Congratulations. Thank you very much, Ashish, and thank you all for sticking around to hear me. So this talk is going to be on pulmonary physiology at the bedside, but I'll try to weave in a bit of my own personal research journey. So these are my disclosures. And it all started for me at the bedside. So this was a patient that we took care of in my fellowship, multiple trauma, ARDS, very seriously injured. And we found him on these ventilator settings in the morning, so CMV, lowish tidal volume. I don't know how they got to that number exactly, but OK, FiO2 of 1 and a PEEP of 13. And my division director at the time said, why don't we call Steve Loring? He's the smartest guy around here. So we called Steve Loring, and I'll get back to him in a second. The question was, how much PEEP to use? And there's multiple studies in the literature then and subsequently, all of which are very confusing. So this is the ARDSNet alveoli trial. This is the Canadian trial, which was published shortly after. Both were completely flat negative, showed no benefit to higher versus lower levels of PEEP. There's a couple other trials in the literature. So this is the EXPRESS trial from France. Here they tried to be a little smarter. They set the PEEP to push the plateau pressure to 28, a strategy which doesn't make a lot of sense to me, because that means that the most compliant lungs and the healthiest lungs get the highest levels of PEEP. And then this is the ART trial recently published out of Brazil, which actually showed injury from high PEEP. But if you dive into it, it wasn't actually from the higher PEEP. It was from the very aggressive recruitment maneuvers that were delivered with the trial. So a confusing literature and difficult to know what to do. Now why is this literature confusing? Because if you take two research strategies, so a control arm with a lower level of PEEP and a intervention arm with a higher level of PEEP, and you include all comer patients in the trial, you're going to essentially be randomizing two separate groups of patients. You're going to be randomizing patients who are non-recruitable, in whom if you increase the PEEP, you increase the plateau pressure and you increase the driving pressure. Those patients are going to be injured by the PEEP. On the other hand, recruitable patients, you increase the PEEP and the plateau pressure stays the same or goes up somewhat, but the driving pressure will go down. Those are recruitable patients, and in them the benefit of high PEEP will outweigh the harm. The issue is if we include all these patients in our trial, some are injured, some are harmed, and the sum total is no benefit. So we need to be smarter about designing our trials and figuring out who's recruitable and who's not. There's been a lot of ways in the literature described to do that, but really we have focused on titrating mechanical ventilation to trans-pulmonary pressure. So when we measure pressures on our ventilator, we're measuring them here at the airway. But airway pressure is not the pressure that distends the lungs. The pressure that distends the lungs is the trans-pulmonary pressure, and that is the airway pressure minus the pleural pressure. Think of the pleural pressure as the weight of the chest wall pushing down on the lungs. You have to overcome that before you even begin to open up the lungs. And we showed way back in 2006 that the trans-pulmonary pressure varies widely from airway pressure. So at any given airway pressure, you can have a very different trans-pulmonary pressure. And if you don't measure the pleural pressure, you can't know what your trans-pulmonary pressure is. Now the real way to measure pleural pressure is to stick a pressure measuring wafer in the chest wall, transduce that, but it's not really realistic in live patients. So we substitute that with the esophageal balloon catheter. This is a catheter that we place through the esophagus. It sits behind the heart, and it measures the pressure in the esophagus. So the esophagus is an extrapleural organ, more or less at the center of the chest. So it's a good substitute for the pleural pressure. And I mentioned Steve Loring. This is Steve in action here, one of my great mentors. And this is Ray Ritz, a respiratory therapist from whom I learned pretty much everything I know. A great example for this society of how multidisciplinary critical care works. So these two were really instrumental in everything to follow. So the research question for us is, if we measure pressure here, and then we calculate the trans-pulmonary pressure, and then we match the PEEP to maintain a positive trans-pulmonary pressure, can we prevent that collapse of the lung at end expiration? So would that be the appropriate way to set the PEEP, by the trans-pulmonary pressure? And there's some evidence to show that this may actually work in actual lungs. So this is an interesting study by Lorenzo Berra at MGH. He measured the trans-pulmonary pressure here, and he measured collapse on EIT here. And you can see that below a certain level of PEEP, we begin to see collapse of the lung. That by coincidence is the PEEP level where the trans-pulmonary pressure is zero. So if I maintain a PEEP level of trans-pulmonary pressure greater than zero, I may be preventing this collapse of the lung. So