Welcome to this session on HRS Health Equity Abstracts and the award session. Really a pleasure to have you all here. Thank you for coming. Welcome to San Diego. John Su as the chair of the Health Equity Council and Camille Fraser-Mills from Duke, also as our vice chair. Really a pleasure to have our award presenters here and thank you so much for attending. Our first speaker is Dr. Aguni. Did I pronounce that correct? Yeah. Great, thank you so much. Really looking forward to having you. I'll get started your talk on gender disparities and implantable CIDs in regards to utilization among patients with end-stage heart failure. Thank you, Dr. Aguni. Good morning, everybody. My name is Kayode. Thank you for having me. I'll be presenting our topic on gender disparities in ICD implantation among patients with advanced heart failure, ICD-10 code end-stage heart failure. So the rationale and background of this is heart failure reduced ejection fraction is very well known to predispose individuals to significant arrhythmias, ventricular fibrillation, ventricular tachycardia, and sudden cardiac death. Annual incidence of VTAC in these patients is about two to 5% yearly. That's the estimation. ICDs are the cornerstone against sudden cardiac deaths in IFRF. Sometimes that comes as a CRTD as well. So our objective was to evaluate the presence of economic, demographic, racial, and gender disparities in ICD utilization among high-risk patients with end-stage heart failure. We chose end-stage heart failure because this is an ICD code end study. And anybody with end-stage heart failure is FRF to the end. And usually this patient, based on guidelines, should have a pre-existing ICD at least. S et al. in 2016 found women and minorities to be less likely to receive ICD counseling and implantation. So even the discussion at all is much less. In Canada, Bousselier et al. found just 50% of the women compared to men had ICD implanted. And we just wanted to see, compared to 2016, is this replicated in U.S. contemporary data, recent U.S. national data? So our study design, we used the most recent NIS sample database as at when this was done, 2021 inclusion criteria, adults with an ICD 10-code, I50.84, end-stage FRF. Outcome was ICD implantation, new and existing, including CRTD. So as not to miss that subset of patients with CRTD and no ICD codes. Software stata, 18.0 basic edition. So our gloss over that is because of time, or statistical analysis, much of what we did, check for normality, use the chi-square and t-test to compare baseline characteristics. And the main point here is we compared ICD implantation rates, including CRTD, using those risk predict, those income predict, those gender predict. And we adjusted each one for age and co-morbidity status. So the Charleston and the Ellicks House, these validated tests for how sick a person is at baseline. If the score is high, the person is sick. If the score is lower, the person is less sick. Finally, we did some form of sensitivity analysis where basically if a person has ventricular tachycardia in the past, they are more likely to have been implanted. So we adjusted for the rate of arrhythmias, VTAC, PVCs, and AFib, just to make sure we adjust for as many confounders as we can, based on the limitations of this kind of study. And also adjusted for co-morbidities. So these are our result baseline characteristics. We had 34,820 end-stage alveolar patients, 68% male, 32% females. Roughly 25% of them had newer existing ICD, that is including CRTD. This is, as we'll see in the next page, this is actually very underutilized, considering the rate of VT in this patient, because these are end-stage patients. 63 versus 67, so ICD participants were younger. So I hope we're able to see this slide. But basically, the points in bold are the most significant. People implanted with ICD were younger. For some reason, they had higher scores of co-morbidity, that is they were sicker. They had higher rates of arrhythmia. So as we see here, this is 34%, which is quite high. And by the time you use a combined rate, this is 25.6%. We also just checked substance use disorder, mental health disorder, if there's any difference. There are really no differences between these groups. So gender disparities. Males are 30.74%, females, 21.96%. These, first of all, this is very underutilized, considering the rates of VT we just saw in the previous slide. The difference is, at baseline, looks like it's not significant, but this is a 10% crude rate difference, and a relative difference of 31% after adjusting for age, and co-morbidity status at baseline. Table 2B will be the most significant on this page. To the left, we have differences, the Hoss ratio without any adjustment. In the middle, we have the Hoss ratio adjusted for age and now sick data at baseline. And to the right, we have the adjusted OR after sensitivity analysis with VT rates and arrhythmia rates. And we see that even after we adjust for previous arrhythmias, there still is a 25% difference. The reasons for this, I believe, might be multifaceted, but we can only speculate at the moment. So compared to racial disparities, we didn't find any disparities significantly, but blacks had higher rates of implantation. And this is significant if we look at the 2016 study that showed they had lesser rates. The interpretation I can give this would be effective QI movements, effective policy movements, and also the fact that blacks have been shown by studies to have higher rates of significant arrhythmias in health failure. So this is just a reiteration of what we saw in the last slide. Implanted, so this is something else we found very significant. Income disparities were not significant. So healthcare utilization project, HCOP stratifies people into income quartiles where the highest quarter is the wealthiest, that's Q4, and Q1 is the lowest income. And using Q1 as the reference, we found no significant difference in ICD implantation rate. This may be due to also, like we said before, ongoing efforts nationally to try to have equity-focused initiatives. So our conclusion is women, because this is the most significant. We found women with advanced FRF are significantly less likely to receive ICDs for protection against malignant arrhythmias and sudden cardiac deaths. This is an area of education and public health initiatives. And we found no economic disparities as well. So implications for practice, this would be to reinforce guideline-based referrals regardless of gender, to standardize ICD counseling in hospital protocols, and encourage equity-focused quality improvement in AP programs and referral