Good afternoon, everybody. I'm Josh Diamond. I am a transplant pulmonologist at the University of Pennsylvania. This is Debbie Levine. He remembered my name. I did. He practiced it. She's from Stanford. I hope this is going to be a really interesting session, so why don't we get started. Thank you guys all for coming. We are going to first talk to, our first talk is going to be on risk stratification of outcomes following lung transplant by body mass index by Dr. Alexander Ewan. Dr. Ewan, this is very close to all of our hearts. Thank you. We can't wait. As she said, my name is Alex Ewan. I'm a second year pulmonary and critical care medicine fellow over at Sears Eye Medical Center. I'm also one of our members of the lung transplant fellow track. Today, I'll be sharing with you all our lung transplant center's data on outcomes following lung transplantation with attention to body mass index. I have no disclosures, for better or for worse, yet. The objectives for my talk today will be as follows. I want to discuss current ISHLT recommendations on candidate selection with respect to BMI and their basis. Our center's experience with transplantation of patients at increased risk due to elevated BMI and ongoing studies looking at other methods of body composition assessment for candidacy for lung transplant. To begin, the most recent ISHLT consensus document, published in 2021, listed that a BMI of 16 to 17 and a BMI of 30 to 35 represented risk factors for adverse post-lung transplant outcomes. With further extremes, a BMI of less than 16 and a BMI of greater than 35 classified as substantially increased risk. These recommendations are based on papers by Upala and Singer, amongst others, looking at registries that detailed significant associations between elevated BMI, primary graft dysfunction, and post-transplant mortality. It is theorized that this is due to pro-inflammatory states related to leptin from excess apostate, surgical difficulties, and post-transplant care. As we all know, though, many lung transplant candidates present with higher BMIs due to steroid use, amongst other reasons, and exercise limitation from their cardiopulmonary state may inhibit weight loss attempts. Additionally, many patients come to us significantly deconditioned and may not be able to achieve significant weight loss due to the severity of their underlying condition. As we have been anecdotally challenged in the midst of COVID, causing profound disease in patients with metabolic syndrome. Since these studies were done for patients in the 2000s to early 2010s, more surgical techniques and experience with these candidates may attenuate some of that risk. To this end, we sought to review our own data. We had 120 transplant episodes from October of 2020 to September of 2022. Five episodes were excluded due to unstageable primary graft dysfunction within 72 hours, and four other episodes were excluded for re-transplantation due to airway stenosis, leaving us with 115 total lung transplants completed with one year available data. I'm really sorry for the busyness of this slide, but it breaks down our patients by BMI categories. BMI of less than 18, BMI of 18 to less than 25, BMI of 25 to less than 30, and BMI of 30 to less than 35. The brief summary of this slide is that groups were similar in age, female sex, rates of bilateral lung transplant, comorbidities, and most were transplanted due to underlying fibrotic lung disease. This table details our initial surgical approach and the level of circulatory support during the surgery. For patients with a BMI of less than 30, most patients underwent an anterior thoracotomy approach with BMIs over 30 undergoing median sternotomies. And there was a non-statistically significant trend to use of a form of circulatory support in obese patients. Here's a slide for our patients that survived to one year, detailing their immunosuppression regimens in general. We maintain people on triple therapy using a calcineurin inhibitor, anti-metabolite, and prednisone so much as possible. And here's the meat of our data. So this will be summarized in some upcoming slides as well, but displayed are degrees of primary graft dysfunction at 72 hours, days on the ventilator, ICU length of stay, and hospital length of stay, as well as survival at 30 days, 90 days, and one year. This is all in raw, and it's going to be represented again in these following slides. So here's our ventilator days, displayed in box and whisker plots for ventilator days, ICU length of stay, and index hospitalization length of stay. BMI of less than 18 was excluded for calculation purposes here because there were less than five episodes for those. A Kruskal-Wallis test by ranks was calculated for durations for each of these. And just to go into that briefly, this test is essentially a non-parametric ANOVA. And if significance is attained, at least one median amongst groups is different. So