that together to calculate even something such as the PAINS-D score, which allows us to estimate or anticipate what the patient's risk of hemodynamic collapse would be during VT ablation procedure. And so being able to look at this and make a quick decision or understand where the patient is on their disease spectrum from a clinical perspective is priceless. Next slide, please. And when we scroll further down, we can see the longitudinal manner of the display of the data, data that was otherwise lost, data that was otherwise gibberish, if you will, because of the multiple reports and sequences that was uninterpretable now put into a graphical format. So we can see that the patient really struggled in December of 2023, the sample patient with ICD shocks, ATP. And then further down on the third line here, we can see that in 2023, they had multiple hospitalizations for urgent and emergent causes. And that I think to me helps us as clinicians understand that this is a patient who is getting sicker over time. This is a patient that we need to make a move on now before it's too late. Next slide, please. And so some of our outcomes and results here as since the inception of our program, we've enrolled just over 200 patients and almost 300 patients actually in our BT center divided into tiers. The tiers represent the acuity of their disease, how much ventricular tachycardia they're having, recent hospitalizations, so on and so forth. We've been able to follow these patients longitudinally. We've been able to perform a quarterly review for updates and also monitoring of quality of antiarrhythmics, which has been a very big topic of discussion in our organization, patients who may need more intensive antiarrhythmic monitoring and overall patients that need monitoring of their disease course itself before it's too late for transplantation or it's too late for LVAD implantation. And so the numbers shown here, our structural BT volume as a result has increased from the end of 2022 to 2023, our structural BT ablations, this is excluding PBCs and idiopathic BT ablations, but more so patients with structural heart disease undergoing ablation, those numbers have increased dramatically and year to date 2024, we're almost at the same numbers we were before the inception of our program. So now by the end of the year, I think we're going to clearly double this number. And one thing that I'm not shown here on this slide is something that's extremely difficult to calculate. And that's the downstream cost opportunity of reducing unpaid readmissions, long hospital stays, taking beds and capacity for other patients who may need to be admitted to the hospital, who may be boarded in the emergency room. And then furthermore, other opportunities for revenue, generating revenue, such as patients who need advanced cardiac imaging, cardiac MRI and CT, who are then going to be referred for catheter ablation. These are very difficult things to track and measure, but certainly these are again, cost opportunities and profit opportunities that when we put together a comprehensive center, it allows us to reach a higher degree in quality of care. Next slide, please. And so our future directions, our goal is to increase our regional referrals for complex BT management within the state and outside of the state. What we are interested in is benchmarking ourselves against other BT centers of excellence to see how we are doing ultimately. And only with a program such as Pacemake can we do that. Are we able to really compare ourselves, our rates of ICD shocks and ATP and therapies, BT burden, use of antiarrhythmics, mortality, hospitalization across multiple centers using one platform, and that is Pacemake. And furthermore, the early intervention and the interception of the disease process that I reviewed earlier in the presentation and identifying those high-risk patients to avoid the frequent hospitalizations, the delayed treatment, and ultimately the unfortunate demise. And with that, I'd like to thank you for your attention and we'll open it for questions. Thank you. This has been incredible work that you've done in just a short amount of time just to build such a world-class exceptional program. So congratulations on that. That's phenomenal. And I'm sure, just to ask you a little bit about how you think the direction the program is gonna go. Obviously, it's on a steep curve upward. And then also, is Piedmont doing anything to sort of help to try to monitor these operational outcomes for you and your center? Or what do you think future opportunities with that would be? Absolutely. I think the future is really broad in terms of how we can utilize this data and other data, even from intraoperative data standpoint to know in terms of costs, how much is each procedure costing the institution or generating revenue for the institution? And more importantly, from a clinical standpoint, monitoring the outcomes of these procedures to be able to demonstrate that, yes, PT ablation works. Who does it work for? And what population of patients? And how are we helping reduce not only the disease burden for our patients and reducing ICD shocks, which are painful, for example, but also being able to prove the benefit long-term in reducing ultimate demise from a complex disease process, their cardiomyopathy. And so we at Piedmont Heart Institute, we track all of these metrics very closely. This is only one of the metrics that we look at, but we track all of our metrics from our EP lab and our consultative services. And we review this and we discuss some of these details in our monthly meetings to see how we can do a better job of clinicians and also from a systematic standpoint, where are some opportunities where we can better the way that we do things as a, as an institution, as a hospital, as a group of specialists that have the same interest in mind. Absolutely. Wonderful. And thank you so much. So super exciting to see what all comes out