channels. Yeah, thank you. All right. Good morning, everyone. I'm Parisa Asher, and I'm honored to be part of the health equity session. I'm currently an undergrad at Duke, majoring in biomedical engineering and biology with a vested interest in population health outcomes. And our presentation is pertaining to geographic and demographic patterns on mortality from heart failure patients with comorbid AFib. Since this was a retrospective analysis of an open access dataset, no external funding was needed. So in terms of the background, AFib is the most common sustained cardiac arrhythmia in the United States, affecting around 2 to 9% of adults and significantly increasing the risk of other factors like stroke, heart failure, and death. Meanwhile, heart failure affects more than 6 million Americans and frequently coexists with AFib, forming some sort of a bidirectional relationship that worsens with clinical outcomes. For instance, AFib can reduce ventricular filling time and can cause irregular and rapid heart rates, leading to such as impaired cardiac output and worsening of heart failure symptoms. On the other hand, heart failure can lead to atrial stretch, increased atrial pressure, and fibrosis, all of which may promote atrial electrical remodeling and the persistence of AFib. So in terms of what we already know, despite their clinical overlap, national mortality trends where heart failure is the primary cause of death and AFib as a comorbid contributing cause have not been well characterized. And prior studies have often focused only on AFib as underlying causes, underestimating the true burden of heart failure-related deaths in the comorbid population, as we'll see later with results. Moreover, disparities by sex, race, geographic region, and urbanization level remain underexplored, and it is essential to understand these demographic and geographic trends in order to inform equitable data-driven prevention and policy interventions. So we approach this with three main objectives. First, evaluate the national trends in heart failure-related mortality among U.S. adults with comorbid AFib as a contributing cause during the past decade, 2010 to 2020, using the CDC WONDER database with mortality data extracted from the National Vital Statistics System. Then, characterize differences in age-adjusted mortality rates, or AAMRs, by sex and race, and identifying demographic disparities through heart failure and AFib mortality. And then finally, assessing geographic variation across various U.S. census regions, states, and urbanization levels. So per our methods, we extracted mortality data from the CDC WONDER multiple cause of death database, and we decided to include individuals age 35 to 84 with heart failure using the following ICD codes as the underlying cause of death and AFib listed as a contributing cause. And age-adjusted mortality rates per 1 million U.S. standard population were calculated. And joint point regression was used to assess temporal trends and estimate annual percent change. And again, analyses were stratified by demographic factors like sex, race, and further by geography. So this was a somewhat analogous Table 1 of what we were analyzing in order to provide some context. We had a relatively even distribution of males and females, but I do want to highlight, especially with the race, we do have to acknowledge that more than 84% were white individuals. So we'll keep this in mind as we analyze the results further. Meanwhile, there's some, there's a little bit more even distribution in terms of geographic region and urbanization level, but race was the most noted aspect from this table. So first starting off with temporal analysis trends. So between 2010 and 2020, the age-adjusted mortality rate for heart failure-related deaths with AFib more than doubled, increasing from 6.3 to 15.3 per million population with an annual percent change of 10%. Furthermore, cumulative age-adjusted mortality rates showed males exhibiting a higher value compared to females, 10.5 versus 8.6. And then further looking at annual percentage changes showed that males had a higher annual percentage change of age-adjusted mortality rate of 11.42% compared to females, 9.3%. Now when looking at mortality patterns by race, we did notice, as acknowledged earlier by table one, they were a small minority of the dataset we analyzed, but they had the highest age-adjusted mortality rate at 11.8 per 1 million, followed then by Native American and white individuals, and then Asian individuals. So these findings really highlight the importance of substantial racial disparities in heart failure and AFib-related mortality, with African Americans bearing a disproportionately high burden. Then we looked at geographic variation and mortality, and at the regional level, we noticed that the west region reported the highest age-adjusted mortality rate, followed by the south. But there can also be a lot of variation within the same census region, so I decided to look at a heat map analysis by state. And when we look at the state level, we see some noted elevated age-adjusted mortality rates in states like Oklahoma, Washington, and Mississippi. And this can kind of give us an insight as we can use this to analyze differences maybe in healthcare access, socioeconomic status, cardiovascular risk factor prevalence, and regional healthcare infrastructure. And then finally, we looked at varying levels of urbanization levels, in order to assess if there was a rural-urban disparity when it came to age-adjusted mortality with heart failure and comorbid AFib. So this was highest in micropolitan and non-core rural areas, reflecting the disproportionate burden in less urbanized regions. Meanwhile, large central metropolitan areas reported a lower age-adjusted mortality rate, with the lowest rate observed in large fringe metropolitan regions. So these trends do suggest that rural populations face a heightened risk, potentially due to reduced access to specialty cardiovascular care, delayed diagnosis, or higher prevalence of comorbid conditions. So what can we really gather from this study? Heart failure mortality in patients with AFib rose sharply during this past decade, with the steepest burdens observed among men, African-American individuals, residents of the Western U.S., with some exceptions like Oklahoma and the Midwest, and those living in micropolitan and non-core rural areas. And these trends are important to really analyze, as they can reflect systemic gaps in access to timely diagnoses, rhythm control, and heart failure management, particularly in underserved and structurally disadvantaged