there's imaging evidence to support this approach. So back to our patient, Steve came and we made measurements. We measured the airway pressure, we measured the pressure in the esophagus, and then our computer integrated that and measured trans-pulmonary pressure. So this patient had a PEEP of 13 and an airway plateau pressure of 40. The esophageal pressure was very high due to his injuries. And the trans-pulmonary pressure was negative eight at end expiration. So there wasn't an actual vacuum in the lungs, there was no negative pressure. It just meant that the weight of the chest wall was higher than the pressure in the lung. And that led to collapse. So what did we do? We increased the PEEP from 13 to 26. That increased the plateau pressure from 40 to 46. How many people put a PEEP of 26 routinely? I don't see any hands. But this is what that looked like. So the trans-pulmonary pressure at end expiration was now four. So these airway pressures are scary. These trans-pulmonary pressures are entirely normal. And we wouldn't know that unless we measured. If you calculate the driving pressure, so it went down from 27 to 20. So this patient clearly fell into that recruitable category. And the driving pressure of 20 is not great, but it's sure better than 27, right? So together, Steve and I designed this first randomized control trial of using esophageal guided PEEP versus empiric low PEEP. That trial just scraped statistical significance. We were lucky to publish it in the New England Journal. And I think the reason wasn't the significance, but the novelty. And it was entirely new at the time. So that was the EPVENT1 trial showing a superiority of esophageal pressure guided measure PEEP versus standard low PEEP. We followed that with the EPVENT2 trial. Here we compared esophageal guided PEEP versus empiric high PEEP. And unsurprisingly, the follow-on trial was negative. And you know, so far, so bad. This is a typical story in critical care medicine. But when you do a trial, you really collect a lot of data, and you have to look behind the bottom line results. So looking behind the bottom line results, we had, for various reasons, decided to compare to empiric high PEEP. And at the time we designed the trial, it seemed like that was the direction people were going. So these were the levels of PEEP delivered. Even at an FiO2 of 0.4, people were getting 10 to 18 of PEEP. When you compare that to what the Lung Safe study shows is actually given in the community, it's nothing like what people give in the community. Nobody gives these levels of empiric high PEEP. So we made a mistake in our control arm. And because we made that mistake, sorry, I skipped ahead. Because we made that mistake, there was no separation in transpulmonary pressure between the two groups. So we took a look at the data, and we found that if we re-divided the patients based on whether they had a transpulmonary pressure around 0, or it was higher or lower than 0, we found that those around 0 had a better mortality. So the goal of the intervention seemed to work. It's just that we had too many crossovers in our two groups. So this seemed to support actually using PEEP set to a transpulmonary pressure near 0. We looked at it a little deeper. So we then looked at the combination of a transpulmonary pressure at or near 0, and a driving pressure less than 12. So if you had a driving pressure less than 12, this was your mortality. If you had a transpulmonary pressure around 0, this was your mortality. If you had neither, you did much worse. If you had both, you did much better. So here we begin to see in the data the opening for an integrated strategy of both PEEP set to a transpulmonary pressure of 0 and driving pressure limitation. And that that is associated with improved mortality. So we wrote a grant around that, and we were lucky to get it funded by NHLBI. My co-investigators, Elias Beydorf-Cassis, Jeremy Beichler, and Tim Houle. So there's a data coordinating center and a clinical coordinating center. This will be a 1,100-patient trial, 20 centers around the United States. And if anybody here would like to talk about joining, please let me know. In the precision ventilation arm, we will set PEEP to a transpulmonary pressure of 0, and then we will reduce tidal volume to get a driving pressure of 12 or less. And there's various ways in the protocol to achieve that, and we know that not everybody is going to achieve this driving pressure, but we will try to get there on everybody. In the intervention arm, instead of specifying high or low PEEP, we're calling it guided usual care, and we're giving very broad ranges to PEEP. And these broad ranges encompass the levels of PEEP seen in multiple clinical trials. We went back and looked at what was seen, and these ranges basically allow clinicians to do pretty much anything they feel is reasonable. The full tidal volume will be 6 to 8 mLs and not looking at driving pressure. So we're very excited to start this. This, you know, I have to thank my lung injury group, Max Schaefer, Elias, Jeremy, Brian O'Gara here in the audience, and Valerie Banner-Goodspeed, our research director, without whom nothing is possible. So I'd like to, you know, thank you for allowing me to share my journey a little bit. I'd like to thank the Society for giving me this