for this, ventilator days, there was no difference detected. For ICU length of stay, a difference was detected amongst medians. A post hoc done analysis was unable to detect a specific difference between any single group for medians. And for index hospitalization length of stay, there was a significant difference, and post hoc analysis for this showed that that median difference for BMI of 25 to less than 30 and BMI of 30 to less than 35 was significantly different. The observed effect size for this was 0.04. Here is a separate representation of our primary graft dysfunction data and survival at 30 days, 90 days, and one year again. Displayed here for primary graft dysfunction, this is any degree of primary graft dysfunction, not looking at various grades, but any grade of it within the 72 hours. And BMI of less than 18 was included for this. A Fisher's exact test was used to determine if there was a statistically significant association between BMI and primary graft dysfunction, survival at 30 days, 90 days, and one year, and it was found that there was not a statistically significant association for this. Subsequently, we did a logistics regression analysis with BMI as a continuous variable. Odds ratios for these suggested, if anything, that 30-day survival odds improved as BMI increased. That said, the remainder were not found to be significant. So in conclusion, our transplant center data suggests that there is a possible longer length of ICU stay and index hospitalization, but otherwise does not see that BMI has association with worse and short-term outcomes for our transplant patients. This reflects the complexity of body composition and the clinically challenging scenario that we often face with evaluating the obese pre-transplant patient. Thankfully, there are ongoing advancements in model construction for predicting frailty, amongst other factors, using more integrated measures of body composition assessment, including electric bioimpedance, assessing muscle mass, and serum biomarkers, such as IL-6, TNF-alpha, leptin, amongst others, to be able to be included in assessing these patients. This has recently also been published as a lung transplant frailty score by Singer and colleagues. Regardless, holistic factors must always be considered for candidacy beyond BMI. Increasing overall BMI throughout the U.S. challenges us to consider these potential recipients more frequently, and there will be significant work for us to continue to do on the post-transplant side for these patients with new pharmacologics, with newer indications for assisting with weight loss being developed and increasing in popularity. Here are my acknowledgments. I am deeply appreciative of the support of the transplant team at Cedars-Sinai Medical Center. I'm deeply appreciative of the chance to be involved in our patients' care. Again, I thank you so much for the ability to talk with you all today, and thank you for your time. Good afternoon, everyone. My name is Natasha Santosh. I'm a second-year internal medicine resident and an aspiring intensivist. I do want to preface my talk by saying that I do have different results from the Cedars-Sinai lung transplant data that they just presented, but I think that basically goes ahead to underline what we're here for, and that's to see both sides and, you know, eventually figure out what's best for the patients. So this is me. I'm from a community hospital in Shreveport, Louisiana. We are a large tertiary care hospital. We do a bunch of kidney and liver transplants, not lung transplants, but my program encouraged me to go ahead and look at the data for lung transplants given my interest in the field. The objective for this project was to kind of obtain precise data in figuring out what sort of predictors we have to see how people are going to do post-lung transplant if they're needing ECMO during the process. And our ultimate goal was to identify risk factors that we could use to almost prognosticate for patients to see how we can optimize their survival. To give us a brief background, as everyone in this room probably knows, we do about 2,500 lung transplants in the year 2021. The median survival after lung transplant is still about six to seven years, and given the development of ECMO over the last few decades, we're able to use it as a bridge for patients to get to lung transplant in situations that they have end-stage lung disease. We used the UNOS database because that's what we had access to from 2015 to 2022, and we selected all adults that were on ECMO at the time of transplant, and then we used the Kaplan-Meier curve and Cox regression analysis to analyze. So we were left with about 1,285 patients, of which 430 patients had graft failure. So this was the baseline characteristics of our patients. The median age, gender, and race were roughly well-balanced between the two groups. There were certain things that immediately