populations. It's important to have targeted AFib and heart failure management strategies in high-burden states and rural communities, such as through expansion of telecardial access, telecardiology access, mobile screening platforms, and culturally-tailored patient education. Further studies could include evaluating longitudinal treatment patterns, hospitalization trends, and access to specialty care. And a future aspect could incorporate predictive modeling using individual-level EHR data in order to enhance early intervention in high-risk populations. Thank you. Thank you. Any questions from the audience? I have one. So from the data that you collected and presented to us, what would be your next step related to targeting, improving the outcomes in these populations? Right, of course, that's a great question, because this was just mainly a retrospective data analysis. I think it's also important to, first of all, apply this in local cohort settings, too, because then again, there's a lot of variability with using retrospective analysis from this national database. So starting from there, but then also eventually leading some sort of quality improvement program, I'm part of a hypertension education access program. So that's just an analogous example of through maybe certain factors that we can target, like maybe education or existing heart failure among patients. When they're aware sometimes, does that help improve outcomes? Again, the limitation is that since this was more so a retrospective analysis, there could be a variety of factors contributing to varying levels. And I think an important part is also just awareness of the condition among the hybrid populations. Thank you very much. Thanks so much. Is Dr. Sati here? Yeah, hey, Dr. Sati. Really welcome you to give your talk on social determinants of health and disparities in diagnosis to ablation time for atrial fibrillation. Hi, everyone, good morning, thank you so much for being here. My name is Dhanushal Davsati, I'm the Atrial Fibrillation Research Fellow working at Johns Hopkins Hospital. I'm really excited today to present our research with all of you regarding the relationships between social determinants of health and disparities in diagnosis to ablation time for atrial fibrillation. So I'd like to begin by giving you all some background info about what social determinants of health are. As defined by the CDC, these are the various known medical conditions or factors in which individuals grow, live, work, and age. Numerous studies have showed that social determinants of health, or SDOS, they have a significantly higher impact on the healthcare outcomes in the general population, but unfortunately little has been done in terms of evaluating their impact on AFib healthcare and AFib outcomes. And we all know, I'm sorry, the CDC defines social determinants of health into five major domains, education, access, and quality, healthcare and quality, neighborhood and built environment, social and community context, and economic stability. And these days, researchers are trying to create a polysocial risk score similar to a polygenic risk score, so that could be included into several different studies to study the health outcomes. And as we all know, atrial fibrillation is the most common arrhythmia, affecting over 60 million people worldwide and can lead to serious complications, substantial morbidity and mortality. And catheter ablation is a guideline-recommended therapy which improves outcomes. But timing matters a lot. Numerous studies have shown that diagnosis to ablation time, the time from the first AFib diagnosis to when a patient gets their ablation is extremely important and is significantly associated with the reduced risk of AFib recurrence if the diagnosis to ablation time is shorter compared to a longer diagnosis to ablation time. But we know that even today, patients are not getting their ablation equally on time and there are huge disparities existing within the system. So our objective was to evaluate how the social determinants of health of the patients could impact diagnosis to ablation time, primarily. And our secondary objective was to also evaluate how the social determinants of health could affect the anticoagulation prescription rates in the AF patients in our healthcare center. So the study design was a retrospective cohort study. We did a retrospective analysis of 1,025 patients from the Hopkins AF Ablation Registry, which spans from 2014 to 2023. Our exclusion criteria was prior ablations, missing diagnosis to ablation time data or missing social determinants of health data. Our primary outcome was diagnosis to ablation time, which was made in months. And our secondary outcome was anticoagulation prescription at discharge for the eligible patients according to the CHAS score. The key exposure was the area deprivation index. So as I mentioned about the social determinants of health area deprivation index, I could just give a little bit of background on that. This is the most widely used and validated surrogate marker for social determinants of health. This is a neighborhood level measure. It includes 17 census-based measures of socioeconomic disadvantage. We decided to use this because we did not have individual data on social determinants of health yet in our registry, which we are planning to do, as I will tell in the future directions. And we analyzed this as a continuous 10 percentile increments, as well as a categorical variable based on tertials. The statistical analysis that we used was a multivariate log gamma regression for diagnosis to ablation time and logistic regression for anticoagulation prescription rate, which was our secondary outcome. Both of these analyses included adjustment for pre-specified covariates, which were age, sex, race, AF type, smoking history, and the comorbidities of the patients. The findings were really striking. When we analyzed the ADI as in 10 percentile increments, we found that each 10 percentile increase in ADI. So over here, increase in ADI means a worse social determinants of health profile. So each 10 percentile increase in ADI was associated significantly with a 3.8-month delay in diagnosis to ablation time, with a confidence interval of 1.3 to 6.4 months. We also did a restricted cubic spline analysis using the three-notch restricted cubic spline, and we can see that the associations were grossly linear over here, that as the area deprivation index was increasing, the patients were getting worse in terms of their SDOH profile, the diagnosis to ablation time was increasing as well. And then when we divided the area