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 SARIPREP, 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 use 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 and molecular markers and 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 SECM 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 Onisi 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. In the comparator, non-COVID respiratory illness had a much, much lower mortality rate. So this is from the work of George Onisi, 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. And 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. Thank you.

COVID-19

respiratory infections

mortality rates

multidisciplinary collaboration

rapid research adaptations