of it in the future. Definitely. And then for our last talk in segment, Dr. Eric Kiel is going to discuss patients being able to be identified that would benefit from lead management or extraction. So I'll turn it over to Eric, if you want to take it away. Thanks, Anne. Great talks, Reza and George. I learned a lot actually in the last 30 minutes, so thanks for that. So basically this is just a kind of more in process talk, I would say, than, than George's and Reza's in terms of, you know, where we are in terms of, you know, implementing this, this pacemate algorithm. But basically, Sentara is a large healthcare organization within the state of Virginia. I think we're the biggest employer, last I checked, in the state of Virginia. We span all the way from DC actually to the Northern kind of border of North Carolina and kind of the Western, Western Virginia. And so there's a bunch of different healthcare systems. And when I just look at our specific group, we'll get through to kind of the data, but actually probably across our entire system, there's double the amount of electrophysiologists. And so we've built a pretty large lead management program here. And the goal was how can we basically utilize pacemate to try and kind of improve outcomes and identification of patients that would qualify for lead management in a timely fashion across a variety of different indications for extraction. Next slide. Okay. So these are my disclosures. I'd say just pertinently, I think for this, I do do a fair amount of consulting for Philips, which is one of the major lead extraction companies, and then have been doing some work with pacemate as well. Next slide. So in terms of kind of the goal here, so basically we're trying to leverage the analytic capabilities of pacemate to identify patients who could benefit from proactive lead management. And the primary indications for lead extraction, which is when we say lead management, we're really talking about here is device infection, which we'll spend a fair amount of time talking about or lead malfunction. So it could be lead fracture, it could be insulation break, it could be a need for an upgrade to a resynchronization device or defibrillator with known venous occlusion. So from an organizational stat, this is just kind of our own region within the Norfolk and Hampton Roads region. We have about 9,000 device patients across 12 implanting EP providers. But if you look at the entire group of Centera across the state of Virginia and kind of Northern North Carolina, it's a lot larger than that. And so if you just kind of think in a broad term about how much time do we all have just in terms of clinic visits to try and follow up these patients, there's more patients than there are slots and follow-up across APPs, providers, device nurses. And so just as you've seen in George and Reza's talks, I think we're going to have to rely more and more on analytics to try and identify and risk stratify and kind of prioritize and triage patients. We, similar to the other speakers, we use Epic as our electronic health record and pacemate is our device management software. Next slide. All right. So we currently have a pandemic right now within CID infection that I think is, over the last couple of years, gotten a lot more publicity. But basically there's a nice paper out of Duke by Dr. Percorny that basically queried a large Medicare database for patients who had CID devices and were found to have device infection and class one indications for lead extraction for full system removal. And what they went through and looked at was number one, how many of these patients got extracted? And if you got extracted, how quick was it from the time you were diagnosed to the time you're actually extracted? So they stratified by less than seven days within a month or no extraction within 30 days. About 80% of the patients didn't get extracted at all. So the KM curves you're seeing on this graph are just the 20% of patients who did get extracted. And what you can see is the cumulative mortality is there's pretty early separation of survival curves based on timing of extraction and then presence of having received an extraction. And if you think through what interventions in medicine we undertake that have a five or a 10 or 15% absolute risk reduction in mortality at one year, there aren't that many. And so in the lead extraction space, we talk a lot about door to balloon time is something that everyone understands about STEMI. Well, door to extraction time is probably the thing you're going to see in the next set of guidelines through HRS looking at extraction. So there aren't just mortality benefits, which is obviously a huge thing here. There's morbidity benefits to early extraction, minimizing long-term antibiotic use, shortening length of stay, reducing downstream hospital visits and repeat hospital admissions. Can't tell you how many times patients get to us for the lead extraction after their fourth admission for Staph aureus bacteremia. And that's what this kind of initiative is trying to fix. And if you look at costs, actually lead extraction as a procedure is an extremely valuable and profitable procedure for hospitals. And so if you can extract based on this data, if you can extract earlier, you actually have a significant amount of cost savings on a procedure that that's actually quite profitable. Next slide, please. So again, this is kind of more work in progress than something we've implemented. So kind of just showing you kind of underneath the hood early on, and I don't want to take full credit for this as it's been kind of a multi-center initiative and has been working on this at MUSC and John Andrew Lee over at Cooper Health had been involved in this too with Maggie Sabo from Pacemate. So I want to say thank you to her if she's on the line. So basically the concept here is what we're trying to do is link epic data with Pacemate to identify patients that have a