deprivation index into tertials, we found that the most deprived tertial, that is, the most deprived patients who were living in a specific neighborhood had a 12.5-month longer diagnosis to ablation time compared to the least deprived patients. And this was also statistically significant. This is also clinically very important because this year, in Circulation EP, there was a study, a meta-analysis, which demonstrated that a diagnosis to ablation time of greater than one year was significantly associated with a 27 percent increased risk of AF recurrence. So this is not only a socioeconomic perspective, but also a clinical perspective as well in how these upstream factors can affect downstream outcomes. We also performed a subgroup analysis, and we found that a stronger association in patients less than or equal to 65 years of age compared to the patients who were older, 66 years and older. But we did not find any sex-based differences, males versus females. In terms of racial-based subgroup analysis, we could not do because our cohort was predominantly white. But the point I'm trying to make here is, as similar studies on social determinants of health have shown, that when we adjust for social determinants of health, the race-based disparities or the sex-based disparities, they disappear or appear to disappear. So this is very important that not the sex of the patients or race of the patients, they might be important, but social determinants of health are much more important than these other factors that we considered in the previous studies. And good news was that once the patients reached our healthcare system, equity prevailed. Ninety-seven percent of the eligible patients received anticoagulation, and we could not find any significant associations between ADI percentile and odds of having an anticoagulant at discharge. So let us discuss briefly on why the delays might occur. I just wanted to clarify that we did not have the specific data to look at the underlying drivers who were driving the observed associations, but these are just hypothetical mechanisms that could be explored in future studies. One of the things could be structural barriers. There could be lack of specialists in the deprived areas. We all know that deprived areas lack cardiologists, let alone EPs. Patients could face travel burdens. A patient who's driving for three hours is more likely to miss an appointment. Then there could be health literacy gaps, delayed symptom recognition. There could be procedural misconceptions among the patients. There are systemic biases. Previous studies have shown that the providers have implicit biases for the patients who are deprived socioeconomically, that they might not follow up with the procedural interventions, and then there could be fragmented referrals. And last but not the least, competing priorities, financial instability is a big factor, and if the patients are having caregiving responsibilities, they might not have a timely procedural intervention. So what are the solutions and policy implications that we as the EP community could think about? One of the things is embedding S2H screening because if we don't know about who is at high risk, we cannot follow them, we cannot flag them, so this is really important. The other thing could be community partnerships, AF education in high ADI neighborhoods such as in community centers or churches could be initiated to improve the health literacy regarding AF, expanding telehealth for pre- or post-ablation care is really important, and policy reforms such as Medicaid reimbursement for transportation or housing incentives for specialists in underserved areas is important. One of the things that we are doing at Hopkins is AFib prevention clinic that we have established over here, and we are following up the patients who are more socially deprived. We are sending flyers to the resident centers, and this model could be scalable in terms of the digital health intervention that we have developed at Hopkins. It could be replicated in other healthcare institutions as well. Again I wanted to highlight the limitations of the study. This was a single center, urban cohort, predominantly white study, so our findings might not be generalizable to other parts of the U.S. or other parts of the world, and we need more studies on this. And again, observational study design, there might be residual confounders that we could not adjust for, and ADI mayors, neighborhood-level deprivation, as I already mentioned, future studies we are thinking about including individual social determinants of health as well using the surveys for the patients who are involved in our registry. And selection bias, only patients who received ablation could be included in this study, and the patients who never received ablation, we could not assess that, so those could be explored in the future studies. To sum up and to summarize, greater S2H burden independently is associated with significant delays in diagnosis to ablation time. Systematic interventions including routine social determinants of health screening and targeted support programs, they are essential to address socioeconomic barriers and improve equitable AF care. Thank you so much. I would like to thank you, my AF research team at Hopkins, especially my primary mentors, Dr. Sprague, Dr. Ronald Berger, Dr. Hugh Calkins, and Dr. Joseph Marine for their immense support, without which this project would not be possible. And now I would like to open the floor to the audience for any questions. Thank you again. Thank you. Great presentation. I have a question for you. You have shown that the social determinants will change the time to diagnosis and treatment as it relates to afib ablation. Do you have data to show that that then results in a different outcome, or are you looking at that data? That's a great question. We are actually looking at AF recurrence and complications related to social determinants of health. But that was not possible in the current data due to some of the missing data and data limitations that we are collecting in the Hopkins Ablation Registry. But one of our future goals is to look at how these disparities in that related to social determinants of health, they translate into maybe higher afib recurrence, or maybe higher rates of complications. So that's our next project after this. Thank you. I was just wondering, you said you collected data from, what is it, 2014 to 2020? 23. 2023. 