So, I'm delighted to introduce our first speaker today, and the speaker is Joanna Klopotowsky. Apologies for the pronunciation. And she's going to talk about the effect of intensive care-tailored computerized decision support alerts on the administration of high-risk drug combinations and their monitoring. And that's the Cluster Randomized Step Wedge trial. And this paper was recently published in The Lancet. Thank you very much, Joanna. Thank you for the introduction. Hello. I work as an assistant professor at the Department of Medical Informatics in Amsterdam, but my background is in clinical pharmacy. So the paper was already introduced, so let's start with the study. First, I have no conflicts of interest to declare. Some background. So, as you probably know, drug-drug interactions cause patient harm. Drug-drug interactions happen when two drugs are administered together, known to interact, which can cause increase or decrease of one or both drugs, causing toxicity or therapy failure. We also know from previous research that ICU patients are more prone to potential drug-drug interactions. This is because ICU patients are treated with a lot of medications at the same time. Clinical decision support systems help to prevent drug-drug interactions by providing alerts when two drugs known to interact are prescribed. However, these systems seem to not well work in the ICU. And this is because ICU environment differs from non-ICU wards. There is a lot of monitoring going on, and also it is often not possible to refrain from prescribing interacting drugs. Therefore, we see very high override rates of potential drug-drug interaction alerts in the ICU, up to 80%. And this is also very nicely explained by a theoretical framework proposed by Seyss et al. In this framework, you see that if a computerized decision support system is not aligned with the environment where it is deployed, it has low sensitivity, sorry, specificity. And in context of clinical decision support system, specificity means that the system gives an alert only when it is necessary. If it is not well aligned with the environment, it produces a lot of alerts with low yield, so false alerts, causing alert fatigue, high override rates, and ignoring the alerts. Therefore, in our study, we hypothesized that by tailoring the system to the ICU setting, will increase specificity of these alerts, resulting in reduced alert fatigue, and therefore more attention to clinically relevant ones. And as a consequence, less exposure to clinically relevant drug-drug interaction will occur, and improve monitoring if one cannot refrain from prescribing such combinations. And how did we test this hypothesis? We conducted a trial in the Netherlands, and included nine ICUs, a mix of academic and non-academic ones, and together they admit around 11,000 people yearly. We used a national knowledge database that contains information about which drugs interact, and this database is used in the Netherlands by all Dutch hospitals. However, the content of this database is not tailored to the ICU environment. All ICUs participating in our trial needed to use one and the same clinical decision support system, called Medication Interaction Module, short of MIM, by the provider Itamedical. At the start of the study, five ICUs were already using this type of system, and four were not using any type of clinical decision support. It is important to stress two definitions to understand our study. First, a potential DDI refers to the administration of two drugs known to interact, and the potential refers to the uncertainty if such administration will lead to actual DDI. Clinically relevant PDDIs in the ICU, we call those high-risk drug combinations. And also, it is important to know that in most studies on PDDI frequency in the ICU, people tend to use drug prescriptions and not drug administration. We deliberately chose for drug administrations, because drug prescriptions don't always get administered, and we really wanted to study exposure to these high-risk drug combinations. Before we started the trial, we completed two other phases, so the whole project consisted of three. In the first phase, we analyzed how often and what type of PDDIs occur in ICU. We analyzed around two million administration records of around 100,000 patients, using data from electronic health record systems. We identified around 200,000 potential drug-drug interactions, which pertained to 270 PDDI types. And to give you one example, a PDDI type, very well known, is a type called QTC prolonging drug interactions. And in this type, many, many drugs can occur in different potential to prolong QTC. Using information from this phase one, we conducted DELFI study to define what is really important in the ICU, so which PDDIs are clinically relevant and which not. And we've done this national study with a panel of experts of ICU physicians and pharmacists, and together they analyzed 184 PDDI types and assessed that 36%, so almost 40%, was not clinically relevant in the ICU environment. So this, you can say, are low-yield alerts. Using this information, we conducted a cluster randomized step-by-step trial in phase three, and the intervention was tailoring the CDSS systems according to the DELFI study, meaning turning alerts on for high-risk drug combinations and turning low-yield alerts off. And this is how it looked. The trial lasted one year, and you see that these nine ICUs were started sequentially every month one, and the sequence was randomized and the intervention was provided at ICU level targeting the physician, because in the Netherlands, only the physician can prescribe drugs. And to assess the effects in the analysis, we compared high-risk drug combinations in the control period, so the control wedge, to the intervention period. Our primary outcome was the number of administered high-risk drug combinations per thousand drug administration per patient and secondary ICU length of stay and the proportion of appropriately monitored high-risk drug combinations. Our statistical