I believe it is about 1030, so we'll go ahead and get started. My name is Dr. Katie Fitton, and I'm going to be moderating this event with my esteemed colleague. So we have some really great cases for you all, and to start us out, we have Dr. Goringer with COVID-associated pulmonary fibrosis, subplural sparing distances, distinguishing feature compared to non-COVID, nonspecific interstitial pneumonia. So thank you very much. Let's welcome Dr. Goringer. All right. Thanks, everybody. Thanks, Chess, for allowing me to present here today. So like she said, my study was COVID-associated pulmonary fibrosis using subplural sparing distances, trying to distinguish between non-COVID NSIP versus COVID-like fibrosis. So my name is Reg Goringer, six-year chief at Creighton University, Omaha, Nebraska, via residency in Denver Med School in Washington. The reason I bring up that my residency is because I ended with COVID, and residency started with the Delta Wave and Fellowship, and by the end of it, I said, there's no way I'm ever doing anything with COVID. It was the C-word to me. We were approached by this research project. I said, no way, but here I am talking about COVID one year later anyways. I have no financial disclosures. I do have a scheduling conflict, so I appreciate you guys being flexible and letting me go first, and we'll go from there. So the background, I mean, we all kind of know NSIP. The study kind of was born pretty easily out of an ILD conference where we were looking at NSIP features, and then we started noticing in our post-COVID clinic that maybe the sparing was a little bit longer, and we have a great chest radiologist at Creighton who approached me with the subject after that, and here we are. So we use subplural sparing to try to distinguish NSIP from other fibrosis, and we'll talk about that a little bit more a little later, but in recent literature, post-COVID infections three to 12 months out following the diagnosis demonstrated these subplural lines that I'll point out in a little bit with associated gap, and so therefore, we were having a lot of this could be COVID-related. This could be NSIP that needs further workup from other etiologies. So in our experience, the subplural distance between the pleural surface and the dominant arc of subplural disease seemed to be longer in COVID. Like I said, we were noticing in our ILD conferences, and so that's how this was kind of born. The specific aim of the study was to demonstrate that subplural distance on CT imaging differentiates post-COVID pulmonary fibrotic-like changes versus non-COVID pulmonary fibrosis, partially because of these pictures here. So one of these is NSIP, and one of them is post-COVID. Probably not a lot of pathologists in the room. If anybody can tell the difference, that's impressive, because I've talked to three different pathologists here and back home trying to distinguish these features, and they said that post-COVID fibrotic changes in NSIP are exactly the same to them. So they cannot tell the difference under the microscope for them. So that's why we tried to say, all right, well, is there a radiographic distance we can find? So we all kind of remember this from medical school and through COVID. This is the diffuse allele of damage, and as you can see, as we get along, here we are now post-COVID. We're in the progressive fibrosis versus stable fibrosis phase, and I think that's where this study is going to come in to the future. Here's the radiographic features to kind of go along with the pathologic features, so the background of COVID progression into the fibrotic-like changes. So you see your ground glass a little bit worse. Now you're starting to have your fibrosis coming, and the ground glass kind of dissipates. So in our method, we used the Nuance mPower tool, which is a fancy radiology thing that they use to be able to pull all the images that, at some point when they're talking in their microphone in their perfectly dark rooms, they say the word subcleral sparing, and we were able to find that image. We found 249 patients. The radiology report was reviewed by the principal radiologist, Dr. Cox, not us. We were blinded by it when we did our images, readings. Then they were retrospectively analyzed by two different participants. I was one of the participants. We had one other participant on the study who read these blind, and the reviewers were blinded to patient data. Like I mentioned, discrepancy was defined as a difference of subcleral sparing and distance greater than three millimeters. So if there was a discrepancy between me and Michelle, who was the other reader, then the board-certified radiologist would come in and decide who was correct. And then the combined results reflect an average of all the reviewers' results, additional recorded patient data separate from image and review included clinical diagnosis as cause of pulmonary fibrosis and general patient demographics. So then we went back and looked, all right, how accurate were we at the very end when we reviewed? We said, this looks like NSIP, this looks like COVID. So terminology, subcleral sparing has been used. So the British Thoracic Society is trying to change this whole thing to dominant arc of subcleral disease. And so for us, we're not smart enough to distinguish the two, so we just use them interchangeably in our study. The dominant arc of fibrotic lung disease, that describes the arc of fibrosis that I'll show you in a little bit. So you can have multiple arcs of fibrosis with NSIP and with COVID changes. The dominant one is the one that has the