Hi, I'm Max Tamaika-Kasu. I'm a pulmonologist in critical care in Grand Rapids, Michigan. So Peter says I have to be louder. I'm a respiratory therapist from the University of Cincinnati, and we're your co-moderators. So our first presentation will take questions after. If you have a question to ask, please come up to the microphone, because they are recording these sessions. Our first paper is on Clinical Application of Computer Algorithms to Cardiopulmonary Exercise Test Interpretation Validation by Expert System Application. And our first speaker is David Lyons from New York, but now California. David? Hello. Welcome. Can you hear me? Yes. Good. Good morning, or if you're not acclimatized yet, good afternoon. I'm looking forward to hearing from our other distinguished presenters today, but I'm going to start things off with our presentation. Okay, we're going to speak about our experience applying computer algorithms to cardiopulmonary exercise interpretation. And just to warn you in advance, wherever possible, I'm going to use verbal abbreviations. My name is David Lyons, an RRT from St. Francis Hospital and Heart Center in Rosslyn, New York. I'm now retired, and I have a financial disclosure. Your presence here today, I think, attests to the fact that you value the role of CPET testing in your practice, and you also undoubtedly appreciate some of the challenges that are involved thereby. We're going to speak about our effort to objectively assess the application of a computer application of a computer algorithm to the work of CPET interpretation. In order to attempt to do so and reduce self-bias, by the way, we've used this application in our own facility for years at St. Francis Hospital. But in looking to have an objective reference, we sought to utilize the authoritative CPET material from the authoritative textbook, Principles of Exercise Testing and Interpretation, specifically the Wasserman-Wippe Stringer 3rd and 4th editions et al., and the Saitsema Stringer et al. 6th edition. And that material, if you're, I expect, familiar with it, we digitized this timed-down CPET data together with patient demographics and then uploaded that to our program. We analyzed the results of that effort using receiver-operator characteristic analysis. We used a five-category discrete ordinal scale to look at our primary and secondary diagnostic impression outcomes. We'll talk more about that in a moment. What you're looking at here is a graphic representation of the scoring system that the application utilizes. So in order to differentiate between the many different diagnostic possibilities, actually 19 in all, not counting some additional subcategories, ranging from normal through mitochondrial muscle disease, McArdle's syndrome, and everything you see in between. The ES application attempts to make clinical differentiations, and these are scored. And what you're looking at in this particular slide is one of the cases from the, I'm going to call it PETI for the sake of brevity, Principles of Exercise Testing and Interpretation. You're looking at one of the patients, which happens to be a woman with severe sarcoid with both cardiac and pulmonary involvement. And this is just an example of the scoring aspect of this. And the PETI material, 180 cases from, I believe, about 149 patients, mixed male-female, 26% female, they covered the entire gamut of these 19 possible diagnostic categories, obviously to a greater or lesser degree frequency. We'll talk first about our alignment of the ES application results. What you're looking at are the tables and the graphics of, just find my pointer, okay. So here's the ROC analysis for the primary impressions only. And you'll see those up at the top of table two. And the ROC analysis showed 93.9% acceptable alignment in the primary impressions with 3.9% non-alignment. And you'll find, if you're interested, whoops, if you're interested, you'll find those numbers right here in terms of alignment and non-alignment. And in secondary impressions, because the application considers the possibility of not just a singular result, but possibility that there could be multiple comorbidities, the secondary outcome alignment was 84.7% in secondary impressions with 4.5% non-alignment. And this is just a little cartoon illustrating some of the primary steps involved. The patient demographic and time down tabular data is digitized. And there's a lot that is going on in each of these steps, which obviously time won't allow me to discuss. But the primary element that's going on here at this point is alignment of stage time sampling. So this is where we get our rest, reference unloaded, peak exercise, pre-exercise, recovery, and anaerobic threshold. And this is digitized and uploaded to the next step, which is analysis. Let me just say a word, though, first about the anaerobic threshold. The ES application picks all these sampling points on its own, but the ES operator could obviously override it if they chose to. But in this case, we left everything at defaults, with the exception of anaerobic threshold. We wanted to make sure that we were aligned with the Wasserman et cetera group's own selection of anaerobic threshold. So the automatic detection of anaerobic threshold in this report was turned off, and it was fixed at whatever the experts, the