23. So over the past few years, ablation has been shown to, the sooner we can ablate them, the better. Whereas 10 years ago, we said, well, we'll try the medications. And have they broken it down just to see with us trying to get patients ablated as quick as possible for better outcomes that we've noticed that there's a change over the past few years with access, or by year, I guess, as we have changed practice, I guess. And the other thing is, is there one specific social determinant of health that actually is more impressive than others that we can work on, or if you saw anything in your data? That's a great question. Both of those questions are really good and insightful. So the temporal adjustments that you're talking about, we could look into that. That's a really good point. We did not adjust for the temporal adjustments and how the trends have changed over time. The second question was regarding one of the specific social determinants. So in the previous studies, we have shown, not in AP patients, but in the general populations, such as cancer survivors, that food insecurity might be one of the biggest social determinants of health, impacting cardiovascular health of the patients. But over here, since we did not have individual social determinants of health data, and we were using neighborhood level deprivation markers, so that was a combined marker of 17 social determinants of health, which were on the neighborhood level, and we did not have data for each of those individual markers that encompass the ADI. So, but our next project is to collect first the data, individually, about all the social determinants of health of our patients, and then we could look into individual markers. That's a great point. There's a comment to add, any thoughts about advocating to add this type of information, really pursuing more information related to social determinants of health into medical school education? That's a good point, that's a great point. I strongly believe that, not only in medical school, but as in primary care, or as in physicians, the social determinants of health, their awareness, what they are, is really lacking. And we do need these studies, and we do need to look at the social determinants of health is really lacking, and we do need these studies which will spark this movement, in terms of focusing more on SDUH, as highlighted by the AFib consensus statement, as well, that the research on social determinants of health is lacking, and we should definitely involve more medical students, as well, in terms of HRS, or ACC, their medical student research communities. We could involve more SDUH-based research in that, as well. Thank you. Great, thank you. Thank you so much, everyone. We'll move to the next speaker. Andrew Nguyen. So, Female Sex as a Primary Predictor of Increased Low Voltage Area in Proximal Atrial Fibrillation, a Real AF Study. Hello, my name is Andrew Nguyen, and I am a medical student at the Keck School of Medicine of USC. Today, I am excited to present my research on female sex as a primary predictor of increased low voltage areas in paroxysmal atrial fibrillation, a real AF study. These are my disclosures. Did you know that female patients with atrial fibrillation often experience more symptoms and a poorer quality of life? Prior studies using immunohistochemistry have shown that women have increased collagen deposition in the pulmonary vein sleeves relative to men. And imaging studies have demonstrated that women have increased late gadolinium enhancement reflective of atrial fibrosis. While this may explain their symptomatic burden, we currently lack data on their atrial fibrosis burden in real-world clinical settings, given that women are less likely to receive ablation therapy. Therefore, I collaborated with the Real AF Registry, which is the largest prospective observational and multi-center registry, enrolling patients undergoing first-time radiofrequency catheter ablation for either symptomatic paroxysmal or persistent atrial fibrillation. All sites included are high-volume centers with low use of fluoroscopy and a standard care protocol involving follow-up at least 6 and 12 months. I seek the answer to the following question. In real-world clinical settings, are women more likely to have significant atrial fibrosis while maintaining a paroxysmal pattern at the time of their AF ablation? Therefore, I performed a retrospective study with a cohort of more than 6,000 patients who underwent first-time ablation for either paroxysmal or persistent atrial fibrillation. During their ablation, operators created voltage maps using CARTO with a Pantoray catheter. Our voltage areas were then defined as having a bipolar voltage of less than 0.5 millivolts that's indexed to LA volume. Based on our voltage histogram data in the population, I set the fibrosis threshold to 5%, less than 5% equals minimal fibrosis, and greater than or equal to 5% indicating significant fibrosis. And with the help of our statisticians, we did multivariate logistic regression analyses to group traditional AF risk factors that you would often see in a chest-back score into one of two groups, the significant fibrosis group that's presenting in a paroxysmal pattern or the minimal fibrosis group that's presenting with a persistent AF pattern. And these are my baseline patient characteristics in the registry prior to doing any multivariate analysis. Today's focus will be on the two columns in the middle. And as you can see here right away, female patients make most of the significant fibrosis and the paroxysmal AF group. You can also see that it's also correlated with age as well. So let's go into our first results. Using logistic regression, I found that female sex was the strongest independent predictor of significant atrial fibrosis in paroxysmal AF. This is reflected by an odds ratio of 2.858. This is followed by vascular disease and then age. BMI was barely significant. Significant stress factors like congestive heart failure and hypertension were actually associated with a reduced odds of belonging to this group, meaning that there is greater odds of it belonging to the persistent AF and low-scar group. History of stroke, diabetes, and sleep apnea were not significant in either group. And now let's look at the cohort of patients who presented in sinus rhythm at time of ablation. I found that female sex still remained at the strongest independent predictor of atrial fibrosis in paroxysmal AF. Similarly, this was associated with an odds ratio of 3.18. This is followed by vascular disease. Age and BMI in this case were not significant. And similarly, congestive heart failure and hypertension were associated with a reduced odds of belonging to this group. And stroke, diabetes, and sleep apnea were not significant to either group. And