larger crescent moon around the entire image, and then we measured from halfway point of that. So that's where the discrepancy could come in based on what we were seeing. Fibrotic-like changes, I like to add this. I added this earlier after I went to a talk just because the pathologist said that the fibrotic-like change is a 100% radiologist made up term because to them, it's all the same. So here's an example of our COVID patient. So you can see the arc of dominance right here. We picked about halfway point, and then we measured to the subcleral distance. So in this case, it was 9.4 millimeters. In contrast, this is the NSIP. So you can see the fibrosis arc is here, measuring halfway there, and this one was 3.1 millimeters. And then I like to compare these to the side, side by side. So like I mentioned, you can see here on the left side that there is also an arc here. However, this arc ended just beyond this photo, which is why it was cropped out, and the dominant arc here was used on that. So on average, post-COVID patients demonstrated larger subcleral distance. We had 35 patients total, and compared to CT imaging a patient without a previous COVID diagnosis, we had 44, and it was smaller at 4.32 millimeters versus the 9.48 millimeters. The difference was significant between the two at 5.16. You can see the p-value, but analyzing the results of the individual reviewers prior to settling discrepancies, the statistical significance still maintained there. There was an interesting feature for the reviewers. We were almost perfect, agreeing on the COVID patients, and there was a little bit more discrepancy on the NSIP patients, which I'll talk about in just a second. The expected range of average combined subcleral distance of post-COVID pulmonary fibrosis was 4 to 8, so a little bit larger than NSIP, which was 2 to 3. And then we'll talk about the large differences here on the next slide to read for you guys. So here's our results in graph form, so you can see the non-COVID ones really were a lot smaller than the COVID-like fibrosis patients. This iterator reliability, this is kind of what I was alluding to earlier. This is a radiology research term on how accurate the readers were with agreeing with each other, and this way you can see with COVID we really agreed with each other, and without COVID we didn't. And if I go back, you can kind of see why this would be a little bit harder to pick a spot on NSIP, just because it's smaller, it's less dominant fibrosis, so iterator reliability was just a little bit lower, however it still met clinical significance. So the conclusion, the difference in average subcleral distance on chest CT between post-COVID fibrotic-like changes and non-COVID pulmonary fibrosis is statistically significant when measured from subcleral distance, and this distribution measurement suggests that an upper value of subcleral distance may distinguish a subset of post-COVID fibrotic changes and non-COVID pulmonary fibrosis even though we weren't able to distinguish one of those directly. So the discussion of clinical significance, this is all cool, but this will probably be chalked up into one of those research projects that's like, okay, whatever, I guess, because it really doesn't change anything, so we still have to monitor these, because we still, even though we're seeing that COVID progressive fibrosis is pretty rare compared to stable fibrosis, we still have to screen for those, and you're still going to get screening CTs for your NSIP patients. So maybe future testing with larger cohorts, we could help to distinguish this, but I think most of the time, it may help us in ILD conferences a little bit where we're using it, but it's probably still going to, both patients are going to get work done. I'll leave you guys with that. Are we supposed to take questions? Yeah, and we've got an open, we're open for questions if anyone has any. Going a little long, so I got one minute, it looks like, for questions. Because I disagree. I think it's a fascinating study and will help us in the future for no other reason than if I have a patient that comes in with ILD, and I'm trying to figure out what the nidus of it was, how often does COVID go undetected, and then if this might help us radiographically differentiate it. So yeah, great study. Anyone have any questions? Did you find any other evidence in literature of similar cases? Yes, so we did, it was fairly unique in the way that we did it, there wasn't anything exactly where we compared COVID versus NSIP, but we did find the average distance for NSIP, and for us, we actually read it as a little bit higher than it was before. But like I said, we didn't know whether it was COVID or not, but based on just the distances from the COVID, we were able to pretty accurately say this is COVID versus not COVID. Yeah, I recognize, I'm sure everyone in our audience has been looking at these two, I recognized it right away, that one looked like COVID over the other one. Awesome, well thank you so much. Thank you. Thank you. So I apologize, we're going to go just a little bit out of order. I think next up we will have Dr. Iskander, she's going to talk to us about assessing diaphragmatic function in patients recovering from COVID-19. Well, hello everyone, my name is Karina Iskander, I'm a recent NYU pulmonary and clinical care graduate, now working in Virginia. I'm here to present our work on assessing diaphragmatic function in patients who recovered from COVID-19 in the outpatient setting. I have nothing to disclose. So a common complaint amongst patients who have been