clinical experts, defined. And by the way, just for the sake of clarity, our reference point, our gold standard, was the Wasserman-Wipp-Sietsema-Stringer et al. narrative impressions, not the Wasserman flowcharts. The flowcharts, which I'm sure you're all familiar with, are a wonderful teaching aid and guide for anyone interested in CPAP interpretation, but these are not used in our application. They're not modified. They're not an element at all. But just for the sake of avoiding confusion, I just wanted to mention that. So initial analysis is where references take place and derived data is taken. That goes on to further analysis, which again, time won't allow me to get into. But this is where clinical differentiations take place, and the separation between all the possibilities is going on. And that ends up at a final report, and in this case, for our discussion today, only the final element of that report is what we're interested in, only the final impressions. But I'll say, hopefully have time to just say a word about the report, because as I mentioned, we've used this for years in our own facility, and this is a second reader for the clinician interpreting the tests. It's not meant to replace the clinician, but to help guide and calibrate the clinician's efforts. So again, that consists of a tabular summary page illustrated here, a narrative summary page, which is a full natural knowledge description of what went on in the test with important findings and secondary elements and probabilities. In 1990, Dr. Newberg editorialized in CHEST his wish that CPX could find more widespread application, and despite all the advances that have taken place since, 24 years later, Dr. Guazi and Irena still echoed Dr. Newberg's wish that a simplified approach to key data identification interpretation was available. I'm going to skip over some of our others and jump to the end. What we're able to demonstrate is that an expert ES system computer application produced acceptable statistical correlation of resulting diagnostic impressions compared with previously published expert conclusions. The clinical implications of this are that presenting a well-organized and reliable CPET tabular narrative and diagnostic report invites clinicians to become more efficient in their reviews and serves as a second reader upon which the practitioners can reflect and calibrate their own initial impressions. I thank you for your kind attention and welcome any questions you might have. Our next presenter is Dr. William Stringer, and he will present Utility of Two-Day Cardiopulmonary Exercise Testing Protocol in Long-Haul COVID Patients Preliminary Data. Great. Thank you very much for inviting me, and I'm anxious to see the other presentations. It's great to see people in a CPET room talking about physiology. Couldn't be better. I'm going to be talking about two-day cardiopulmonary exercise testing in long-haul COVID and the utility of that. These are my coworkers. Some of them didn't get to come to Hawaii, but also did great work. I work at David Gesson School of Medicine at UCLA. My background is in pulmonary and critical care, and we have a research institute where we do most of our testing, and this is where the Wasserman book came from. I don't have any direct conflicts with this, but we do do long-haul COVID exercise research with our foundation, and also we're with part of the Recover, the Vital, and Neuro. So my intent today is to explore the use of the two-day CPET in trying to understand post-exertional malaise in patients that have long-haul COVID. So as we're increasingly aware, there's just a number of symptoms that can be seen in these patients, and frequently multiple symptoms. The most common really is fatigue up here at 58%, and all the various colors on there, I don't have time to go through them, but they're actually assessing the various inflammatory mediators and radiographic abnormalities with the various symptom complaints. So we're not sure what causes long-haul COVID, but there's a lot of research going on right now. One of the thoughts is there's a persistent viral replication somewhere in the body, and this is where Recovery, Vital is after with 15 or 25 days of Paxlovid. We'll see how that turns out. Also that the immune system is clearly activated, and what's causing that ongoing activation, whether it's a viral reservoir or something else, is not known. The possibility that we've activated some internal virus that we already have, EVV, CMV, et cetera, is a possibility. Also the gut and the microbiome seems to be affected, and that also may be an area where there's viral persistent antigen release, also similar to something like HIV, where we see the gut be affected. There's certainly microvascular dysfunction, not only the structure of the microvasculature, but also abnormal coagulation. And then finally, there's autoantibodies that you can find both peripherally and inside the CSF. So autoantibodies and any of these mechanisms, plus multiple mechanisms, may be active in these people. So we were interested in using the two-day CPET. Normally in patients that have a CPET one day and the next day, they're very reliable, they're within 5% of each other in terms of peak oxygen uptake. And also the variability is a little more in terms of the lactate