were apparent when we compared the two populations, and we'll go into that with our results as well. We did see that patients that were older, greater than 60 years of age, patients with higher BMI, greater than 30, and with a history of previous intrathoracic organ transplants tended to do worse after the lung transplant. Additional factors were ECMO support required at 72 hours, ventilatory support required at five days, and need for renal replacement therapy of any kind at post-transplant. Also an increased FiO2 requirement was associated with worse prognosis. And here are the results. I've just gone ahead and highlighted the stuff that was statistically significant on this slide. Here's the BMI, greater than 30, and then the others that we just touched upon. These are the survival estimates for these patients. The median survival was about 380 days. And, you know, as you can see on the graph towards the right, patients that required mechanical ventilation for more than five days after transplant were associated with a lower survival rate at five years. This is the survival graph stratified by etiology of the end-stage lung disease. Patients with cystic fibrosis who were on ECMO around the time of transplant had a better graph survival. We also included data about patients with COVID, and they did not do as well. So in conclusion, 35 percent of patients that required ECMO support suffered graph failure in this cohort. Advanced age, obesity, history of previous thoracic organ transplant were independently associated with worse outcomes. In addition to ECMO requirement for more than 72 hours, ventilatory support and increased FiO2 requirement, along with dialysis requirement. And cystic fibrosis patients were seen to do better. Some of the implications of this. First of all, we wanted to repeat this study using the ELSO database. We found that we can get more dynamic data from that database as well, and we want to further stratify. In our study in this iteration, we weren't able to differentiate between – we didn't have data to differentiate between patients on VV-ECMO versus VA, or at what point they were put onto ECMO. We also need further multivariate analysis to try and see – to further characterize these risk factors and also understand what drives the graph failure in these patients. Ultimately, we hope that this would reach to practices that would help us identify who is more likely to do well and how we can optimize patients that are not as likely to do well on ECMO. Thank you so much. Good afternoon, everyone. Thank you for allowing me to present my study. My name is Areeba, and I'm a postdoc research fellow at Norton Thoracic Institute, Phoenix, Arizona. And this is my study, and this is the faculty at the center. I have no disclosures. So earlier this year, my co-authors and I were invited to publish an editorial commenting on the importance of patients' preoperative functional status on post-redo lung transplant outcomes that has attracted much attention in recent organ transplant literature. However, after the editorial was published, we were motivated to explore our own center's outcomes following redo lung transplant and understand what factors influence observed differences in clinical outcomes after retransplanting compared to primary lung transplants. We also want to generalize these findings for future considerations in our redo transplant cohort where appropriate. So lung transplant is an option for definitive treatment of lung allograft failure in carefully selected candidates. It carries obvious risk that should be weighed in the context of several competing interests. Extensive efforts have been made towards understanding how to best balance these competing interests, such as appropriate allocation of a finite pool of donor lungs, the synergy of patient-related and operator risks in redo lung transplant candidates and redo lung transplant procedures, and the goal to continue prolonging survival and quality of life in lung transplant recipients with allograft failure. As redo lung transplant becomes increasingly successful, it becomes even more important to answer these questions regarding what recipients tend to derive the most benefit from this operation in the long term. Presently, the answers to these questions are based on a few large registries and registry analysis that have clearly identified a few risk factors associated with poor outcomes after retransplant, such as older age, female sex, diabetes, pre-op mechanical ventilation, need for pre-op ECMO. Being a high-volume transplant center, we wanted to revisit our center's experience regarding redo. So we retrospectively evaluated all patients who underwent a transplant at Norton Thoracic Institute between June 2010 to December 22. Out of all the patients transplanted, 63 were redo lung transplants. Five had to be excluded because they were multi-organ transplants. In order to enhance comparability and reduce bias, we conducted a 2 is to 1 propensity score matching between primary lung transplants and redo lung transplant patients. The match variables included BMI, sex, UNOS diagnostic group, BMIs, the type of lung transplant, bilateral versus single, pre-transplant mean pulmonary artery pressure, pre-transplant pulmonary capillary wedge pressure, ischemic times, and pre-transplant predicted FEV1 ratios. So from June 2010 to December 22, we had 63 lung transplant patients. 