infected with SARS-CoV-2, regardless of their disease severity, has been dyspnea well after the initial infection. So traditional methods of investigation, such as CT imaging, pulmonary function testing, are often normal, or the degree of dyspnea is out of proportion to the findings of this testing. So the purpose of this study was, so, and, you know, the musculoskeletal component of the respiratory system, especially the diaphragm, is often overlooked, but can significantly contribute to dyspnea if compromised. So the purpose of this study was to assess the utility of diaphragmatic ultrasound in identifying diaphragmatic dysfunction in patients recovered from COVID. So our methods, so adult patients from the NYU post-COVID clinic with varying severity of dyspnea were enrolled from December 2021 to February of 2023. Using an ultra-portable device, ultrasound of the right diaphragm was performed in the supine position during quiet and deep breathing. Parameters that were evaluated were diaphragmatic thickening and diaphragmatic excursion. I'll be explaining both of those in the next slides. And patient reported dyspnea was scored using the five-point Likert scale for dyspnea. Okay. So here is a demonstration of how diaphragmatic excursion was measured, and this is clips from some of the patients that I scanned. So the ultrasound probe is placed in the subcostal region in the abdominal preset, as outlined in the schematic over here. Only the right hemidiaphragm was evaluated in our study, as the liver window makes the visualization of the hemidiaphragm a lot easier on that side. It's this thick, bright white line seen at the bottom of the screen, labeled in yellow. So we placed the probe, and then M-mode is initiated, and the recording is taken during both quiet and deep breaths. The distance between the baseline and the peak is measured. This is our excursion. So for diaphragmatic thickening, the ultrasound probe is placed on the lateral chest wall. Ideally, you would like to be in the zone of opposition, so where the diaphragm attaches to the inner chest wall. This is where the diaphragm thickens up the most. So in this clip, the diaphragm is the strip of muscle that's in between the two hyperechoic lines, and as it shortens, it thickens. Sorry, when the patient takes a breath, it shortens and it thickens. So diaphragmatic thickness was measured at end exhalation and end inhalation. The thickness was then measured at three different points, and the average of those three values were calculated. The thickening fraction was determined using the following formula. So end inhalation minus end exhalation over end exhalation. So far, our results for the patients were enrolled. Sixty-three percent of them were men. The average age was about 52, and the BMI was about 29. The mean Dyspnea score was 2. The Dyspnea score 1 meant absolutely no shortness of breath, and 5 meant the worst. They could not walk even a step. Seventeen patients had a history of being hospitalized for SARS-CoV-2. Of those 17, eight were intubated, five received paralytics, and three recanulated for ECMO. For those who were intubated, the duration of mechanical ventilation ranged from five to 120 days, so a pretty big range. Adequate images were obtained in 34 patients for diaphragmatic excursion and 37 patients for diaphragmatic thickening. The problem is that what is considered normal excursion is extremely variable, like even in the literature. So some studies quote a normal excursion with tidal breathing is three to five centimeters, some say five to six, and then there's some saying that with deep breathing it's seven to 12, which again is a very wide range. In our study, the average diaphragmatic excursion during tidal breathing was about 2.2 plus or minus 0.7 centimeters, and deep was 5.1. So again, a very wide range. Regardless of what we consider normal, as you can see, we did not find a correlation between dyspnea and excursion. Given this wide range in the normal absolute measurements, we tried to look at the ratio of diaphragmatic excursion during deep breath and tidal breathing. Again, a correlation between this and self-reported dyspnea was not identified. So moving on to the thickening fraction. So the average thickening fraction was 0.66 plus or minus 0.56. There was, again, no correlation between this thickening fraction and dyspnea. When separating those who underwent invasive mechanical ventilation versus those who did not, the diaphragmatic thickening fraction in those who underwent invasive mechanical ventilation was actually lower compared to those who didn't, but this was not statistically significant, and when separating the groups, again, dyspnea did not correlate. And in about 26 patients, PFTs were available, and we, again, did not find a correlation between FBB1, predicted FBB1 and FEC to the thickening fraction. So in conclusion, diaphragmatic thickening fraction and diaphragmatic excursion did not correlate with dyspnea scores in post-COVID patients of mixed severity. Larger studies are needed to specifically investigate diaphragmatic function in patients who were mechanically ventilated during the acute phase of COVID-19. Although larger confirmatory studies are needed in patients recovered from SARS-CoV-2 infection with mild to moderate severity, the sensation of dyspnea is less likely explained by diaphragmatic dysfunction. Thank you. Awesome. Thank you. Hi, my name is Miriam. I'm coming from Scripps Mercy Hospital. Thank you for the introduction and for being here. I'm excited to tell you all about our findings about