threshold, but peak ventilation, VEVCO2, they're all within about 5%. So you'd expect normal subjects day one, day two, to be very similar. This is data by Dr. Decato. So if you take normals and do day one, day two, they're identical. This is unexplained fatigue in Gulf War veterans. They're actually, this figure is from that paper. It's a little complicated figure, but this is peak oxygen uptake. This is day one, day two for the normals, and day one, day two for the Gulf War fatigue folks. And so you can see that they're very similar day one, day two. And that contrasts a little bit with what we see in chronic fatigue syndrome. And this is also a little bit of a complicated thing, but basically they show decrements in peak oxygen uptake, work load, and AT compared to normals. And so that top row there is about 50 patients, Dr. Snell et al. And they found decrements of about a mil per kilo in terms of peak oxygen uptake. And then the work rate went down about eight to nine watts. So slight decrement the second day, and the thinking that's related to post-exertional malaise. So we asked the question in long-haul COVID, is it actually a way to assess post-exertional malaise? Because there's been a lot of thought in the popular literature that chronic fatigue and long-haul COVID are the same etiology. So we took 15 patients, they were average age 53, about 50% female, body mass index a little high, 32, pulmonary function test that was about 90% of predicted in both spirometry, lung volumes, and DLCO. We did questionnaires to assess their status. They're very poor sleepers. They're moderately depressed. They have mild anxiety. They have mild to moderate dyspnea on the MMRC. There's a post-COVID functional limitation scale that they're fairly high on. We didn't find a lot of cognitive dysfunction in these people, but they have very high fatigue levels. And specifically, the DePaul is felt to be a questionnaire that represents post-exertional malaise. I'm going to show you that in a second because it may not be familiar to you. But 80% of these people in this study actually showed that. So the exercise systems, the cardiopulmonary exercise system, medical graphics, three minutes of rest, three minutes of unloaded cycling, eight to 12 minutes, and then recovery. BORG scores are collected. The average ramp increment was about 13.9 watts, but between 10 and 20. And then exercise was terminated when they couldn't maintain the cadence at 50 despite encouragement. So this will give you a little idea of the DePaul questionnaire. So do you have dead or heavy feeling after starting exercise? Again, this is the frequency and the severity, but just about 80% in both of them. Next day soreness, mentally tired after the slightest effort, minimal exercise makes you tired, physically drained or sick. And then the things that are more specific to post-exertional malaise is if you became exhausted after an activity, would you recover within an hour or two? So normal fatigue and exercise recovers quickly. Most of those people said no. Do you experience worsening of fatigue with minimal effort? Yes. Do you experience worsening of the fatigue after engaging mental effort? Yes. And then do you feel worse after activities? How long does this last? This is really where the variability occurs, but most of these people say more than 24 hours, but some said less than an hour. And if you don't exercise, is it because your symptoms get worse? And the answer is yes in half these people. So in the two-day CPET, the answer was the peak oxygen uptake was very much unchanged. This sort of gray box is the average pre and post, day one, day two. And these are the individual responses. You can see they're very similar. This is the LAT and the peak oxygen uptake. Borg score equivalent was slightly lower, about one unit, but it was statistically significant but not clinically significant. And then Borg scores are very similar between day one and day two. So the answer for us was that there really wasn't any significant change in these parameters day one to day two, as we might expect in other diseases, including chronic fatigue. And it was much more similar to healthy subjects and patients with idiopathic fatigue. And the results don't mirror what we see in the myalgic encephalitis, chronic fatigue syndrome. And then long-haul is likely differs as other viral syndrome, post-viral syndromes do, and shouldn't be equated one-to-one with chronic fatigue syndrome. It may have some overlap, but may have some areas that differ. And the two-day CPET may not be of very much value in terms of understanding long-haul COVID and understanding pathophysiologic mechanisms. So anyway, I'll quit there. I'm happy to take any questions now or at the end, whatever you'd like. So thank you very much for inviting me, and I appreciate the chance to present. Thank you, Dr. Stringer. So our next presenter is Dr. Divya Narayanan, and she will present changes in the shape of expiratory flow volume curves affect dead space and tidal volume patterns during exercise in patients with COPD. Hi, everyone. I am going to be talking about how changes in the shape of expiratory flow volume curves affect dead space and tidal volume patterns in patients with COPD during exercise. My name is Divya Narayanan. I am a hospitalist at Cedars-Sinai