award. I accept it on behalf of all of these people who really made me what I am. Thank you very much. And these are my lessons for future investigators, okay? Research begins at the bedside. Mentorship that I receive plenty of is important. Build your team, try and give back, and mostly have fun. Thank you. Thank you. Thanks, Dr. Talmor. We will have time in the end to finish in time, so if there's questions, comments, or anything you want to address with the speakers, please hold your thoughts to the end. Next two speakers are recipients of the Discovery Network's WILE grant, WILE grant named after the famous Max Harry WILE, is usually a single center initiative. It can be basic clinical or translational research. I'm going to first introduce Dr. Katie Moynihan. Katie is a cardiac intensivist at the Boston Children's Hospital and an assistant professor of pediatrics at the Harvard Medical School. She studied medicine in Australia and New Zealand, and that's where she completed her initial training. Her area of work is geographical access to pediatric health care according to social determinants of health. She did share a fun fact with me that her dog is named after a favorite food of hers and a favorite food of mine as well, which is chicken nuggets. So Katie, welcome and congratulations. Thanks for that introduction, an extraordinarily tough act to follow there, and I very much appreciate the lessons learned that you just showed. I have no conflicts of interest in the disclosures relate to the funding from the BCH, ICCTR, and Society of Critical Care Medicine, which I'm super grateful for. Now where you live is a key social determinant of health with many pathways to influence health outcomes, and while centralization promotes quality for infrequently used therapies that are complex like ECMO and PICU, it is at the expense of geographic access with distance unknown impediment to access life-saving therapies, and so uneven geospatial distribution of health care resources according to social determinants of health may intensify inequitable access. But at the same time, social barriers also shape access to care, and non-geographic factors contribute to cross-border patient flows, and there's more to patient care than just a PICU bed. And so in an attempt to study both geographic and non-geographic facets of access, we apply complementary geographic information system and market share approaches to comprehensively analyze distribution and quality of PICU and ECMO capacity in the continental United States. And so we performed a cross-sectional vector-based GIS analysis, delineating drive times of 30, 60, and 120 minutes to hospital locations with more than or equal to one PICU bed or those providing ECMO support, and this was based on 2019 AHA survey data and the Extracorporeal Life Support Organization. And so this creates four catchments at increasing distance from a service with direct geographic access defined by a less than or equal to 60-minute drive time in the literature. And we then described the percent of pediatric population, population density, as well as social determinants of health for each catchment, and the social determinants were based on census data and publicly available metrics, and this was performed at each catchment to compare social determinants of health according to drive time at national, regional, and state levels, as well as cartographically. In the interest of time here, I'll focus on the Child Opportunity Index and the proportion of underrepresented or underserved residents as being representative of neighborhood-level resource availability and diversity, respectively. So the market share approach, National Healthcare Utilization Project, or HCUP data, was used to define bed capacity by allocating ICU capacity to zip codes based on actual PICU admissions in patients less than 14 years. And we did this for admissions to hospital with greater than or equal to one PICU bed based on the AHA data. And to examine more specialized PICU bed capacity, we restricted the PICU admissions to dedicated children's hospitals and hospitals with the top 25% of pediatric discharges. And for ECMO capacity, again, we used ELSA. And we then tested for disparities in access to capacity by studying the hospitals serving patients with different racial and ethnic backgrounds and according to measures of social advantage, and here, again, I'll focus on COI as representative of these social metrics. Finally, to explore quality of each hospital, we examined variables available in the AHA, HCUP, and also three years of CMMS beneficiary data aggregated at the hospital level. And that included the structural variables, inputs or outputs per bed, as well as some patient outcomes. And I'll start out by describing our geospatial results. And we identified 337 PICUs and 199 pediatric ECMO centers. And 80% of children have direct geographic access to PICU services, while 5% of children reside more than two hours from PICU. Just under 70% have direct geographic access to an ECMO center, and almost 10% live more than two hours away. And at a national level, neighborhoods with direct geographic access to PICU services have statistically greater social advantage, and this was indicated by a six point higher child opportunity index. Closer areas also had a 