Well, thank you, John. And before I start, I just want to give a huge shout out to Danielle, because I'm not somebody who identifies as a data scientist, and it's kind of magic to me how you manage to sort of demystify some of these processes. So thank you for making me feel less dumb. I really appreciate that. It's a real pleasure to be here for this session and with all these great partners. So in terms of disclosures, in 2022, I did serve on the Scientific Advisory Panel for Endpoint Health, which is a commercial health entity looking at precision diagnostics and therapeutics for sepsis. And just as sort of a system-wide disclosure, I happen to be the incoming vice chair for the SCCM Discovery Network, so I'll talk a little bit about that. So Dr. Gong already kind of covered some of this territory, but I just want to sort of bring this back up. And, again, as somebody who doesn't sort of primarily identify as a data scientist, I think it's always helpful for me, as I think through this, to kind of review what are the concepts that we're talking about here. And so there's sort of this idea of real-world data, real-world evidence. And just to reemphasize what Michelle was saying earlier, real-world data is just routinely collected data, often from the health care delivery process, and it can have myriad different sources, right? Electronic health record data, registries, the apps on our wrists, claims data, et cetera. And real-world data has the potential to generate real-world evidence. And in that setting can help, hopefully, bridge this gap between clinical research in a sort of a very traditional RCT-controlled environment model into clinical practice. It has, obviously, potential for designing and conducting confirmatory trials, and Dr. Gong already talked about some of this target trial emulation work that I think is really fascinating. And I think in a really interesting way, and this kind of also tags onto what Danielle was just saying, allows us to answer questions that would be really difficult to address with sort of traditional trial design methodology. So I think the appeal of real-world data and real-world evidence is really very appealing and very evident to us. And we'll talk, I think, in some of the subsequent sessions about some of the potential limitations of this that were alluded to earlier. So shifting just a minute from real-world data and real-world evidence to this idea of an embedded trial, and these are intrinsically pragmatic trials, right? So when you look at that sort of precise two-diagram of a trial and its pragmatic things, embedded trial is intrinsically pragmatic. It should be set in routine healthcare delivery. Most of them will have very broad eligibility criteria and use routinely collected data, most oftenly from the electronic health record, in order to minimize sort of the additional burden and infrastructure requirements of conducting the trial. And fascinatingly, you can do different trial designs with embedded trials, right? You can randomize by patients in this. You can do observational trials clearly with this. You can do cluster-randomized trials where you randomize, and Michelle showed some examples of some of the fluid trials, right, of cluster randomization to balance crystalloids versus normal saline for resuscitation. So cluster randomization by unit or hospital or whatever unit you choose. There are lots of advantages, some probably that have panned out, some that are largely theoretical to doing embedded trials. And there are some disadvantages, and I know one of the subsequent talks in this session will sort of delve more deeply into some of the cautions around the idea of doing embedded trials and the use of real-world data. So I think one of the big advantages to the idea of doing an embedded trial is access. And this is something that probably I don't personally think is talked about enough in terms of the access to research and the access to participate in research. And it is, you know, if you attended the morning session, this morning the keynote speaker or the thought leader speaker focused a lot on health equity. And I would put broader access to participation in research as another endpoint that we should be seeking health equity for. So there is equity in institutions that may be able to participate, ideally, right, in an embedded trial with support from tools like the EDGE tool and others, may require less infrastructure and may require fewer resources to be locally available. So for those who practice in community hospital settings where you don't necessarily have a big research machine at your institution, the idea of an embedded trial where you don't necessarily need as much local resources on the ground to be able to participate and offer research opportunities to your patients is very appealing. Sort of echoing some of the earlier comments as well, embedded trials are hopefully more generalizable, right? They really do reflect more real world circumstances rather than the idealized world of a highly controlled traditional RCT model. And I think John mentioned this in his early comments, both the reduced cost as well as hopefully a sort of tighter turnaround time to trial results when you embed a trial into clinical practice. And something near and dear to my heart is, in coming out of COVID and the sort of flood of research that came out of COVID, is also this idea that an embedded trials and particularly embedded trial networks are more readily scalable to do high quality research in an emergency. So that building the infrastructure in this sort of inter-emergency time period that we're now hopefully back in, I think is a great idea in order to better prepare us for the next emergency. There's also tons of disadvantages to the embedded model. And I think we'll touch on this later, is that, you know, back to this garbage in, garbage out concept, is that anything that we do that relies upon the embedded biases that we already have in clinical care delivery when we design a trial around that risks both reinforcing and perpetuating those existing biases in clinical care. There are methodologic concerns that can be sort of addressed with some statistical analyses, but it's not the sort of