Medical Center and a research associate in the Division of Pulmonary Critical Care at the Lundquist Institute at Harbor UCLA Medical Center in Los Angeles, California. I don't have any disclosures for this presentation. So some background before we get started. Dynamic hyperinflation is associated with the development of concavity in the spontaneous expiratory flow volume curves in patients with COPD. And as you can see on the top right there, those are normal expiratory flow volume curves that you would see in someone without COPD and someone with COPD. And prior research has shown that this degree in concavity that we're seeing can be quantified by the rectangular area ratio, or RAR. The RAR is the ratio of the lower to the upper portion of the rectangle on the flow volume loop. Normal would be an RAR greater than 0.5. And concavity of the spontaneous expiratory flow volume curve can be characterized by an RAR less than 0.5. Prior research has also shown that patients with COPD who demonstrate a decrease in concavity of their spontaneous expiratory flow volume curve during exercise in response to bronchodilator therapy increase their exercise endurance more than those who do not. And volumetric capnography during exercise can allow us to determine the subcomponents of dead space, that being airway and alveolar dead space relative to tidal volume during exercise. So here in this figure on the right, the alveoli labeled C is kind of demonstrating our dead space components. We have total physiological dead space that is made up of the airway dead space and the alveolar dead space. And alveolar dead space is really just wasted ventilation. It's alveoli that are either under perfused or not perfused at all. And when we take total physiological dead space and divide it by the tidal volume, we get the DVT. That ratio is the dead space fraction of each breath. And prior research has shown that bronchodilator therapy can increase the airway dead space, but the effect on alveolar dead space and total physiological dead space and the dead space fraction hasn't been well characterized. So our objective of this study was to understand how differences in flow volume curve morphology through the RAR affect alveolar dead space and total physiological dead space during exercise in patients with COPD in response to bronchodilator therapy. For the study, we had 48 patients that were assessed longitudinally. They were randomized to receive placebo therapy or glycopyrrolate from motorol, a combo Lama Laba bronchodilator therapy. They received one of the two therapies for two weeks. Then we acquired full PFT data, CPET data, and transcutaneous CO2 data. They then underwent a two-week washout period where they didn't receive any therapy, and then were crossed over to receive the other therapy for two weeks. And then after those two weeks, we again acquired full PFT data, CPET data, and transcutaneous CO2 data. The data that I will be presenting were from 44 out of the 48 patients. The rectangular area ratio pre- and post-bronchodilator therapy were determined for each patient from their spontaneous expiratory flow volume curves. The capnography allowed us to determine the dead space components for each of these patients, and the differences were calculated using unpaired and paired t-tests. And all the data that I will be presenting were from isotime of the shorter of the two tests, and two primary patterns were seen here. Those who increased their RAR in response to bronchodilator therapy or had a convex phenotype to their expiratory flow volume curves, and those who decreased their RAR during bronchodilator therapy or had a concave phenotype to their expiratory flow volume curves. So here are some of our demographics. For those who increased their RAR, there was 30 of them, average age of 63, 24 were male, and they had an average BMI of 28.01. Those who decreased their RAR, there were 14 in that group, had an average age of 62, 7 were male, and had an average BMI of 32.8. There was no significant difference in the age between these groups, but those who decreased their RAR did have a significantly higher BMI than those who increased their RAR. And when looking at the change in RAR, this graph here, we have those with convex expiratory flow, sorry, those with convex expiratory flow volume loops here on the left, those with concave expiratory flow volume loops on the right plotted against RAR on the Y axis. Placebo is in the blue bars, bronchodilator therapy in the orange bars, and we can see here that those with convex expiratory flow volume loops, that increase in RAR was significant, but in those with concave expiratory flow volume loops, that decrease in RAR was not significant. When looking at the endurance between those groups, again, those with convex expiratory flow volume loops are on the left, those with concave expiratory flow volume loops are on the right, plotted against time in seconds, again, placebo in the blue bars, bronchodilator therapy in the orange, and we can see here that both groups had improvements in their endurance time going from placebo to bronchodilator therapy, but that change in endurance was not significant within or between the groups. And then when looking at the dead space components, here on the left, we have placebo and bronchodilator