7% higher proportion of underrepresented races compared with children living more than 70 minutes away. And ECMO results really mirrored those for ICU. Obviously there's substantial overlap between those cohorts. Looking at this graphically across the four distance increments, we can see that for both PICU and ECMO, the COI is substantially higher for the less than or equal to 60 minute drive times. In comparison, the percent underrepresented races has a very steep decrement beyond the 30 minutes. And what was really interesting is that at a state level, while the socioeconomic measures formed a more consistent trend, as you can see with the dark line here, the relationships more substantially differed between states in the race and diversity measures. And the south and west, or the northeast and midwest, tracked together regionally. Now maps can identify gaps in coverage and access, and here in these maps, the colored areas have a high population density. So that is more than 1,000 pediatric persons per square mile. And the green areas represent 30 minute drive times from a service. The red areas there represent areas with low COI that have drive times more than 60 minutes, and the blue represents high COI areas with long drive times. In both PICU and ECMO maps, there are substantially more red areas than blue, which reflects the national findings that I just described, but allows a deeper dive into the regional differences. And also at a city level, where using the same color scheme, you can see the red low COI areas with high drive times to ECMO in New York City and LA. One of the innovative aspects of this study was incorporating quality metrics to see if that changed any of the relationships between social determinants of health and geographic access. And we looked at statistical metrics, including missingness, distribution, collinearity in an exploratory data analysis, and also weighed clinical relevance, objectiveness, and data accuracy for all these variables. And ultimately, it led to these variables in red being chosen as the quality metrics to use for adjustment. And interestingly, it resulted in really minimal changes to the mean difference in social determinants of health scores between direct access and more than 60 minute drive times for both PICU and ECMO. And this probably emphasizes both the strength of the baseline relationships we identified, but also some of the challenges in defining and quantifying quality. Using the market share methodology, pediatric ICU capacity for all children was 1.75 beds per 10,000 pediatric residents. And that fell to 1.5 for specialized PICU capacity. And 56% of PICU hospital capacity offered ECMO. And so first, looking at capacity by race, the number of stars here indicates greater statistical significance with the reference groups shown in blue. And so it can be considered with these numbers, as for the average child in this social determinant of health group, what is the ICU capacity serving them? And we can see consistent with some of the geospatial results, Hispanic children had higher local PICU capacity. But this was much less substantive in the specialized analysis, where actually non-Hispanic black children had less access to specialized ICU capacity. Hispanic and Asian children had less ECMO access, while that was actually higher for the non-Hispanic black children. There were no clear trends for COI, except ECMO access was much higher for the very high COI cohort, again, consistent with some of the geospatial findings. Aggregate inputs via pay-a-mix, outputs per bed, as well as the rates of CLABSI are shown here for all children from HCAP. And notably, hospitals serving Hispanic or non-Hispanic black children had substantially lower capital, expenditure, and FTE per bed. Now, these financial dollars are in the range of multiples of 1,000, so represent a really massive financial deficit. A difference in expenditure of up to $130,000 and 1.2 FTE per bed for hospitals serving underrepresented races and ethnicities. And these lower outputs were mirrored by lower inputs, with a much higher proportion of Medicaid pay-a-mix in hospitals serving black or Hispanic children. Relative to the very high COI groups, more socially disadvantaged children were also served by less well-resourced hospitals. Patients from very high COI areas attended hospitals with, on average, $370,000 more dollars expended and 1.5 FTE per bed compared with patients from the very low COI area. And the difference in proportion of Medicaid pay-a-mix was 13% between the two. And finally, CLABSI rates were pretty similar across the board. A few limitations to note, our analyses exclude NICU, AHA, and ELSO data is self-reported, and so there's potential that this doesn't reflect exact contemporary care provision. And HCUP data unfortunately cannot be linked to external sources, and their output and input data includes both pediatric and adult revenues, but the specialised pediatric results that were restricted to high proportion of pediatric discharges showed very similar trends. So in summary, critically ill children living further from acute care tended to have less neighbourhood-level resources. Consistent with urbanisation patterns and historic migration, underrepresented races and ethnicities often live in cities closer to