one-year follow-up of lung ultrasound findings in outpatients with COVID infection, and I have no disclosures. Lung involvement in COVID definitely relates to both disease severity as well as mortality. There's been extensive radiographic studies to that effect, including looking at acute interstitial edema as well as long-term fibrotic changes. But the majority of those studies have been done in folks who've been hospitalized or who had very severe COVID infections. And there's a relative paucity of data and individuals who actually were not hospitalized and had mild to moderate outpatient COVID infection. And therefore, the long-term effects of mild to moderate COVID are largely unknown and may relate to disease severity as well as individual differences and how they mount an immune response. The way we studied COVID infection in our outpatients was using lung ultrasound to detect B lines. At the very top on the right, you see a gentleman that we were imaging at the apices of his lungs. And at the bottom of the page, you see the B lines, which are hyperechoic reverberation artifacts that comet down from the pleural line to the bottom of the screen. What we did was we did these lung ultrasound assessments to detect B lines of follow-up data of patients who had had COVID a year prior. So overall, our goal was to describe the prevalence of persistent B lines at one year after a mild to moderate COVID infection in a cohort of high-risk outpatients who'd been referred by their PCPs to get monoclonal antibodies. So this study was a follow-up on a study that was done at the end of 2021 and at the start of 2022. 201 consecutive patients who'd been referred by their PCPs for monoclonal antibody treatment for their mild to moderate disease were all taken consecutively and imaged using the protocol that you can see on the right. So at the top, we have the two lung apices where the actual ultrasound probe went. B would be like a healthy lung with just regular A lines and C shows the vertical B lines reverberating down the page. If there were three or more B lines, then that was considered positive in any one or more lungs of the patients. There were 55 out of those 201 individuals who tested positive by having at least three B lines in one or more lungs in that original study. So in the current study, a year later, we called up all 55 of those individuals who had positive B lines in 2021-2022 and re-imaged them to look for either the persistence or the resolution of their B lines from that original bout of COVID infection. I got 14 of them to come back in to be re-imaged and also ask them questions about whether or not they got COVID again in the subsequent year, if they had any persistent pulmonary symptoms and if that was associated with their B lines, as well as their vaccination status. And what we found was pretty interesting. So when we lung ultrasounded the 14 individuals who agreed to come back in, eight of them still had the B lines that they demonstrated back in 2021. Seven of those eight were greater than or equal to 65 years of age and only one patient that still had B lines was less than 65 years of age. And that patient had no prior history of lung disease or interstitial issues at all. There were no other significant relationships other than age that we were able to find with our 14 patients who came back in, no relationship with BMI, prior lung disease, or any of the clinical correlates that we looked at as well. Interestingly, despite the mild to moderate nature of their infection and the fact that these patients were never sick enough to need hospitalization, 43% of them had persistent symptoms over the course of the subsequent year. And there was no relationship that we were able to find with the persistent symptoms and the persistence of the B lines over the course of the subsequent year. And again, none of those folks were hospitalized and all of them had received monoclonal antibody infusion as part of the original study. Here's the same data in tabular form, just to give you a big picture of the patients that we looked at. Across the columns, you can see the subjects, their persistence versus resolution of their B lines. And down the rows, it's their different characteristics. So as far as age goes, we see those who are above, generally above the age of 65 were more likely to have persistent B lines. And those who resolved their B lines tended to be a little bit younger. I also wanted to draw your attention to the fourth and fifth row, looking at pulmonary disease and no history of pulmonary disease. Those with pulmonary disease were not necessarily more likely to have persistent B lines a year after that initial bout of COVID infection. And then in the last three rows, those were the clinical questions that we asked the patients. We didn't necessarily find a very striking relationship between clinical findings and the persistence or resolution of the patient's B lines. So overall, this is a descriptive case series of 14 patients who got monoclonal antibody in 2021 and early 2022 for their outpatient COVID infection, came back in, and actually eight of those, so more than half of them ended up having persistent abnormal lung findings in terms of B line positivity. These observations are useful because they can help us generate hypotheses about whether or not a COVID infection, even if it's just mild to moderate, could potentially produce subsequent lung fibrosis, especially in individuals who are over the age of 65. And of course, we need more data to better understand what characteristics and what features