therapy for the convex expiratory flow volume loop group, and on the right, we have placebo and bronchodilator therapy for the concave expiratory flow volume loop group. In the blue bars, we have airway dead space, alveolar dead space is in orange, and total physiological dead space is the gray bars. And what we can see here is that airway dead space was similar in both groups. The airway dead space increased post bronchodilator therapy in the convex expiratory flow volume loops, but did not change in those with concave expiratory flow volume loops. Alveolar and physiological dead space were larger in patients with convex expiratory flow volume loops versus concave loops. And neither of these changed post bronchodilator therapy. And then this graph here is just for those with convex expiratory flow volume loops. We have the dead space components on the x-axis plotted against dead space and leaders on the y-axis, placebo in the blue bars, bronchodilator therapy in the orange bars. And this first group here is kind of what we showed on the prior slide, that airway dead space increased post bronchodilator therapy. But you can see here in the second set of bars that the alveolar dead space didn't really change. So that caused a slightly increased higher physiological dead space in response to bronchodilator therapy in this group. And then when looking at tidal volume here, again, we have the convex flow volume loops on the left, concave on the right plotted against tidal volume and leaders on the y-axis, placebo again in the blue, bronchodilator therapy in the orange. And what we can see here was tidal volume was larger in patients with convex versus concave expiratory volume loops during placebo, but did not change in either group post bronchodilator therapy. And because the physiological dead space and tidal volume were both higher in patients with convex flow volume loops, the VDVT ratio or that dead space fraction was the same in both patients at placebo and bronchodilator therapy. So in conclusion, neither the overall endurance nor the change in endurance after bronchodilator therapy was significantly different between the groups. Changes in the RIR during exercise patterns in patients with COPD was associated with difference in the airway and alveolar dead space during exercise. Patients with convex expiratory flow volume curves were more likely to have a greater alveolar and physiological dead space at baseline and a greater increase in their airway dead space in response to bronchodilator therapy, which has been shown in prior studies of COPD patients. But despite the changes that we're seeing in the expiratory flow volume curves in these two groups, that didn't seem to affect the total VDVT ratio or that dead space fraction at either placebo or in response to bronchodilator therapy. Thank you. Our next speaker is David Lyons again. So he's going to present the consolidation and classification of cardiopulmonary exercise test data from aggregated group populations cross validation by expert system application. Hello again. We have taken a perhaps unusual approach to looking at what is ubiquitous in research studies, which is the representation of the results of population groups in research studies. And what we wondered was, if you were to take those results that represents a whole population of mixed patients, mixed demographics, et cetera, and were to aggregate them into a single surrogate patient, would it perhaps yield any interesting or insightful information into looking at those groups once again? Again, I have no financial disclosures. So again, we hypothesized that where we were able to aggregate the information, that there is a possibility that we might be able to define additional insights into population studies. These population groups could be a group, a single individual group, they could be comparison groups, such as what we're going to present here today, or they might be groups being studied over time for therapeutic or other interventions. And you may recognize a bit of a trend emerging here. We looked to have an objective and authoritative reference for our comparisons in this effort. And so we selected a recent study by Inbar et al. in Israel, in which they successfully applied machine learning to the approach of interpreting CPET studies. And what we did was take their group-summarized digital data and then consolidate it into an individual patient. And what you're looking at on the right, whoops, sorry, what you're looking at on the right here are the final ES impressions for each of those groups. And those groups, by the way, were two clinical groups. They were trying to differentiate whether they could have a computer application, recognize the differences between COPD and CHF, and in addition, they had a group of healthy volunteers. So here, what you're looking at is the ES, our expert system impressions, final impressions for the healthy COPD and CHF groups in the learning phase of the Inbar study. The Inbar study involved, for those of you familiar with machine learning, involved supervised learning phase and then a validation phase. And here, what we're doing is, in this illustration, just as a hypothetical example, where a group population of mixed numbers, mixed ages, mixed sex, mixed height and weight are aggregated