pure traditional RCT model. There's lots of issues around data collection and privacy concerns that tools like Edge are trying to help us overcome. But I think institutions still have a great deal of caution about their data leaving the proverbial firewalls of their institutions. And I think there will be a learning curve around that in terms of getting folks to a place where we can feel confident that we have the best data protection and privacy protections in place, but also getting institutions familiar enough with these types of tools that I think are relatively new, and although growing in prominence, and getting people comfortable enough to be able to be willing to potentially put their data into these systems. And another comment that John, I think, mentioned in his introductory remarks is that the funding model for this type of work is not well-defined. And I think that's particularly here in the United States. So how do these potential trials get funded, I think, remains a challenge here in the U.S. particularly. There are lots of examples of recent embedded trials. Because this is a session sort of about moving beyond COVID, I just want to talk about these two kind of platform-adaptive embedded trials. And so REMAP-CAP is one that I think within the critical care community we are very familiar with from it. And, you know, not the embedded elements in this trial were really kind of, you can distill them down into embedding the concepts of patient identification and eligibility, so using embedded elements to identify, enroll, and follow patients all from the EHR. The REMAP-CAP platform does embed the data acquisition piece, so the research outcomes and endpoints are all aligned with clinically available outcomes from it, and evidence integration, so results come out really quickly. And so you saw that, I think, relatively nicely in the pandemic that REMAP-CAP was publishing as clinical care was evolving and that this timely publication of high-quality research enabled us to change clinical practice on the ground. If you look at iSPY-2, which is a different platform-adaptive randomized trial, it's open to all. It's really about breast cancer, looking at investigational and repurposed drugs for breast cancers, and they're looking at adding repurposed drugs to sort of standard of care neoadjuvant therapy. Does that increase the likelihood of a pathologic response for breast cancer patients? And if you look at the embedded elements in that trial platform, it's really, again, this idea of data acquisition and interventions, and then evidence integration as well. So it's really nice. You can think about what aspects of a trial may be embedded, and there probably is some trials out there that sort of do a hybrid approach where some elements are embedded and others are not. They all fall under this umbrella of pragmatic trials. Those of you who know me outside of this setting will know that one thing that I'm particularly interested in, obviously, is sepsis care for patients. And I would take a moment to kind of suggest to you the use case that sepsis is a syndrome that is particularly attractive for us in critical care as the basis for an embedded platform trial from that. And I'll tell you my rationale for that. I'm not the only one who thinks this. I'm sort of proselytizing as a sole voice for this. But as we all know, sepsis is common. It's very deadly. It's extremely expensive. And for those of us who have been practicing critical care for the last 20-some years, we also know that advances in treatment have been exceptionally difficult to come by, particularly using our traditional clinical trials model of conducting a freestanding RCT of a single agent or intervention for patients with sepsis. And there's a lot of reasons for that and a lot of theories about why that might be. And secondly, I would advocate to you that many of the research questions that we think of in terms of how we take care of patients with sepsis are difficult to fund as standalone trials. And those, to me, are questions around systems of care. So we've put a lot of work into designing fluid trials for sepsis. And then they don't show any difference in outcome. And we critique the design of like, oh, well, that's not how care is practiced. That's not what we actually do. So I think strategies of care and supportive care delivery are something that blend themselves very nicely to embedded trials. And obviously, this idea of using repurposed drugs as well, not just sort of in the latest, greatest R&D agent. One particular aspect I think that also, and I believe the question from the audience to Dr. Gong got at this as well, is this idea of heterogeneity has limited our traditional trials in sepsis. And so I deliberately chose the framing that sepsis is a syndrome, not a diagnosis, right? It's a constellation of clinical findings. And there's been beautiful work that's coming out, obviously, about identifying phenotypes, endotypes, subphenotypes of patients with sepsis. And there's many lovely position papers and panel discussions that have been held that talk about the failure of RCTs in sepsis to show a difference may be in part attributable to this heterogeneity of treatment effect, right? That we're treating people with a heterogeneous syndrome all the same with a trial drug and not showing a difference. And that if we were better at predictive enrichment or better at prognostic enrichment, that we would be better able to design these trials from that. And I think embedded trials have a real role in helping us define that as sort of an exploratory piece and looking at the heterogeneity of treatment effect amongst different phenotypes or endotypes of sepsis patients. And lastly, I think going back to the advantages of embedded trials as a tool for advancing health equity, using an embedded trial in sepsis will hopefully allow us to enroll populations of patients who have been traditionally under-enrolled in clinical trials and who experience disproportionately bad outcomes from sepsis compared to those who have been better represented in clinical trials from that. So I'm going to move on from my pick-up-the-sepsis-torch-in-the-clinical-trial and go back to some of the challenges with