now let's look at the 12-month outcomes after ablation. I used the Cox proportional hazards model and found that there were no significant differences between either group and their risk of arrhythmia recurrence. And as you can see here, there only seems to be about one curve per graph. This is because it overlaps to such a substantial degree to the point where you only see one of them. And let's look closer at the covariates that I analyzed using the same Cox model. In this case, I found that female sex was the only covariate that predicted an increased risk of arrhythmia recurrence at 12 months with a hazard ratio of 1.26. The fact that it was still significant after controlling for fibrosis indicates that it encapsulates a broader risk profile such as systemic inequities. And now let's look closer at a cohort that presented in sinus rhythm at time of ablation, a likely healthier cohort that probably has appropriate rate or rhythm control. I found that female sex was no longer a significant predictor of arrhythmia recurrence and only congestive heart failure was the one that predicted the increased risk of recurrence. This may be what you would see in a population with minimal effects of systemic inequities at play. If all of this taken into account, I can conclude that female patients are more likely to have increased left atrial fibrosis at the time of their catheter ablation while having a paroxysmal pattern. In addition, they are also more likely to have an increased risk of arrhythmia recurrence likely due to systemic inequities. Increased low voltage areas may explain why women have a greater disease burden despite having paroxysmal atrial fibrillation. Our future directions mostly center around the question why. Why is it that female patients have paroxysmal AF while having fibrosis at the same time? Is it because of systemic inequities such as being diagnosed at a later age or being less likely to receive certain types of treatment like antiarrhythmics, cardioversion, or catheter ablation? Or is it because they're less likely to be represented in clinical trials and studies? There will also be a biological component that have been published in prior studies such as an increase in FSH expression leading to oxidative stress on the myocardial tissue. And there's also the lack of estrogen in postmenopausal women that would have been protective. In this case, it would lead to an increased TGS beta pathway leading to fibrosis. A message that I would like to leave for you all today is that if we only treat what we see, we'll keep missing those that we don't see. And in atrial fibrillation, the ones we miss are often women. By recognizing and acting upon this, we can bring the word of AP together towards equitable care. I would also like to thank my faculty mentor, Dr. Janai Zaman, who is also the senior author of this abstract. I would also like to thank all of my co-authors, Alpha Omega Alpha, Biosense Webster, and Keck Medicine of USC for their support. Thank you. Very nice job. Any questions from the audience? You said, great job, really a very important piece of work. But here, vascular disease, could you define what is vascular disease? Is it a veno-occlusive disease, peripheral arterial disease, or microvascular disease? Second question, have you looked into any autoimmune diseases, concomitantly any data on that? Thank you. Thank you for the question. In this case, vascular disease encapsulates all of which that you mentioned. When it comes to autoimmune diseases, it wasn't an option in the registry, which is one of the limitations. But I do recognize that autoimmune diseases can, of course, contribute to fibrosis, given the pro-inflammatory effects. That was something that I would like to do in a follow-up study, such as a single sensor, which would be feasible. I'll ask a quick question before the next question. I think we all know that left atrial size has a lot to do with potential areas of low voltage or risk of atrial fibrillation. Did you account for that in your analysis? When you consider congestive heart failure and hypertension, of course, it could lead to fibrosis, but that's when you consider that it leads to fibrosis at a later time. Based on our data, it's primarily a functional remodeling that leads to electrical remodeling first. In this case, the stretch in the left atrium and the dilation of it would most likely lead to AF conduction heterogeneity first. That leads to patients presenting with persistent AF very soon. That's why they are associated with a low scar. Over time, they may end up being involved with fibrosis, and that's when they would have persistent AF and high scar, which is not one of the groups that was analyzed in the study. We have time for one question. Great presentation. A couple of questions, actually. You found that there was a lot of fibrosis in low-voltage areas in women. Could you characterize, was it more at the LAPV junction? Was it on the posterior wall, or was it diffused? That's number one. The other question is, what kind of ablation procedure was done in that patient population? Should women, depending on if the predominance, let's say, was low-voltage all over versus more in the LAPV junction, should women then, depending on the distribution of low-voltage areas, should there be a different procedure of ablation to improve the outcomes post-ablation in this patient population? Thank you for the question. First of all, when we consider low-voltage areas and their distribution in women, we do not have that data analyzed in the study at the moment. However, we recently have access to Cardonet at several centers. In that case, our future study can, of course, include which areas that they're most likely to have low voltages in their left atrium. When it comes to a specific area in the left atrial wall that may have fibrotic effects, for example, the posterior wall, or is it more diffused in general? It is likely that we would need to personalize ablation therapy for women to target those areas, because those are the areas where conduction heterogeneity may occur. They are the areas where they may form re-entrant circuits that may be transient or sustained, depending on how close these fibrotic areas are. Thank you. Thank you, Dr. Nguyen. Our next speaker is Dr. Tabarres, talking about real-world data of sex differences in ablation strategy and outcomes for paroxysmal atrial fibrillation. Really looking forward to your talk, Dr. Tabarres. Oh, where is it? Here. Good morning, everyone. My name is Daniel Hincapie. I'm a research fellow from the Brigham and Women's Hospital, and I'm honored to