into a single individual. And then norms are drawn up for that consolidated individual and then processed by the application. And you might recognize that this sort of study had a very limited information basis. What you're seeing, you can see that in the populated versus the empty information here in our ES application initial page. And these are the group demographics for the Inbar group and Inbar consolidations for CHF, COPD, and healthy. And for the aggregated groups, including the CPT data, ignore the columns on the right. We just went a step further and consolidated all of those groups, but we did not include that in this report today. So here, again, you're looking at a clinical differentiation graphic. In this case, it's from the CHF group in the learning phase. So this is a surrogate single patient representing 50 individuals in the learning group. And as you can see, it tends to predominate with heart pulmonary vascular changes and CHF, as expected. Our results showed 100 percent alignment in both the primary and secondary analysis. There were no false positives and no false negatives. And you've seen this, and I won't go through it again. In summary, we were able to demonstrate that you can aggregate populations, each represented here by a single surrogate patient, and that those aligned correctly with the expert clinical categorizations from which they were derived. And just a word about those clinical characterizations. The Inbar group had senior clinicians, cardiologists, and pulmonologists using ATS and AHA standards to define their population groups. And as to the clinical significance, we demonstrated that, yes, it can be done, but as to whether that's adding insights into looking at population groups through this particular method, that's going to require further work. I will say, just in wrapping up, that there has to be some coherence in these groups. Otherwise, this would be a meaningless exercise just to go through this process. And I welcome any questions you might have, and thank you for your attention. So now we have Dr. Gustavo Cortez-Fuentes, who's going to present Cardiopulmonary Exercise Testing Transgender and Gender Diverse Patients. All right, everyone. Thank you very much. There you go. So it's a great opportunity to be here to share with you some of our findings, a partial portion of them, regarding our study Cardiopulmonary Exercise Testing in Transgender and Gender Diverse Patients. I'm Gustavo Cortez, and I'm an assistant professor of medicine and physiology and a consultant in the Division of Pulmonary and Critical Care Medicine at Mayo Clinic in Rochester, Minnesota. And I have nothing to disclose. So there is a large body of evidence actually supporting the very positive effects on mental health outcomes of gender-affirming hormonal therapy among transgender and gender-diverse individuals. And the population of adults who identified as transgender has been growing, with most recently 1.4 million identified in the United States, based on the most recent Gallup report. With gender-affirming hormonal therapy, there are body composition changes that are going to align with the gender goals of the patients who are transgender and gender-diverse. And these changes may challenge our current standard of interpretation of cardiopulmonary exercise testing. So with that in mind, I would like to review some of the main sex differences in the pulmonary system that may have an impact in exercise performance. So patients or individuals who are assigned male at birth are going to have a much larger luminal area of the central airways, meaning the trachea and the third generation through the third generation of bronchii. Also, they will have a larger total lung capacity, and the shape of their lungs is going to be more pyramidal compared to a more prismatic shape that you see in individuals who are assigned female at birth. This will have direct impact on the exercise performance of these patients that will be related directly with these morphological differences between sexes. So individuals who are assigned female at birth are going to experience a much larger resistive component to the work of breathing, and therefore they will experience or exhibit a much larger oxygen consumption that is associated to the activation of respiratory muscles. Also, individuals assigned female at birth are going to have a more frequent activation of extra diaphragmatic muscle activity, and they are going to be more prompt to develop expiratory flow limitation at peak exercise, and their perception of dyspnea at peak exercise is also going to be much higher compared to individuals assigned male at birth. It's very important to understand that these changes are actually established approximately at the age of 14, so it's easy to start to think that this dynamics in pulmonary mechanics and exercise performance could be influenced by the presence of pubertal blockers before this age. However, after the age of 14, there are many other body composition changes that may have a direct impact on exercise performance, and that includes lean body mass, the total muscle mass of the patient, distribution of fat mass, and of course hemoglobin levels. So all of that is going to still evolve after the age of 14 as a result of gender-affirming hormonal