analysis were all intentioned to treat basis ones. We used generalized linear mixed effect model to assess the effect, and we created three models, one unadjusted, one adjusted for variables that differ significantly between intervention and control groups, and one adjusted for a number of prognostic factors which might influence the number of drug-to-drug interactions. The results, here you see a summary of patient's characteristics. We included around 10,000 patients divided between control and intervention group, and then looking at differences, we see two that are significantly different. There were slightly more people with medical admission type and slightly more people having COPD, and although these differences are small, they were significant, and therefore we corrected for those differences in our M1 model. Finally, here are the results. In all models, we see a significant reduction in the number of high-risk drug combinations, and to summarize, it is a reduction between 12 to 14%. Regarding the ICU length of stay, we see a reduction of 6 to 10%, and regarding the appropriate monitoring, we see an increase of 9% of appropriate monitoring when one could not refrain from administering high-risk drug combinations. A couple of strengths and limitations to stress. This is a large multi-center study with a strong design, randomized, cluster-randomized step with trial. We measured actual drug administrations as opposed to prescriptions. We conducted a Delphi study to assess the high-risk, low-risk of drug combinations, and besides measuring exposure, we also measured monitoring and ICU length of stay. Regarding limitations, also some other factors may influence effectiveness of the systems, for example, usability, how the alerts look, and when they pop up, but because all the ICUs participating in the trial were using the same CDSS, this limitation is limited. Also, the number of ICUs, the number of clusters was small. It was sufficient for the power and to see a significant effect, however, not big enough to, for example, study differences between academic and non-academic ICUs. Also, we did not assess patient harm associated with this high-risk drug combinations. So main takeaways from the study. This is the first study evaluating tailoring CDSS to the ICU environment on the administration of high-risk drug combinations, the monitoring, and ICU length of stay. Modifying CDSS alerts for PDDIs improves CDSS effectivity. I could say less is more. For practice, of course, this study also shows that the one size does not fit all. You can't have one CDSS system for the whole hospital. And we also believe that our list of this high-risk drug combinations is transferable to other systems and also to ICUs outside Netherlands because the frequency of PDDIs is more or less comparable between countries. And this concept of tailoring CDSS to a specific environment is also broadly applicable. You can think about pediatric neonatology, oncology wards. And also, we hope that this study inspires hospitals to take a more critical approach on all the alerts physicians, nurses are getting. In terms of research, of course, more study are needed to assess tailoring of CDSS to specific environments. It would be good to have a measurement of adverse drug events related to PDDIs. And also, there are many possibilities to improve the PDDI alerts even further. Of course, I did not do this study by myself. Especially, I would like to stress the contribution of Tinka Bakker. She was the PhD student on this project and successfully defended her thesis on 7 December last year. And of course, there were many other people involved and I think a really multidisciplinary project involving medical informatics, intensivists, pharmacists, and methodology experts. And also, of course, the study was not possible without the contribution of ICUs and many collaborating partners. And after the talk, I, of course, have time to take your questions and you can email me or scan this QR code to learn more about the study. Thank you. Thank you. Thank you, Dr. Klopotowska. Our next late-breaking presentation is a paper that is hot off the press in the Journal of Critical Care Medicine in February 2024. And the title of that is Association Between Nurse-Copatient Illness Severity and Mortality in the Intensive Care Unit. I'd like to invite Dr. Katherine Ryman from the University of Pittsburgh School of Medicine and the Department of Critical Care Medicine to present her paper. Thank you. Good afternoon, everyone. Thank you for this opportunity. I'll be presenting on the Association Between Nurse-Copatient Illness Severity and Mortality in the ICU. I first want to begin by acknowledging my funding. I'm funded generously by a T32 and my mentor is Dr. Jeremy Kahn. I got to acknowledge also my co-authors because it takes a village to write a good research paper and so my co-authors. So to start out, as I'm sure you're all aware, nursing workload and outcomes are related. But what we really don't understand is what are these levers for managing nurse workload. Sure, one to two nurse-to-patient ratios are ideal. And we've had legislation that has, in some states, mandated ratios. But these ratios are not always feasible. And in the context of a one to two nurse-to-patient ratio, workload can vary. One to two nurse-to-patient ratios don't always look the same. You can have two patients that look very differently from each other, from assignment to assignment. So I venture to think about some more creative ways that we can manage workload. And one of those ways is considering the co-patient. So in a one to two nurse-to-patient ratio, each patient has a co-patient. So if two people are assigned to a nurse, one patient's illness severity may potentially affect the other one's outcome. So in this study, my goal was to ask, does the condition of one patient affect the nurse's capacity to care for the other? So as I'm sure you're aware, and any nurses in the audience, when you're caring for two patients, there are times when you