51 were included in the final study. We had to exclude five patients because they were multi-organ transplants and seven had to be excluded when we were propensity matching simply because we misplaced the data. We mainly transplanted for BOSS, but we had seven patients who were transplanted for restrictive CLAD. Our primary outcome was mortality at different time points up to five years. Secondary outcomes included intraoperative mechanical circulatory support, support return to OR for bleeding, PGD at 48, 72 hours after transplant, ECMO after transplant, and duration of post-op mechanical ventilation and post-op hospital length of stay. So this is a chart of the preoperative characteristics of the redo patients versus the propensity match primary lung transplant patients. When comparing the two, we found that the redos were younger. The majority of them were females. More Caucasians were retransplanted compared to other ethnicities. We also saw these patients had a lower BMI. More had an incidence of type 2 diabetes. We thought our cohort of redo would have lower KPS and high LAS scores, and this would be statistically significant, but it did not reach statistical significance. More of the patients in the redo group required hospitalizations prior to their procedure. Of those hospitalized, more required ICU care, although this was not statistically significant, but there was a higher rate in these patients. More patients had increased serum creatinine prior to their transplant and had lower six-minute walk test distances. Interoperatively, we saw that half the patients in the redo group required mechanical circulatory support, cardiopulmonary bypass. PGD rates at 48 and 72 hours were also higher in the retransplant group, although this did not reach statistical significance. More patients in the redo group needed ventilator support, greater than five days. We saw higher rates of bleeding in redo group and return to the operation theater for chest wall re-exploration. We also had higher rates of ECMO support for PGD rescue in patients post-transplants in the redo group. This is our data for chest re-exploration and post-op bleeding. Although we found a higher incidence of DSAs in the redo group, this did not reach statistical significance. Additionally, we saw a significant decline in renal function post-operatively in the redo group and more patients in the redo group required renal replacement therapy. So this is our survival data. After retransplant, we saw a significantly higher portion of patients died in the first year compared to the propensity match controls. However, the one-year conditional survival was comparable between the two groups. Pneumonia, septic shock, respiratory failure, surgical complications were responsible for majority of deaths in the first 30 to 90 days after a redo lung transplant, while chronic rejection was responsible for most of the deaths in the long term. Additionally, we applied a Cox regression model to study this, a multivariate Cox regression model to study the association between multiple predictor variables and mortality outcomes in retransplanted patients. The overall model applied was statistically fit, was statistically significant. The retransplant itself was not an independent risk factor for mortality at our center. However, a low BMI, LAS scores greater than 50, were significantly associated with mortality. Additionally, we saw females and time between primary and retransplant, less than two years, almost reached statistical significance. Interestingly, restrictive CLAD was not associated with adverse survival outcomes in our study cohort, although we had, although this could be a bias because we had only seven patients who were transplanted for restrictive CLAD. We also saw almost twice the patients, there was almost twice the risk of mortality in patients who required cardiopulmonary bypass. We also saw that every 0.3 milligram per deciliter increase in eGFR contributed to increased mortality. And furthermore, patients who require renal replacement therapy were at significantly increased risk for mortality. In conclusion, we see that redo lung transplant may not be inferior to primary lung transplant. Analysis of our five-year group demonstrated favorable short-term outcomes for patients undergoing redos, particularly in terms of 30-day and 90-day mortality, and they were comparable to those observed in primary