PICUs. However, there's wide variability between states and regions, and PICU capacity specifically differs within the individual underrepresented racial groups. Patients from under-resourced communities or under-represented racial and ethnic backgrounds are more likely to be served by substantially less well-resourced hospitals, and these investment trends were mirrored by the revenue those beds generate based on pay-a-mix. So some conclusions. All PICU beds are not created equal, potentially contributing to disparate outcomes, and so we need better ways to define quality to better assess that. Inequitable access is probably context-specific, requiring analyses that look beyond aggregated national results. As paediatric acute care continues to consolidate, resource allocation requires critical review with a health equity lens, and this is going to be important because focusing exclusively on geographic distribution may not be adequate because healthcare consumption is dynamic and quality varies. Ensuring all children have access to high-quality acute care requires more bottom-up approaches to break the cycle of disinvestment. I really want to thank all the people on this list and many, many others who've helped to complete this. Thanks very much. Thank you. Thanks, Katie. Thank you. Thanks, Katie. So next we'll go to our WILE Research Grant Recipient for 2022, right? This is Dr. Blair Wendland. She had to take a break for maternity leave, and now she's back this year to present her work, so congratulations. And I'm going to give a quick introduction. So your work is on—a broad focus of your work is post-traumatic stress disorder and ICU family caregivers with trajectories and risks and associations with patient health outcomes. You are currently serving as an intensivist at the University of North Carolina in Chapel Hill, and your research program is focused on long-term outcomes of critically ill patients and family caregivers and for identification of risk factors and symptoms of post-traumatic stress following ICU admission, measuring associations between caregiver post-traumatic stress and patient health outcomes. Moving forward, one of your goals is to develop an effective family-centered intervention or a set of interventions to support the health and well-being of ICU survivors and their caregivers. So welcome, and congratulations once more. Thank you. Well, thank you so much for that introduction, and thanks again to all of you for sticking around for this last session. It's been so wonderful so far, and I'm excited to be able to be contributing. So I have no disclosures. So symptoms of post-traumatic stress are a core outcome measure following ICU admission, both for patients and their family caregivers. Post-traumatic stress is common. We know that up to one in three family caregivers will experience clinically significant post-traumatic stress symptoms at some point. And while reducing caregiver stress is a stated priority of the SCCM and other leaders in critical care research, interventions to date trying to address post-traumatic stress, which have mostly focused on improved support with communication and decision-making in the ICU, have shown limited success. So my goals with my WILD grant were to try to fill some key knowledge gaps building upon the foundation of these other completed studies to help develop effective support interventions for these patients and their families after ICU discharge. And so under this award, I completed two connected research studies I'm going to present the results of here today. The first was a prospective cohort study to measure post-traumatic stress symptom trajectories over the six months after a loved one's ICU admission for family caregivers. And then in part two, we did a qualitative analysis to learn more about how family caregivers define their own outcomes and to try to get a sense of how much caregiver-defined distress and how they perceive their own distress in their own words aligns with the quantitative measures that we so often use to measure and define caregiver outcomes, quantitative measures of things like post-traumatic stress and other psychological symptoms. So for part one, we enrolled patients experiencing acute cardiorespiratory failure as defined pretty broadly by needing only one or more of the following four things, pressors for shock, non-invasive mechanical ventilation for an acute indication, high flow nasal cannula or invasive mechanical ventilation. We enrolled those patients along with their primary family caregiver within the first 48 hours of ICU admission. And we performed four assessments over time shown here on this timeline. So the first one was at the time of enrollment, again, early in the admission, hospital discharge, and then three and six months later. And at each of these assessment points, we measured caregiver post-traumatic stress symptoms using the Impact Event Scale Revised, or the IESR, and then also measured key patient and caregiver characteristics, and then three and six month outcomes. After data collection was complete, we performed latent class analysis to identify caregiver post-traumatic stress trajectories over that six months, and then went on to look for trajectory predictors and also the association between caregiver trajectories and three and six month patient and caregiver outcomes. So a little bit about our cohort. We enrolled 95 patient caregiver dyads. For the caregivers, mean age is about 54 years. Three quarters were women. 