are in one patient's room most of the shift, and the other patient cannot necessarily get the same care. So I wanted to study this and understand this a little bit more, because plenty of times when I've been working, I've been in one patient's room most of the time. So the objective of the study, as I said, to examine the effect of co-patient illness severity on ICU mortality. So this was a retrospective cohort study using electronic health records from 24 ICUs in eight hospitals from 2018 to 2020. So the way that I linked these nurses and these patients, so one big limitation of a lot of existing nurse workforce research is that it's based on aggregate data on the unit level or the hospital level to outcomes. But using EHR data and metadata, so nurse digital footprint, their breadcrumbs they leave in the EHR documentation of their assessment data and medications, I linked nurses to individual patients. And I got those nurse to patient assignments. So I could link the individual nurse to the patient that they were caring for. So using this validated algorithm and the metadata, I was able to find these assignments to do this study. And you can look at further methods in the paper in JMR Medical Informatics. So the exposure. So I operationalized the exposure co-patient illness severity as a categorical variable with the reference category being neither mechanical ventilation nor vasoactive support. And then there's also mechanical ventilation only, vasoactive support only, and then both mechanical ventilation or vasoactive support. And outcome was 28 day ICU mortality. So I wanted to break down statistical analysis here because we have multiple levels of data in this study. So I did a proportional hazards model with time varying covariates. And this was at the level of the 12 hour nurse shift. So each patient, because it's 28 day ICU mortality, they could have 56 shifts. Not all patients had 56 shifts. So at each shift, the end can vary because a patient can be discharged or die prior to that 56 shift. So then you can kind of see from, there were time invariant covariates, which included age, gender, admission source, and comorbidities as operationalized by Elixhauser. And then covariates that varied by shift, including the patient SOFA score, their number of co-patients. So in this sample, I did a sensitivity analysis in samples that were, had different nurse to patient ratios. But in this primary sample, it was either 1 to 2 ratio or 1 to 3 ratio specifically. And so, but I also did a sensitivity analysis in a cohort that was only 1 to 2 the entire time and also 1 to 1, 1 to 2, and 1 to 3 to make sure that these findings continued through the other cohorts. And so in addition, co-patient illness severity is covariate. And so by saying that these vary by shift, meaning from shift to shift, they are different values. So let's look at the patient cohort. So it's 20,650 patients with most being white and with four or more comorbidities and most admitted via the emergency department. You can see the average SOFA score on admission, and keep in mind that this is at admission because it varies by shift, is about 2.8 with about 8% with an ICU mortality. And then looking at the shift, the average SOFA score at shift was 3.1, and most co-patients were neither mechanically ventilated nor had vasoactive support. And you know, I think this is probably largely attributed to surgical ICUs in the sample. You know, the patient that get extubated pretty quickly and is transferred to the floor. So the findings of the study generally were that when your co-patient is sicker, your risk of death is higher. And keep in mind, this is a retrospective cohort study, so we have limitations on being able to see what the mechanisms are and causality, but you can see that when a patient's on both mechanical ventilation or vasoactive support compared to the reference group, their risk of death is higher. And this is more pronounced in the vasoactive support group only, which I have some hypotheses on why this occurs. And I believe mechanical ventilation only was not observed because, you know, we are largely supported by our respiratory therapists, and they decrease our workload tremendously. And so I think vasoactive support only had the largest effect because the nurse is the effector limb in there. They're delivering the care, and they don't have the support of their respiratory therapist colleagues who are so often helping us in the ICU. So what are some of my conclusions? Well, one to two staffing creates interdependencies that affect patient outcomes. I think that we might be able to intelligently pair patients within assignments to improve outcomes. And I, you know, shameless self-promotion, but I would like to tell you to come to my research snapshot theater because I did a qualitative study looking at what nurses think about the nurse-to-patient assignment process because I think the next step is looking more about how these assignments are made and how is this affecting the nurse workload. And I want to better understand the assignment process from the perspective of nurses because before we implement any intervention that is going to change the way the nurse assignment process is done, you have to hear from the stakeholders themselves because they're the ones who are in the trenches. And so beyond that, I have future plans to look at, I'm curious if, you know, co-patient illness severity affects the completion of SATs and SBTs, requisite care that we need to deliver that maybe the workload of a co-patient might be preventing you from giving to the index patient. Thank you so much for this opportunity and, well, I will hand it off to the next speaker. Thanks so much, Dr. Ryman. A quick note before our next presentation, very recently the editor-in-chief of pediatric critical care medicine, Dr. Robert Tasker, unveiled a new section in PCCM and this next article is a beautiful example of something that fits very nicely into that section. There's a new section in PCCM dedicated specifically to