present our work entitled Real-World Data on Sex Difference in Ablation Strategy, Complications, and Outcomes of Rarefrequency Atrial Fibrillation Ablation for Paroxysmal Atrial Fibrillation. The insights from the prospective multicenter, Real-World Registry, Real-AF. So, as a little bit of background, sex difference in atrial fibrillation ablation strategies and outcomes remain insufficiently characterized, particularly between AF subtypes. Sex-based difference in paroxysmal atrial fibrillation management may influence rarefrequency ablation efficacy due to variations in the disease presentation. Usually, we have found in previous studies that women are often older at the time of the ablation, present with more severe symptoms, and have more advanced atrial remodeling than men, which might impact procedural success and increase complication rates. So, the objective of our study was to evaluate sex difference in ablation strategy, procedural success, complications, and clinical outcomes in patients with paroxysmal atrial fibrillation undergoing rarefrequency catheter ablation in real-world settings in the U.S. So, as my colleague previously mentioned, the Real-AF Registry is a real-world experience of catheter ablation for the treatment of symptomatic paroxysmal and persistent atrial fibrillation using novel contact force catheters. So, it's an observational, non-randomized, prospective, multicenter real-world registry that has been designed to collect real-world data on procedural and clinical outcomes of patients undergoing radiofrequency catheter ablation for the treatment of paroxysmal and persistent atrial fibrillation, following a standard of care clinical practices. So the reality of registry as per February 2025 has 69 active sites, four sites in activation right now. We have enrolled 12,016 patients, accounting for paroxysmal patients, 7,262 patients, and persistent atrial fibrillation, 4,754 patients. The target of the registry is to enroll more than 15,000 patients. So we are close to the goal. So let's get into the methods. So the inclusion criteria for the reality of the study are subjects with symptomatic paroxysmal atrial fibrillation who, in the opinion of the investigator, are candidates for radiofrequency catheter ablation. Usually we include the novel ablations, but you can include a redo procedure if the index procedure has been already included in the registry. Subjects must be older than 18 years old, and they have to be able and willing to comply to pre, post, and follow-up testing on all requirements. So the exclusion criteria encompass patients enrolled in an investigational drug or device trial if they have undergone prior surgical or catheter left atrial ablation, or if they have any known contradications to the ablation procedure, or a device or drug-required ablation procedure. In terms of the procedural approach, so radiofrequency catheter ablation using the commercially available biosense Webster catheters for the treatment of paroxysmal atrial fibrillation was performed. Pulmonary vein isolation was the ablation lesion set that was done to all patients as per standard of care, and additional ablation lesion sets were performed according to the operator's discretion. So as per follow-up, the registry follows a standard of care practices, so follows patients for asymptomatic recurrence at three months, post-blanking period, and 12 months of follow-up, and you can also do additional follow-up for symptomatic recurrence at six months and 12 months if required. The data includes procedural workflow, short-term and long-term outcomes, clinical outcomes and procedural outcomes, any procedure or device-related complications, and all atrial arrhythmia recurrence. So in this particular study, we evaluated 2,773 consecutive patients undergoing radiofrequency catheter ablation from January 2018 to May 2023, across 44 active clinical sites involving more than 114 operators. So we collected baseline demographics, ablation strategies, intraoperative parameters, and procedural and clinical outcomes, which were compared between sexes at the three and 12-month follow-up. So now let's get into the results. So we can see that most of the baseline characteristics had a significantly statistical difference between men and women. So the age, women, as we can see, is consistent with the prior studies that my colleagues have presented. So women also are referred or go to the EP or the cardiologist at an older age with a higher chance of a score. Oh, sorry. However, they have less comorbidities, like the rates in men of congestive heart failure, hypertension, vascular disease, diabetes, prior stroke or TIA, and sleep apnea are lower in men. So that means that the gender, women itself, it's like causing this higher rate, higher mean in the chance of a score. Also, they had smaller left atrial volume indexes and a higher left ventricular ejection fraction at the time of the procedure. Let's get a little bit down in the procedural characteristics. So we can see, in general, women require more substrate modification, usually more posterior wall isolation, even in patients with paroxysmal atrial fibrillation. They often require more ablation, anterior and lateral mitralismus line, superior vena cava and coronary sinus ablation. We can see that, however, they require more additional ablation lesion sets, the procedural time and the radiofrequency time are a little shorter. So it's very paradoxical, and the radiofrequency time is also a little shorter in women. As for our primary outcome, we can see that the freedom from all atrial arrhythmias and adjusted for all comorbidities was significantly lower in women compared with men, 79.6% versus 82.8% in men, without a statistically significant p-value of 0.024. However, when we did the adjusted model, we can see that the difference is still there, but it's not statistically significant anymore with a p-value of 0.110. We can also see that when we do the subgroup analysis, the female gender was an independent risk factor for undergoing additional ablation lesion sets encompassing soft-strain modification and CTI. However, when you only compare pulmonary vein isolation plus CTI, usually men undergo more CTI than women. So the conclusions of our study mainly women with paroxysmal atrial fibrillation had more comorbidities, more percentage of atrial scarring. They require more extensive ablation and had higher oligotrial arrhythmia recurrence. However, the complication rates between the groups didn't have any statistically significant difference. Women appear to be referred older with advanced atrial fibrillation