38% of the caregivers were the spouse or the partner, while 35% were the adult child, and then a small collection of parents and other relationships. About a quarter were black, and three quarters were white. For the patients, mean age is about 62 years. 43% were women. I didn't copy the racial characteristics here because the breakdown was almost identical to the caregiver sample, but I'll note that one patient identified as Hispanic ethnicity. 40% of patients were admitted with COVID-19, and then 58% were required in basic mechanical ventilation at the time of enrollment. So this slide here with this figure shows the main set of findings in this portion of the study. And so along the x-axis are the four time points of measurement that I mentioned earlier. The y-axis shows the mean IESR score or the PTSS symptom severity. That score runs from zero to 88. Higher scores indicate worse PTSS. So as you can see, starting at the bottom with the red line, about 51% of our cohort of the caregivers belong to what we call the resilient trajectory. So they started with pretty much absent post-traumatic stress symptoms at enrollment and maintained very low symptom levels over the entire six months. Shown in the green line in the middle, about 33% of caregivers belong to the resolving trajectory. They started with pretty high symptom levels. So on the IESR, scores of 33 indicate clinically significant symptoms. So this mean in this middle group was about 30, indicating, again, pretty high symptoms that then resolved over time without intervention. So that by six months, the symptoms were basically absent. But then a small but important part of our cohort, 16% of these caregivers belong to what we call the chronic post-traumatic stress trajectory. So they started with very high symptom levels within the first 48 hours of the patient's ICU admission, and they stayed high the entire time. We looked at trajectory predictors, and so we found that a set of four characteristics, two belonging to the patient, two belonging to the caregiver, best identified caregivers who are belonging to the chronic trajectory. So we found that if the patient had a high severity of illness at the time of enrollment and a good premorbid functional status combined with the caregiver having a prior history of trauma for themselves and also low resilience, those four things in combination best predicted caregivers belonging to the chronic post-traumatic stress trajectory. And then looking at outcomes, we did not find any statistically significant associations between patient outcomes and caregiver trajectory, but we did find that caregivers in the chronic trajectory suffered from increased challenges at work, so reduced self-perceived efficacy at work and also having to change jobs more often after the loved one's hospital stay. And then also these caregivers in the chronic trajectory had a reduced health-related quality of life as indicated by lower SF-36 scores. And so while we felt that we generated some results that shed some really important light on the risks and contributors to caregiver post-traumatic stress, we kept coming into this sort of discrepancy over and over that was best summarized by this quote here. So one of our family caregivers in her REDCap survey assessing her post-traumatic stress symptoms said that she typed this, these questionnaires are worded so poorly that I don't think you're gonna get valid and reliable results. And so while this was but one quote, this concept came up over and over where these caregivers would be subjectively endorsing tremendous distress, but having really low PTSS scores or vice versa, said they were doing well, but high symptom levels. And so wanted to try to kind of let caregivers tell us things in their own words and try to get at these discrepancies. And so that brings us to part two, is qualitative analysis. So all caregivers who were in the prior cohort that I just presented, everybody who hung with us for the whole six months and completed all their surveys were invited to participate in a semi-structured interview. We had 21 caregivers who took us up on the offer and completed an interview. And this was just one-on-one, our study team and the family caregivers. Interviews took about 45 minutes and covered global perceptions of the caregiver's wellbeing or lack thereof, and then factors that increased or decreased stress all throughout the hospital stay and after. All interviews were coded independently by two coders and all disagreements were resolved. And then we analyzed the data using the framework analysis, using a conceptual framework called the Chronic Traumatic Stress Framework. So our kind of main findings from this was that distress was both common and also multidimensional. And so the 21 caregivers interviewed, about half reported that at six months they were feeling quite distressed, whereas the other half approximately reported they were doing well. Then there were two caregivers who really had sort of this ambivalent mixed wellness and distress outcome that couldn't be neatly categorized. We also found that distress was multidimensional