of the original infection may make someone more likely to have persistent interstitial abnormalities. Thank you. I'll take questions. All right, so next up, we have Dr. Bird talking to us about quantifying diaphragm blood flow in humans, a novel application of contrast-enhanced ultrasound. Thank you for coming today to Lessons in Imaging from COVID-19. I'm happy to present this work. So before I get started, I'm Jordan Bird from the University of British Columbia, and I've got no financial disclosures to declare. For today, we'll be talking about contrast-enhanced ultrasound to measure diaphragm blood flow, as well as the reliability of this technique and how this is the first minimally invasive way to assess diaphragm blood flow in humans, and how it might be a new way to interrogate the hemodynamics of mechanical ventilation. As a little bit of background, the diaphragm is continuously active throughout life, which makes it incredibly hard to assess for human physiology. Not only that, it's also very hard to access being sequestered between the heart and the stomach, as well as being within the rib cage. And so because of this, all of what we know comes from animal models with over 160 studies in animals, and only one study looking at diaphragm blood flow in humans. This is particularly interesting, as a lot of work in animals has demonstrated that mechanical ventilation severely reduces diaphragm blood flow, but we do not know how to assess this in humans. And so right now, we have no minimally invasive technique to look at this in humans. However, contrast-enhanced ultrasound has good reliability in other skeletal muscles, particularly the gastrocnemius and deltoid muscles, and ultrasound of the diaphragm has excellent reliability over the last couple of years in critical care. So with that, the aim of our study was to assess the feasibility, physiology, and reliability of using contrast-enhanced ultrasound to assess diaphragm blood flow during increasing levels of respiratory work. And we hypothesized that there would be a linear increase in diaphragm blood flow with increasing diaphragmatic work, and that we could get good test, retest, and inner observer reliability for diaphragm blood flow. So how do we measure this? For our methodology, we had 16 healthy subjects come in and do a prescreening questionnaire, along with consent form, before going through a battery of tests to assess pulmonary function, diaphragm thickness, and familiarization with inspiratory threshold loading. What this involves is individuals breathing on a mouthpiece in order to generate enough pressure to lift a weight for inspiratory air flow. In terms of inclusion and exclusion criteria, we used healthy normal intensive subjects that were free from cardiovascular and respiratory disease, and they were excluded if there were any contraindications for our contrast agent or latex lidocaine or antihistamines. In terms of our protocol, this involved two experimental days that were separated by at least 48 hours, where within subjects we randomized the order of these trials at an increasing level of maximal inspiratory pressure, ranging from very little mouth pressure all the way up to 25% of maximal inspiratory pressure. During these five-minute trials, in the last two minutes we used a constant infusion of DFINITY, which is a contrast agent, and then at the very end of these trials we had our participants hold an end expiratory apnea so we could get our diaphragm blood flow measurements. For this, we had mouth pressure in an increasing amount for each of our trials. In order to assess diaphragm blood flow, we used diaphragm ultrasonography at the 8th to 10th medial costal diaphragm space to be able to get our diaphragm in between our liver and intercostal muscles. For our blood flow measures, we used a contrast agent, that is these small microbubbles that are about the same size as red blood cells that allow us to image our blood pool to show up as white. With that, we get an image that looks something like this, where using a high-intensity acoustic pulse, we can burst all these bubbles within our field of view and allow them to reaccumulate. Using some post-processing, we can select a region of interest within the diaphragm and get images that look like this, where on the x-axis we've got time and on the y-axis we've got video intensity. Deconstructing this mathematically, we can get indexes of blood flux, that is how fast these bubbles are coming into the tissue, as well as blood volume for how much of these bubbles are within the tissue. Together, these flux and volume parameters can give us an index of blood flow that is proportional to actual blood flow measurements. We also wanted to assess a control area, and so we also looked at the region of interest within the liver as a non-active control. So what did we find? First, looking at destruction and replenishment curves for our diaphragm, we found that there was an increasing amount of diaphragm blood flow as we increased from baseline all the way up to stage three, and this was consistent from day to day. However, within the liver, there was no real difference in liver blood flow, and so the takeaway from this is that diaphragm blood flow increased with increasing inspiratory threshold loading, however, liver blood flow was unchanged. Next, we wanted to look at the relationship between diaphragm blood flow and diaphragmatic work, and so on the x-axis we've got percent of PDI max, and on the y-axis we've got diaphragm blood flow, where each of those faded lines is individual responses