implantable device, have had a recent hospitalization or are currently hospitalized and have received an antibiotic from a list that we generated during said hospitalization and have a diagnosis on a watch list. And what you can see here is kind of a working query of what that's generated. I think there's about 20 patients within our system that it generated recently. What we've done as we've kind of gone through this is we first kind of just used all those and used a wide swath of antibiotics and found out that, you know, probably some of these antibiotics are a little bit, they're not narrow focused enough. We're really focusing on class one indications for extraction, so strep and staph. And so you can see a list of the antibiotics there. The ones I have starred, I think are ones that maybe we need to spend a little bit more time looking at in order to try and optimize signal versus noise. You know, cefepime, ceftriaxone are commonly used drugs for community acquired pneumonia or, you know, gram negative sepsis, same with peptazo. You know, the vancomycins and cefazolones of the world are probably more likely to be what we're actually looking for, but we also don't want to miss things because somebody is being treated broadly. And you can see the diagnosis watch list there. One of the things in kind of an earlier iteration that we noticed is when we were kind of just looking at just hospitalizations, we were actually catching our own device changes. So patients who had received say cefazolone during a recent outpatient hospitalization for a generator change. Well, that's not what we're kind of looking for. We're looking for a diagnosis. And so that's kind of where we're at now on that. We'll talk about where we're going in the future, but the big nugget here is trying to integrate Epic to actually give us culture data. And one of the issues we have, you know, with trying to identify these 80% of patients that we're missing is where are they? So, you know, Piedmont's a great example. Sentara is a great example. I think MUSC is another great example of kind of large health networks that geographically span a really large network. And so we as the EPs that extract can't be in everyone's chart. And so we need to have an electronic medical record device record that can span across lots of different healthcare systems and alert us to a patient that may be at one of our outlying facilities that should be a candidate for extraction. And so how do we do that kind of utilizing a holistic approach, understanding that different hospitals might have different medical record systems. This is obviously only going to work with our current Epic system, but maybe we could use it to integrate with other medical record systems in the future. You know, Epic has care everywhere, so it may allow us to look at different health systems that may be in the area, say, if our patient was admitted to a different healthcare system. So the reach of this potential project is, I think, vast. Next slide. So that's the lead infection part. And I'll show you some data kind of from our own experience in a little while, but most of the extractions that we do are not for lead infection or device infection. They're actually for malfunction and occlusion. And so we have a bunch of data about lead alerts. And so, again, it's all about trying to figure out the signal versus the noise. We started by focusing on RV lead alerts. And what you can see is there's a lot of alerts in the system. So we need to kind of winnow that down more, but we're working on kind of impedance out of range to look for fracture or insulation break. Trends over time that might be worrisome, that this is not a lead that's going to be lasting very much longer. High voltage alerts for lead fractures, say, on a defibrillator lead. Right atrial leads are certainly an area we're going to be looking at soon to try and discriminate alarms for atrial lead noise that are kind of mischaracterized as atrial fibrillation versus a true lead alert that may cause inhibition of pacing in a patient with sinus node dysfunction. So, again, very much in the early iterations, but you see we can analyze all this data. We can trend it out. And I think it's going to be extremely valuable for all of us that are lead extractors. Next slide. So, you know, real-world operational values. So just kind of using our own experience. So I came from Cleveland in 2019. At the time, our lead extraction program was primarily run by cardiac surgeons for a variety of reasons. We're happy to kind of give the lead extraction program over to us from EP. And you can see from 2019 to 2024, our volume has actually gone up by sevenfold. And so how do you get there? Well, you can analyze your market. So in the kind of top left, you can see a map of Virginia. And in the very bottom right with the big blue circle, that's where we kind of are from a geographic perspective. But again, we span the whole state. And the blue circles are accounts that have laser for extraction. And the red accounts are devices where possible systems where devices are implanted, but there is no laser available. Now you could extract with non-laser tools with mechanical tools, but in general, most extraction centers have both available. So it kind of shows you where there's a dearth of extraction capability and it tells you where the catchment area probably would be. So uniquely for us, we're a big center with a little laser and there's no one else nearby in a large metropolitan area. And so I think that's probably why we've seen a lot of growth. But if you look at the extractions by category, what you'll see in that pie chart in the bottom right is that only about 20% of those extractions, and I think that data was from like 2022 or 2023 were infections. So you're only looking at somewhere on the order of 20 to 40 infected devices. And if you just look at the sheer number of devices, we follow and the percentage of patients per