therapy. So with that in mind, we actually developed a very simple research question, which is, what are the changes in the percentage of predicted peak oxygen consumption when gender is used instead of sex assigned at birth to estimate normative predictive values among transgender and gender diverse patients that are older than 14 years old who are receiving hormonal therapy as gender-affirming care? So we were able to identify over the last almost two decades in our cardiopulmonary exercise testing database from the cardiovascular disease 16 patients who are transgender and gender diverse, specifically eight transgender men and eight transgender women. All of them were receiving hormonal therapy at the time of the test, and we were able to analyze all the clinical indications of the cardiopulmonary exercise testing, some biometric data, all the CPET parameters, and we also performed a review of the chest imaging, echocardiographic findings, and hemoglobin levels. The main results that we thought were very important in this specific study is that among transgender men who were receiving testosterone as gender-affirming hormonal therapy, deconditioning was the most frequent finding in their cardiopulmonary exercise testing, and the reason why they end up undergoing cardiopulmonary exercise testing, the most frequent indication was dyspnea. Also, when we use gender-congruent normative predictive values instead of sex assigned at birth to estimate the predicted oxygen consumption at peak exercise, we found that patients who were transgender women increased, displayed an increase in oxygen consumption while the peak exercise oxygen consumption was decreased among transgender men. When we look at the very commonly found deconditioning finding in the CPET test of transgender men, it's very interesting because in theory you should expect an increase in muscle mass, and it's easy to believe that that will probably translate in some degree of increased exercise performance. However, when you look at this from the perspective of a central airway and all the pulmonary morphology changes that are established before the age of 14, then very likely these patients are working with a much larger muscle mass and therefore generating a much larger oxygen consumption for any degree of exercise they do. However, arriving to that conclusion with the data I'm presenting today is rather difficult. We will need degree of exercise activity at baseline, and of course, body composition data for each one of them to actually have an association between gender affirmative hormonal therapy and exercise performance. Also, among transgender women, and I think this is definitely related to the nature of the pool where we obtained the patients, we found very frequently cardiac chronotropic insufficiency as one of the findings in the cardiopulmonary exercise testing. And I think that, as you can see, the main reason for this and I think that, as you can see, the main indications of the test are the underlying congenital heart disease, which, of course, have nothing to do with the gender-affirming hormonal therapy they are receiving at this time. So apart from being a mathematical exercise of changing the parameters of gender and sex assigned at birth to estimate percentage predictive values for oxygen consumption, it's really important to underscore the potential clinical implications it could have, this could have, in the interpretation of the cardiopulmonary exercise testing. As you can see here, the percentage of predicted oxygen consumption for both transgender men and transgender women changed significantly when gender instead of sex was used to estimate this normative predictive value. The same occurred when we tried to calculate the functional aerobic capacity among both groups. And, of course, again, most of these findings are related not only to the mathematical exercise, but the need to actually appreciate the physiologic changes that are occurring during gender-affirming hormonal therapy. So not only the oxygen consumption changed significantly when different normative predictive values are used, but also it illustrates the importance of monitoring body composition changes and muscle strength as important pillars of improved delivery of care in this patient population, especially among those that have chronic underlying cardiopulmonary diseases. It's important to not only understand that physiology, but also work towards developing a personalized approach to the interpretation of both pulmonary function and cardiopulmonary exercise testing in this patient population. With that, I thank you. Happy to answer any questions. Our next speaker is Dr. Rochelle Meyer-Legaspi. She's going to present a comparative study of the effect of masks on the six-minute walk test on healthy health care workers in a tertiary institution, a randomized crossover trial. Hello, everyone. Thank you for attending today's session. So our study is the comparative study on the effect of masks on the six-minute walk test on healthy health care workers in a tertiary institution, a randomized crossover trial. So I'm Rochelle Legaspi. I'm a fellow of adult pulmonary medicine from St. Luke's Medical Center, Manila, Philippines, and I have no financial disclosures. So regular use of masks started during the