for day one and day two within subject, and those large lines are our group responses, and so as you can see from the statistics for day, we have no difference in terms of this relationship from day to day, and this was also found for the average breath by breath pressure time product of the diaphragm, and so with this, this demonstrates that diaphragm blood flow is linearly related to diaphragmatic work, and this relationship was consistent from day to day. The next thing we wanted to assess was the test-retest reliability for these measurements that we're taking on two separate days, and so on the x-axis we've got day one, and on the y-axis we've got day two, and we found that there was a good to excellent interclass correlation coefficient, which suggests good reliability for the diaphragm, however, the liver did not have great reliability, and so the takeaway from this is that diaphragm blood flow had good to excellent day-to-day reliability, and liver blood flow had poor to moderate day-to-day reliability. Next, we wanted to make sure that this could be assessed by multiple individuals, and so we did inter-observer reliability, and so these were two separate individuals that were scoring our data, and we had observer one on the x-axis, and observer two on the y-axis. We found that diaphragm blood flow had an excellent reliability, and not only that, we had excellent reliability within the liver, and so taken together, diaphragm and liver blood flows have excellent reliability depending on who is scoring them. So, as a little bit of a recap, diaphragm blood flow increased at each stage of inspiratory pressure threshold loading, however, liver blood flow remained fairly consistent. Next, diaphragm blood flow linearly increased with diaphragmatic work, and this mirrors work from the early 1990s where in anesthetized dogs, there was a linear relationship between transdiaphragmatic pressure on the x-axis and phrenic artery total blood flow on the y-axis. And then lastly, we had good to excellent day-to-day reliability and excellent observer-to-observer reliability. So, just to wrap this up, I want to acknowledge the incredible team that we've had over the last couple of years, and specifically my supervisor, Dr. Glenn Foster, for all this work. And with that, thank you, and please don't forget to evaluate the session in the app. All right, so next up, we have point-of-care ultrasound, POCUS use in intensive care unit setting for COVID-19 patients, and evaluation of venous thromboembolism. Thank you for coming. Sorry, I was late. I had another poster I was presenting. My name's Jonathan. I recently did my residency training at Queens Hospital Center, Jamaica, Queens, New York, and I have no financial disclosures. So, the purpose is a systematic review, kind of discussing POCUS and DVTs and PEs. So, the purpose that we did this study, this analysis was, we realized that patients that had PEs, DVTs, they potentially could have significant deterioration, especially with COVID-19. And with COVID-19, when it develops into ARDS, generally, they're mechanically ventilated, and to diagnose PEs with a CTA, generally can be difficult to maneuver into a patient outside the ICU and moving them. So, this study was kind of showing if you can use POCUS along with other tests while the patient's still in the bed to detect if there is a thromboembolic event. This is the method that we used for the systematic review. This was the search. And this is the exclusion criteria for everything that we used. Now, for the meat and potatoes of the study. So, demographic that we used was 285 patients that was admitted with COVID-19. 44 of them were smokers. The average age was 62.37. 20 of these patients had a prior thrombotic event, and 65 of the patients developed ARDS, and 62 acquired mechanical ventilations. I guess three of them were a little bit luckier. So, after we did our study, the gold standard was CTHS to diagnose pulmonary embolism and a lower extremity duplex to diagnose DVT. So, what we did was 36 of the patients had diagnosis of DVT, but we also did a POCUS exam to determine if there was anything. And the sensitivity was 24%. Specificity was 88.8% when comparing the lower extremity duplex to the POCUS. We did the same study with the PE, pulmonary embolism. And with the POCUS exam, we looked for right heart strain specifically. Sensitivity was 40%. Specificity, 83%. The game winner here is when we combine the POCUS exam with the right heart strain and a WELL score. So, with a WELL score greater than two, so three or greater, along with showing right heart strain, we were able to get a sensitivity of 100% and specificity of 80%. So, with this, that point-of-care ultrasound, I know there's a lot of limitations as to who's doing it and a lot of variables to it, but essentially when we combine the POCUS with the WELL score, getting a sensitivity and positive predictive value of 100%, it's pretty significant. And the clinical implications of this is if we're able to diagnose patients with PEs while they're in the ICU and not having to move them around, it can really help just preventing complications of getting the patient out of the ICU and start treatment a lot faster and easier to diagnose, preventing some of the complications of a CTA, acute kidney injury, anything of that sort. Yeah. Thank you so much. Thank you. Okay, all right. I think that concludes our session, then thank you all for coming.

ultrasound

COVID-19

pulmonary fibrosis

diaphragmatic function

lung ultrasound

contrast-enhanced ultrasound

point-of-care ultrasound