and we found four key dimensions of distress. So I do want to note that all nine of the distressed caregivers did endorse some element of psychological or physical distress symptoms. So there was some post-traumatic stress, anxiety, depression, chronic pain, insomnia, something along those lines. There were also three other elements of distress these caregivers identified as being really important and those are shown here. Caregivers felt they had a low capacity for their own self-care. Many of them felt like they were struggling in the caregiving role, struggling to care for their loved one, and then an ongoing sense of life disruption, which was often endorsed as sort of the sense of not getting back to normal. I'm gonna show a few key quotes here on the following slides to summarize these outcomes. So in a few cases, caregivers did describe symptoms that sounded a lot like post-traumatic stress. And so this was the mother of a woman who's, excuse me, a mother whose daughter died in the ICU on the ventilator of liver failure and septic shock. And she said, I think that it's kind of like when you get hit really hard and you don't feel it at first and later the feeling starts to come back. We've had some enlightening, brutal memories of that time. We're getting there, but I still haven't removed her slippers from my living room and I haven't wrestled her bedroom yet. And so as I pointed out here with the arrows, these statements really do align quite nicely with very typical post-traumatic stress symptoms, things like emotional numbing, flashbacks, and avoidance. More often, this distress was sort of more bundled and went well beyond symptoms. And so this is a woman whose husband survived an ICU stay for severe COVID-19, but he came home with lots of physical, cognitive, and emotional issues. And she said, I've been doing a lot of emotional eating and stuff too, so I've gotten fat. That makes me usually feel worse. Just a lot of domino effect things. It affects my eating, my sleeping, my time management relationships. It affects everything right now. Sometimes I get with him and other people more short, pissed off answers and comments. I sleep a lot, but I think that's a sign of the just tired and stressed and exhausted, so I don't take care of myself. And so again, she's endorsing some psychological and physical distress and also just lots more to what she needs help with and what she's going through. And then the other key thing that we picked up on in these interviews was that while a lot of the caregivers did endorse high quality emotional support in the ICU, this support was not enough to override what they were going through after the ICU stay was over. And so one man whose wife died in the ICU said, the staff did what they could, I think, make me feel better. They let me know what to expect. They told me what they were gonna do to make my wife comfortable so that she didn't suffer. And that was wonderful, that was great, but it doesn't keep you from suffering. And so to wrap this up and bring these two studies together, we found that one in six family caregivers, so a small but important subgroup of caregivers experience serious, persistent, chronic post-traumatic stress at six months. These caregivers can be screened to identify high symptom levels or being at risk for high symptoms in the first 48 hours of ICU admission. And the interventions for these caregivers to reduce post-traumatic stress should incorporate elements of trauma-informed care and resilience building based on our findings that these caregivers have a history of prior trauma and low resilience. But beyond this, we found that about half of caregivers describe an overall sensation of distress at six months that includes, not in many cases, but often extends beyond post-traumatic stress. We found that in-ICU support is important, but may need to be paired with post-discharge support to improve caregiver outcomes. And that in addition to psychological symptoms like post-traumatic stress, interventions should focus on addressing adaptation to help caregivers adapt to their new normal after an ICU stay, and also skill building for patient and self-care. So I will stop there. I have a wonderful group of mentors and collaborators, both at UNC and elsewhere, and I thank you all for your time. Thank you. Thank you for sharing your work with us, and as we come to the end, I'd like to introduce Dr. Jonathan Sevransky, who's going to give us an update on SARI-PREP, which is one of Discovery's program projects. A fairly brief introduction for John, that he's a professor of medicine at Emory University Hospital, and his area of focus of investigation is acute respiratory distress syndrome and septic shock, and we've all read and seen all of his very well-published portfolio of work. Importantly, for Discovery, Dr. Sevransky is one of our founding pillars, should I say, and is also actually a founding member of what led up to Discovery, which was the UCID group, the U.S. Critical Injury and Illness Trials Group, and also he is one of the senior editors of the journal Critical Care Medicine. John, welcome, and we look forward to hearing about SARI. So thanks so much, and I appreciate people sticking through to the bitter end. I have two disclosures to make. The first is, I am not Laura