pandemic, and as restrictions started to ease up, people have learned to incorporate different use of masks in their regular activities. Concerns have been raised regarding its safety on performance and its effect on performance when used in regular activities or activities of submaximal exercise. A six-minute walk test was utilized in this study because it can assess an individual submaximal level of functional capacity. It is easily done without the need for any sophisticated equipment, and it is easily reproducible. For the method, this is a randomized controlled trial, specifically a crossover design. The required sample size, including dropout adjustments, were 34 participants. Included were adult healthy health care workers with no pulmonary or cardiac comorbids and a normal simple spirometry results. So after the initial interview and informed consent was signed, each participant completed a simple pulmonary function test. Participants with a normal simple PFT was subjected to a fit test for the N95 respirator to ensure proper sizing of the mask. They were then randomized into the sequence of masking using an online application. Each participant performed three sets of six minute walk tests with one day washout in between. Parameters taken include total distance walked in meters, blood pressure baseline and post six-minute walk test, heart rate baseline, highest during the test and post six-minute walk test, O2 saturations baseline, lowest during the test and post six-minute walk test, and symptoms of dyspnea and fatigue were also recorded using the modified Borg score. The initial screening included 63 participants. 29 were excluded and 34 completed all three walks with no dropout. For the results, table 1A shows the demographic and clinical characteristics of the participants with a mean age of 27 years, height of 161 centimeters, and body mass index of 25.7. Table 1B shows that the most participants were aged between 21 to 30 years old, majority were females, all had no comorbids and were non-smokers, and more than half were of normal BMI. Table 2 shows the comparison of vital signs. This shows that during the six-minute walk test, not all mean lowest O2 saturations were equal across groups with a p-value of 0.031, with the surgical mass group having the lowest mean. Post hoc analysis using the Bonferroni and Sidak method showed that the mean lowest O2 saturations were significantly different between the surgical mass group and the unmasked group, both with p-values of 0.029. The mean vital signs were not significantly different baseline and post six-minute walk test. Table 3 compares the score of perceived dyspnea and fatigue by modified Borg score. It shows that immediately after the six-minute walk test, the mean Borg score for dyspnea were not equal across all groups with a p-value of 0.015, with the N95 group having the highest mean. Post hoc analysis using the Bonferroni and Sidak method shows that there was a significant difference in the mean Borg score for dyspnea between the N95 group and the unmasked group, both with p-values of 0.018. Table 4 shows that the mean total distance were not equal across all groups with a p-value of 0.004, with the N95 group having the lowest mean. Post hoc analysis using the Bonferroni and Sidak method shows that there was a significant difference in the mean total distance between the unmasked group and the N95 group, both with p-values of 0.002. During the post hoc analysis, during the comparative analysis of the three groups, the six-minute walk distance was significantly lower in the N95 group versus the unmasked group. The O2 saturations during the test were significantly lower in the surgical masked group versus the unmasked group. The perceived dyspnea using the modified Borg score was significantly higher in the N95 group versus the unmasked group, and no significant adverse events were reported across all groups. How does the findings compare to other studies? So other studies showed no significant physiologic changes with mask use. However, our results are more consistent with a recent study which compared similar parameters during brisk walking with and without mask and showed that wearing a face mask might be associated with desaturations. Differences in perceived dyspnea with mask use were also reported in multiple studies. For the conclusion, mask use is generally safe. However, it may affect the overall performance and perceived dyspnea, especially if translated to a more strenuous activity. Among healthy participants with normal simple TFT, we found a transient drop of more than 3% in O2 saturations during the test, which may affect the overall exercise capacity. And lastly, for the clinical implications, there should be proper management of expectations among people who wear different masks in performing activities of submaximal exercise. Uniformity of mask use should be considered among those who undergo serial six-minute walk test and cautious interpretation and comparison of results should be done in patients who perform serial six-minute walk test wearing different kinds of masks. Thank you. Thank you.

effects of masks

six-minute walk test

healthy healthcare workers

randomized crossover design

surgical masks

N95 respirators

oxygen saturation

perceived dyspnea

modified Borg score