disease, bigger LFH rooms, more scarring in the left atrium also, and they require consistently more soft-strain modification. Women also face longer term arrhythmia recurrence risk, especially in ablation strategies involving soft-strain modification, including posterior wall isolation. Despite they have similar comparable short-term outcomes, the rates of first-fast PBI were the same between the groups, and as I said, acute and long-term complications were the same. And these findings highlight the importance of sex-specific ablation approaches that account for extensive atrial remodeling and non-pulmonary vein triggers to improve long-term clinical outcomes in the female gender. I personally want to thank Dr. Cici Tsang for her major contributions to this project. I want to thank all my mentors, especially Dr. Tsai, and all the reality of investigators for their amazing work. And I want to thank the Cardiac Arrhythmia Service at Brigham and Women's Hospital, because without them, none of this could have been possible. Thank you so much for listening, and any questions, let me know. Thank you. So congratulations from really terrific work, and I congratulate the designers of the Real AF Registry, because I think this gets us closer to understanding some of these differences that we see. We hear a lot about the differences over and over again, but we don't get to the ideology of why these things are happening, so really, congratulations. I do have a few questions, and I hate to ask multiple questions, but I'm gonna do it in this time, and I'll try to go slow so you can remember what the questions were. Thank you. The first one is really about, did you look at the distribution of where women were ablated across the registry sites? Because as we know, ablation is a very operator-dependent thing, and so you might gain some insight, because I'm kind of curious as to why women received so much adjunctive ablation. Was it driven by specific things that people saw? Some people have pretty dogmatic approaches of how they ablate persistent atrial fibrillation, or if there is scar, that will drive them to do certain things, and sometimes that can be clustered among the sites, and so if you see that a lot of women are ablated at certain regions or certain sites, that might give some interesting insights, so that's my first question. The second question really is about how the age differences that you saw for women being older, but they had less risk factors, but more scar, which I think is counterintuitive to how we typically think about scar formation and the things that contribute to scar formation, so any thoughts, hypotheses about why you see that? Well, thank you so much for your questions. So the first one, so the VLAF registry is a prospective multicenter registry. Usually all the centers and operators that are included are highly experienced operators, and usually academic centers and highly volume centers. However, this is one of the limitations of the registry because it's really difficult to know the granularity of the data, so we can only see what it has been reported and collected to the ADC. We know that, so pulmonary venous isolation was a standard of care ablation lesion set performed, but whether, if they wanted to do anterior mitralis muscle and posterior wall isolation, that was per operator discretion, and I know it may vary a lot. However, it's a different thing when you see a paroxysmal or persistent patient, and then you go into their atrium, like what you are going to find out. Sometimes patients are classified as paroxysmal, but maybe they didn't, they were asymptomatic, and they have persistent, and their atria is super scared, have a higher rate of, a greater atrial scaring area, but they were misclassified, so I think maybe this is an unpopular opinion, but maybe the atrial fibrillation categorization should improve because sometimes we don't see the correlation. It's a paroxysmal patient with a healthy atrium, and then we go into the lab, and the atria is huge, or the atria is full of scaring, so I think it's really different what you see, like what you see verbally, or on the medical records, patient with paroxysmal atrial fibrillation, that one you find actually when you go into their atrium to their heart, and I think it's because the classification is imperfect because sometimes if you have asymptomatic recurrence, it's really hard to tell. Also, women, usually they don't go to the doctor as frequently as men. This is also unpopular, but we are busier, and they, oh no, this is nothing, this will pass, so I think that's why they go to the doctor older. They try to mitigate their symptoms, and I think that's why they go to get referred with a more advanced disease. Is that answer to your questions, or you have an answer? Thank you. Great job presenting everything. Real AF is wonderful, but going back to, I think registry data is registry data. It's a wonderful start, but just like the last speaker, we don't get the granularity of, when you look at sex-specific differences in atrial fibrillation as a pathology and treatment, you mentioned congestive heart failure because of CHA2DS2 vascular risk score. We don't define, is it, what about those women with HEF, medium EF, HEF, PEF? We know it's higher in women. We don't know how they do when compared to the HEF-RAF population, and so I just wanted to bring that up, say good job, great work. Thank you very much, and I think that's really important because we have seen, the real AF has produced a lot of research. We also compare patients with heart failure with reduced ejection fraction and heart failure with preserved ejection fraction, and we can see that sometimes there was some inconsistency in the data, like congestive heart failure, how is it defined? Is it more specifically? We should improve the CRF, and that's a great feedback to include heart failure with reduced ejection fraction or heart failure with preserved ejection fraction, or even borderline because they behave differently, but thank you very much for having me. Thank you so much. Out of respect for time, we would like to conclude this session. We do wanna give some abstract awards for our presenters. All five presentations were amazing. Thank you so much to the presenters, but I'll start with the first award that goes to Dr. Aguni in regards to gender disparities and CIEDs, so thank you, Dr. Aguni. Congratulations. Thank you.

health equity

heart failure

gender disparities

geographic inequalities

ICD implantation

AFib comorbidity

social determinants

atrial fibrosis

systemic barriers

equitable healthcare