Evans, who was, you probably don't need me to tell you that, but she was scheduled to give this talk, and her flight time got changed. Along with that, while I was a member of this consortium, and I'm delighted to present the results, I'm probably not an expert on anything that I'm going to present here. So please bear with me as I'm going to use non-expert language to describe the work of many, many experts. So these are Laura's disclosures. This was, and the SARI prep, which I'm going to talk about, is funded by the CDC Foundation. And we started out at the beginning of the pandemic trying to figure out whether COVID was different than other severe acute respiratory infections. The definition that we used is listed here. It's been used in a lot of other studies. And one of the things that we weren't clear about at the beginning was whether it was the virus, the patient, or the environment that they were in, or the treatments that they got that was responsible for the outcomes that we saw. And so we really had no idea. And in many ways, our hypotheses at the beginning of this was throwing spaghetti at the wall. We had a lot of things that could happen. And we really didn't know which of these were most important. And fortunately, we had a group of investigators with expertise in hospital stress, in molecular markers, in epidemiology. And we had some case report forms that some of our colleagues across the pond were kind enough to share with us. So really what we wanted to see is what was important. Was it the patient, their treatment? Our primary outcome measure was the number of ventilator-free days. And we did a large number of analyses, again, looking at molecular markers, clinical characteristics, treatments, and then finally wanted to report the natural history of a large cohort of patients. We also, as noted here, we spanned a lot of different disciplines and had a lot of smart people who knew stuff about each of these disciplines. It was a partnership between a large number of academic institutions, a professional society, and the CDC Foundation, which is a nonprofit funder. And as a previous discovery chair, one of the things that I was really interested in doing was building, as an SCCM discovery member, building up our capability to serve as a data coordinating center, which is something that we were able to do with this. So here are the people who participated. You can read the list. And several of the sites had a number of different hospitals that participated. And I'm just going to point out my own hospital has both a safety net hospital as part of that, as well as areas of another hospital that has a very, very different demographic group. So Laura Evans was a PI. George Anissi did a lot of the work on hospital stress. And Pavan Bhattacharya did a lot of the molecular analyses. You can read the remainder of the people here, but this was a collaboration. What I'm going to show you here is some unpublished data that comes from the final analysis that is currently being written up. And it's a busy slide, but one of the things that I think is pretty clear is that there's a difference between COVID and other respiratory infections. In terms of the length of ICU stay, the length of hospital stay, the mortality rates. And the other thing that I think this shows nicely, and you can see from your left the different variants, the different waves, that initially the hospital length of stay was quite long. The mortality rate was fairly high. And as we got better and got some drugs to treat people, all of these things improved, including mortality rate. And the comparator, non-COVID respiratory illness, had a much, much lower mortality rate. So this is from the work of George Anissi. And I want to briefly take you through a very complicated slide that has the different waves of COVID. And what you can see in red is the overall hospital stress. And it wouldn't, I think, surprise most of you to know that hospital stress went up when the number of cases of COVID went up. But it's not exactly in the same time proportion. In different parts of the hospital, the ED got stressed, as you might imagine, a little bit earlier on. The ICU stayed stressed a little bit later on. And stress was defined as doing things differently. This is data that was self-reported. So there are obviously limitations in the data. But it shows that not all aspects of the hospital, not all parts of the hospital will feel or see stress at the same time. So what did we learn from this? Probably the most important thing was that having a group of people who had worked together before let us ramp up relatively quickly. And having a warm base of research is essential. The way that we did research prior to COVID, where we take weeks to months to years to put things together, I hope has gone in the rearview mirror. And I hope that now moving forward, now that COVID is gone, that we can continue to be more efficient and effective and have some of the collaborations that we have move from thought to design to funding to outcomes relatively quickly. And with that, I'll thank you for your attention. And appreciate you sticking around to the end.

pulmonary physiology

mechanical ventilation

ARDS

PEEP levels

trans-pulmonary pressure

esophageal balloon catheters

EPVENT trials

bedside research