year that have infection, we're clearly missing infections. And so that's why that's kind of the low hanging fruit we need to go after. I think Pacemate allows us to leverage this data with Epic, and it's going to allow us to, I think, save lives, save money in the infection space, and I think improve outcomes in the non-infection space. But lead extractions are really, truly a multidisciplinary team. It requires cardiac surgery, cardiac anesthesia, hospital administration, infectious disease, device nurses, device representatives. I'm a big Michigan fan and there's that old saying from Bo Schembechler that it's all about the team, the team, the team. And so I think this is a tool that really helps us as a team. Next slide. So I think takeaways, I think earlier identification of lead infection can facilitate more rapid lead extraction, can improve morbidity and mortality while decreasing healthcare costs and utilization. In a modern era where we've got limited resources, limited available slots to be seen in office, we're going to be relying on these types of digital workflows to try and identify patients and streamline and triage care. So the big thing is integrating blood culture data is going to allow us to really get this down to an optimized signal algorithm. And I think we need to call the impedance alarms, I think, on our early work for non-infection devices, but I think that's going to be extremely valuable long-term. I think the question long-term is when we get these alerts, how do we as a team integrate them across multiple systems and hospitals to try and improve this door to extraction time? And I think automated alerts from Pacemate are going to help a fair amount. Next slide. All right. We'll take questions. I just thought it's fun to show pictures of your kids. So that's my last slide. Thank you, Eric. That was wonderful. And I agree. I mean, I think for those of us in big extraction centers, this type of thing is something that keeps us up at night in trying to figure out who are the patients that we're missing, that we need to obviously clearly identify earlier on in their course in an infectious etiology, as well as any ways that we can potentially have early identification of lead malfunction before we start getting to the inappropriate shock realm as well, or loss of pacing, anything that's kind of more on the detrimental lead. Absolutely. Just one question for you. I know when George was presenting, he talked a little bit about kind of the scalability of the overall data. Just wondered kind of what your thoughts about how using Pacemate data and the integration between Pacemate and Epic has helped you grow your program and make everything more scalable, essentially. Yeah, I think it helps identify, like I said, patients. And I think from an early perspective right now, actually in the more non-infectious category, it's allowed us to identify patients earlier. I think as patients are getting ready for generator change and we see a trend that we don't like, you can have a proactive discussion to your point of, should we extract this lead now, or should we extract it in five years? We should probably extract it now. Today is going to always be the easiest day to extract this lead. I think in terms of scalability, the infection part, I think may make us too scalable, actually. I think my guess is we're probably missing just the same percentage, even though we're pretty adamant about trying to reach out to other folks about infection. I think we're probably missing 80%, just like you guys are. And so if you say, what would those volumes do to the hybrid operating room and available resources? I think once we identify the patients, we're going to have to figure out where to put them. Yep, absolutely. I agree. And then just to swing it back to Reza, just kind of any thoughts on your kind of scalability or growth for the VT program that you've started and are doing great things with there? Great question. I think from a scalability standpoint, I think I would like to take it back to the benchmarking and being able to utilize our data and data from other institutions to see where we stand and the quality of the care that we're providing to our patients. And so when it comes to sort of a cloud-based technology that is able to take data from various other institutions in that way, I think is the only way to make it scalable. And I think the opportunities are definitely there. And furthermore, to be able to scale even within our region and be able to enroll our patients, which is something that we are already doing. Patients who are referred to our institution for VT ablation or that land here for other reasons, for advanced therapies, for heart failure, they're enrolled in our system. We keep track of these patients to see how they do over time by enrolling them into our basement registry. Yep, absolutely. No, that's a great point. And I would just like to thank all three of our presenters for just excellent, fabulous presentations. I think it really kind of highlights just some unique and exciting time in electrophysiology, especially in the device world, as we kind of parlay all of the data and information, clinical data that we get to better serve our patients and to continue to build excellent clinical programs as well. So I'd also like to thank Pacemate and MedAxiom for giving us a chance at a platform just to kind of speak about things that I know all three of us, or all four of us, are very passionate and excited about and be interested and excited to see all the future research and where this develops and goes. Thank you, Dr. Croman. Just for our audience at MedAxiom, we will be recording, we have recorded this and we'll be making it available. I want to thank Dr. Croman, all the panelists, and Pacemate. I really appreciate it and thank you for joining us today. With that, we'll close it out. Have a great day.

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