lung transplant recipients. We had, for redos, our 30-day mortality was 96, our survival for redo was 96%, and at 30 days was 96%, at 90 days it was 89%. In our primary lung transplant group, our 30-day survival was 96 and 92, respectively. Although survival did decrease after 90 days and our one-year survival in the redo group was 70%, we found mortality rates at our institution to be similar between the two groups after one year, as shown in the Kaplan-Meier earlier. Additionally, we recognized several risk factors for mortality, however, CLAD was a major hurdle in our cohort and contributed to significant mortality at one year, and it was a major cause of death at one year, which was significant in our cohort. Our study has several limitations. For starters, it's a single-center experience. Five of our patients were contralateral single lung re-transplants, and we did not differentiate them from the double lung transplants. Our study has selection bias, since we re-transplanted mainly for BOSS and had only seven patients transplanted for restrictive CLAD. Additionally, a small sample size of the re-transplant group produces potential for type two statistical errors in the multivariate analysis. Also, we did not evaluate donor characteristics, such as the donor lung size. Our study pretty much reproduced national data for re-transplantation. The novelty of our study is in what the future holds, including standardizing lung re-transplantation selection criteria, weighted scoring model to stratify the risk for lung re-transplant candidates, the impact of the newly introduced composite allocation score on lung re-transplantation. My co-authors and I would like to thank CHUST for this opportunity to present our data. Thank you. Good afternoon, everyone. I am going to talk about clinical characteristics and outcomes with the use of extracorporeal membrane oxygenation. For interstitial lung disease, a systematic review and meta-analysis. My name is Prashant Balasubramanian. I am a fellow in pulmonary critical care at Mayo Clinic, Florida. And regarding the introduction, so acute exacerbation in ILD has been reported in up to 10% per year. And patients with IPF who presents with an acute exacerbation and requiring mechanical ventilation have mortality rates from 73 to 100%. ECMO has been increasingly used as a salvage support strategy for acute decompensation, as well as a bridge to lung transplantation. However, the indications, outcomes, and complications in this patient population are limited in the existing literature. We don't have any systematic review and meta-analysis in this specific area, and hence we decided to work on it. So we collected the articles published in Medline, Scopus, and Web of Science until 22nd January, 2023. And we updated it on 20th August, 2023, which I'll be presenting in this talk. And our patient population was patients with ILD, intervention ECMO. Comparison group, not treated patients with ILD exacerbation, not treated with ECMO. And the outcome issue being in-hospital mortality. And we included observational studies, case series with more than five patients, and RCT, and, sorry, we excluded case reports, case series of less than five patients, and studies which did not include the data on the outcomes. We adhered to the PRISMA and meta-analysis for prognostic factor studies guidelines. And as you see in the PRISMA flowchart, we identified 1522 studies, and after excluding the duplicates, we had 1,134 articles. And we did, after the initial abstract screening, we identified 104 articles, and we did a full literature review, and finally included 21 studies in the final review. And interestingly, all those 21 studies were retrospective observational studies in nature, with all of them being published after 2010. And of these 21 articles, 20 of them had moderate risk of bias, and one of them had a high risk of bias as identified by the QIPS tool. And of these 21 articles, two of them reported from UNOS Registry at different time points, and one of them from ELSO Registry. And I'll summarize my results in two groups. Part A is the overall cohort, and in that we had, out of 21, after excluding the duplicates from the same institution as well as merging publications from the same author, we identified 18 studies with 1,341 patients, with a mean age of 55.89. The most common mode of ECMO was VV, with 75.3%. And the overall mortality was 52.6. But the mortality was lesser in VA ECMO, 34.2%. And we also did the comparison between the non-survivors and survivors, with respect to the in-hospital mortality. And here, the only factor that was of statistical significance was mean age, so the unadjusted mean difference was higher among the non-survivors, 3.15 years. And interestingly, there was no significant difference with respect to the sex or the type of ECMO, VA versus VV. And also, when we analyzed the unadjusted odds ratio in the studies