embedded trials in critical care. And I'm specifically doing this within the U.S. context because I think others around the world have done this better than we have. And one is, as Danielle was alluding to, there are a multitude of electronic health records out there, and everybody has their data definitions separately. We do not yet have a clearly standardized data structure. We don't collect the same variables at different clinical sites. And so we are left to do sort of post-hoc trying to integrate this, which is an amazing effort, but it's not necessarily the same as if we define these things ahead of time a priori. And that's a challenge, I think, in a country where we have numerous, numerous, numerous health systems and every installation of an electronic health record, even though they do talk to each other, are fundamentally different at a local level. We also are in a country where many institutions and sites don't have an established, robust infrastructure to conduct research. This makes meeting regulatory standards a challenge for folks. It does heighten some of the data privacy concerns as well. And when you're looking at, for example, at something like the REMAP-CAP trial, right, they had extensive training, right? They have the—Australia, in particular, has a research coordinator at each facility that has an intensive care unit. And so they have the opportunity to really standardize some of the training around enrollment of patients. And so even the thought of being able to do that in the United States and saying that, you know, we can train staff on site at each of these institutions to be able to do a proper consent process, that is a heavy lift in terms of what to—I think there's real space there for us to make a lot of progress. And lastly, of course, the idea of funding. Again, that this is not—we are looking for our traditional investigator-initiated research funding model does not lend itself well to what is fundamentally a research infrastructure project that then can help us answer questions that are hypothesis—generated by hypotheses. Wearing discovery hat, I want to just take a moment and kind of talk about what SCCM's discovery is working on in this space, and it's largely around collaboration, which I think is a really great role for SCCM and discovery here. So there's ongoing work that's going to help advance the real-world data, real-world evidence generation, and embedded clinical care trials. We're going to hear later about the collaboration with the CURE Drug Repurposing Collaboratory. We just heard about the collaboration with the EDGE tool to help sort of democratize access to data. And then there are other efforts around standardizing data definitions, specifically the C2D2 effort to kind of look at data elements and try to get us to talk about the same data in the same way. And going back to, again, some of Dr. DeGong's comments from her initial talk, this matters, right, when we're talking about timing of therapeutics, right, dosing, what does pre-hospital corticosteroids mean? Does it mean the same thing to me as it means to Dr. Gong, or does it mean something different? So we have to kind of decide on the meaning of different data variables and come to a consensus around that. And you can see a lot more about what discovery is doing through the SCCM website. Hopefully now it's up working again. I also do want to call your attention, as sort of a proof of concept of some of this work, there's a really cool abstract being presented tomorrow, or sorry, on Tuesday the 23rd about basically doing a clinical trial emulation or simulation of the recovery dexamethasone trial. So a fairly, you know, even recovery was a fairly simple embedded trial, not a million data elements, but a really cool abstract basically saying that using the EHR data and using the EDGE tool, they were able to replicate these trial results from over 10,000 patients who are hospitalized with COVID, mostly here in the United States. There's a propensity matching analysis of this, and the results aligned really nicely with the recovery trial. Again, recovery is a fairly simple trial structure, but it is a nice proof of concept that using these tools, you can emulate the results. And again, the point estimates of benefit with reduced risk of death for those patients who were on oxygen or mechanical ventilation receiving corticosteroids compared to not, there was a very similar point estimate to the recovery trial. So I'm going to just wrap up by saying that I think we have a ways to go to really leverage real-world data and embedded trials to generate real-world evidence that impacts our critically ill patients at the bedside. I do think we're on the journey, that it's a long journey though. And there's a lot of advantages to this approach. Again, I would say sepsis is a great case example of this. It's a syndrome that we all struggle with, where the winds have been sort of hard to come by. This approach really does address some of the limitations of traditional trials, particularly hopefully around heterogeneity of treatment effect and access to research. The reduced cost is obviously a huge appeal as we think about this and the timeliness of this. There are also, I think, barriers to this approach for research in the absence of a single healthcare system works against us. The lack of common data structure and elements. We don't have an incentivization structure for institutions to participate in research. We're really relying on people's intellectual curiosity and altruism to do this for the most part. And research infrastructure varies very substantially across hospitals and regions within the United States. I think there are unanswered concerns about data protection and privacy that still need additional work around that. And I think there are very legitimate concerns about perpetuation of existing bias in clinical care through the application of these. I think that the EDGE tool, the CDRC, SCCM, the C2D2 effort may help overcome some of these barriers. And I hope to see many of you on this journey together as we hopefully improve care for our patients. So I'll stop and say thanks.

real-world data

embedded trials

health equity

data privacy

sepsis