that reported the outcome between the use of ECMO versus no ECMO in the setting of acute exacerbation of ILD, there was the unadjusted odds ratio looked to be lower in the patients with ECMO, but again, this is not of statistical significance. And regarding the bridge-to-lung transplant cohort, we identified 13 studies with 1,002 patients, mean age of 52.1, 52.2% male, and VV being the most common modality. And as you see towards the bottom of the table, the 30-day and one-year survival post-transplant was 93 and 82%, which is pretty comparable to the overall survival in the UNOS registry. And coming to the bridge-to-lung transplant cohort, the comparison between the non-survivors and survivors, here the main thing that was of statistical significance is the adjusted odds ratio on the use of VA versus VV ECMO. So the use of VA ECMO, surprisingly, has a lower adjusted odds ratio of mortality, 0.62, with a P-value of 0.04. However, there was no statistical difference with the mean age, unadjusted odds ratio of male sex versus female, or the mean difference for LAS, as well as those who are listed versus not listed for lung transplant. And we also did sensitivity and subgroup analysis. We did the sensitivity analysis by excluding the studies that reported data from elsewhere in UNOS registry. Although it decreased the sample size to 434 and 188, respectively, there was no difference in the findings. And we also did subgroup analysis by including the studies that reported only CTD-related ILD versus the studies that reported all-causes ILD, and there were no difference in the findings. So the main points of discussion is we found that the non-survivors had a higher age compared to survivors. Among the patients with ILD supported on ECMO in the overall cohort, but this did not hold true in the bridge-to-transplant cohort. The reason could be because of the sampling bias by avoiding lung transplantation in the higher age group. And the second interesting finding that we had was in the use of ECMO in ILD for bridge-to-transplant, the in-hospital mortality with V-ECMO was lesser compared to V-V-ECMO. But this was of low-grade certainty. The reason being this result is only from two studies, with one of them specifically studied on ILD with pulmonary hypertension, in which case it might be a no-brainer. But on the other study, this is from ELSO registry. And this was irrespective of the pulmonary hypertension status. Patients with end-stage ILD might have pulmonary arterial vasculopathy irrespective of the presence and or severity of the pulmonary hypertension, as shown in a study on explanted lungs, which was published in 2020. So this might be one of the reason to explain our findings that V-ECMO might have a better survival in the bridge-to-transplant population with ILD. So to conclude, use of ECMO in the setting of ILD is feasible with similar odds ratio of mortality compared to patients treated without ECMO. And the mortality might be better with a lesser age, and specifically in the bridge-to-transplant population with the use of V-ECMO. However, we need further prospective studies to better evaluate the use of ECMO in the setting of ILD with hypoxic respiratory failure. I'd like to thank my team, especially Dr. Pramod Guru, who's my mentor and director of our ECMO program. Thank you. All right, so I'm John Rose. I'm a PGY-3 at UT Southwestern in Dallas. I'm gonna talk just briefly about the COVID pandemic, specifically Omicron and pre-Omicron variant outcomes for lung transplant. No financial disclosures. These are the three sort of brief objectives I wanna touch on today, characteristics, mortality, and morbidity. So by brief review, what we looked at at our center was every patient who had had a lung transplant ever who was diagnosed with COVID-19 between March 2020 and February 2022. So this is 163 patients. We kind of grouped them into two groups, the pre-Omicron, which is kind of way back when wild-type UK variant Delta, and then the Omicron, which really extended up into the beginning of BA.2 and then kind of stopped after February 2022. I just included the trends of hospitalizations nationally, just so you could see there were kind of the three peaks when we started having access to more testing. Of course, in March, we didn't have as much testing. Our data really mirrored that. We saw, you know, at the end of 2020, there was a bit of a peak. We had a bit of an attenuated Delta peak, kind of a flattened hump. And then Omicron really, at the end, contributed to about half of these 163 in January. So breaking it down a little bit further, so about 46% or half of these patients had Omicron. It was really interesting, early on, we had a protocol where almost everyone got admitted with a lung transplant who had Delta, and I'm sure you guys probably, maybe you had the same or did not, but 90%. Whereas with Omicron, you know, about two-thirds were admitted. In both groups, though, one out of four would get readmitted for any reason within 30 days, which is fairly high. You know, you're gonna ask, John, but like, were these people even symptomatic? Well, most of them were in both groups, and about half had, you know, fevers or subjective chills. Almost all of them, on the way through each chart, had, you know, headache, myalgia, cough. I did wanna briefly comment on reinfection rate. Interestingly, I only found four cases across our 163 of reinfection, which I was actually really surprised about. All of them were vaccinated with three doses by the time of their second infection. All of them had a positive IgG spike titer at the time of their second infection, except for the unavailable data for number two. Going back, though, to talk about sort of baseline characteristics. I wanna draw your attention to BMI. I know that's been mentioned a lot, obviously, but just for the purposes of this, our Omicron cohort had an overall lower BMI, just kind of how it played out. There was a trend for some changes in FTC at baseline, numerically higher female proportion in Omicron, but overall, other things all being equal. Not surprisingly, there were more opacities in the pre-Omicron cohort. So basically, of the people who had a CT chest, which means basically all the admitted people, almost all of them had opacities in the pre-Omicron cohort, whereas there's a coin flip for Omicron. CRP levels were significantly higher in people who did not have Omicron. We looked at D-dimer, we looked at LDH, and time didn't suffice to go through all those, but suffice it to say those were all equal, including D-dimer. One of the things we noticed is interventions changed. They were sometimes week by week, and so early on in the pandemic, we gave a lot of convalescent plasma for pre-Omicron. I wanna draw your attention to length of stay and then mechanical ventilation. So pre-Omicron, length of stay median was around nine days. It's a pretty long hospitalization for basically a non-transplant patient, but I mean, for transplant, I guess maybe more typical. Median was about five days for Omicron. Almost everyone got steroids according to the recovery trial. One in five people were intubated in the pre-Omicron cohort, and that's including, that's most people, we admitted most people with pre-Omicron as well, whereas there were only about 4% or actually just two cases out of the 50 Omicron that were intubated. Bear with me, we didn't tabulate in full detail for pre-Omicron, so I don't include it, but it was lower than Omicron. So I wanna transition to mortality. So we saw these kind of baseline differences. Despite this, mortality was the same. Around 30-day survival, 88 to 93%. On the left, you see our pre-Omicron. On the right, we have our Omicron. Most folks died within 30 days of contact with the healthcare system, either diagnosis or admission. All our deaths were inpatient, just incidentally. Let's see. When we drill down a little bit higher, I know our numbers are small. Going back, we had 10, 12 deaths in the pre-Omicron cohort and eight deaths in the Omicron cohort. But drilling down, there were about a fourfold higher ratio if you were male for death. And then lower BMI was a little bit protective. Lastly, I wanna talk about morbidity. One of the things we looked at, so on the x-axis, these are just different patients. And basically on the left, we have the pre-Omicron. The blue dots are basically before infection and the baseline FUV. The black dots are after infection at most recent office follow-up. You can see it's all over the map. And one of the things we talked about, I think in previous talks about how CLAD is tough and how like FUV1 and stuff is parametrically so variable. Everyone responded a little bit differently. On average and aggregate, there was a decrease in both groups. But I didn't really know what that meant, you know, five to 7% in each group. So I looked at a little bit differently. So let's say the people who dropped their FUV by 10%. The proportion of people who dropped their FUV by 10% was about the same within statistical error. In other words, around a 30 to 40% of each group had a 10% or more drop in FUV1 at their like ambulatory follow-up. So in conclusion, the Omicron variant was actually associated with lower rates of admission or sort of invasive mechanical ventilation. But despite that, we had lung function loss and a similar mortality. Part of this you may say is due to the power of the study. Those were small event numbers. So we could say we're underpowered and other data, you know, points to worsening severity with Delta. But I think the lung function loss was interesting. I'm happy to take any questions. And I'd like to thank my co-authors and my mentor, Dr. Bhanga. Thank you.

lung transplantation

body mass index

ECMO

COVID-19

obesity evaluation

lung transplant outcomes

Omicron variant