protocols for clinical trials in pediatric critical care medicine as well as feasibility and pilot trials. So definitely have a look out for the text where he describes the specifications for that. So with no further ado, based on that, the title of the next presentation is stress ulcer prophylaxis versus placebo, a blinded randomized controlled pilot trial to evaluate the safety of two strategies in critically ill infants with congenital heart disease or suppress CHD. So I'd like to invite Dr. Kimberly Mills from Boston Children's Hospital to tell us about it. Thank you, Kimberly. All right, thank you for the opportunity to present today at this late-breaking session. I am here to present for our team, which is large as it takes a village to perform a trial like this. And as Sapna said, we were published last week in Pediatric Critical Care Medicine. If you have any questions, you can refer to the article further. Our disclosures are the study was funded by the Gerber Foundation's National Research Grant and then also by Harvard Catalyst Clinical Research Center and the Institutional Centers for Clinical and Translational Research at Boston Children's Hospital. And below, you can see the individual author's support, although all are not necessarily relevant to the trial conduct. To start us off, as all of us know in this room, upper GI bleeding due to stress ulcer formation is a known complication of critical illness. When I think about critical illness, we think about increased catecholamines, low blood pressure, and hypovolemia that lead to low cardiac output. This can result in increased SVR and activation of our inflammatory cascade, which can lead to splint neck hypoperfusion and ischemia, which is often a complication we find in the pediatric cardiac ICU for our patients. The results of splint neck hypoperfusion and ischemia decreases bicarb secretion in the stomach, can decrease GI protective hormones, motility, decreases mucosal blood flow, and increases acidity, which increases the risk for stress ulcer formation. And as we know, those ulcers erode into gastric vessels and can result in upper GI bleeds. Upper GI bleeds have been associated with increased morbidity and mortality in critically ill patients, specifically in increased hospital resource utilization. As you can see on the right, there is an increase with ICU length of stay. And on the left, there's an increase in mortality when we look at adults that are critically ill. To try to combat from stress ulcers forming, since the early 1990s, the practice has generally been to ubiquitously prescribe stress ulcer prophylaxis. And this has eventually transitioned to the pediatric and neonatal ICUs as well. Stress ulcer prophylaxis is prescribed to prevent upper GI bleeds. And in this meta-analysis in 2018, you can see there is a trend towards benefit. But what we've known over, or what we've seen over the last 10 to 15 years, is that there's an increase in the adverse outcomes associated with stress ulcer prophylaxis, and specifically with hospital-acquired infections. The common adage is that pediatric patients are not small adults. And that is certainly true when we think about this as well. Risk factors for upper GI bleeds are unique for children that are critically ill. And that can include organ failure in high ventilator settings. And the adverse outcomes associated with stress ulcer prophylaxis are specifically unique as well. We do see ventilator-associated ammonias that have been previously described by some of the authors in our group. And in the neonatal ICU, they've published a number of studies also with hospital-acquired infections, but specifically necrotizing enterocolitis mortality that has led to a black box warning for most stress ulcer prophylaxis. Despite that, we still prescribe it to our patients. And I'll come back to this picture just to stress that our patients in the pediatric cardiac ICU are at risk for all of, from splint neck hyperperfusion and ischemia on. And specifically in the pediatric cardiac ICU, there's some unique characteristics that put them at higher risk for stress ulcer formation. And specifically, that is most of our patients, somewhere between 70% to 80% are less than one year of age. We're mostly a surgical unit. We have lots of risk factors, including low cardiac output, use of mechanical ventilation both preoperatively and postoperatively, high incidence of end-organ injury, fortunately temporary, and high-risk medication use, and that includes IV NSAIDs, high-dose aspirin, and anticoagulation drugs, and at times, steroids. And so our objectives were to investigate the feasibility of conducting a clinical trial assessing the safety of withholding stress ulcer prophylaxis in infants with congenital heart disease in the cardiac ICU. We defined four a priori criteria for feasibility that included screen rate, consent rate, drug allocation, and protocol adherence, and our secondary outcome was to compare the rate of clinically significant upper GI bleeds, which I'll define in a minute for you, and hospital-associated infections in patients receiving stress ulcer prophylaxis versus placebo. This was a single-center, double-blinded, placebo-controlled pilot feasibility, randomized controlled trial. The inclusion criteria were infants that were less than 12 months of age, as previous papers have shown that the incidence of upper GI bleeds in this patient population is quite low, and at our own institution, over an eight-year period, we only had four episodes of upper GI bleeds. We included patients who were diagnosed with anatomic, myopathic, or arrhythmic heart disease, and we required them to anticipate respiratory support for greater than 24 hours. Respiratory support was defined as either mechanical ventilation, non-invasive ventilation, or high-flow nasal cannula, and the reason we selected this as an inclusion criteria was to try to pick higher-risk patients. Exclusion criteria, some of them make sense. If you had received acid-suppressive therapy for greater than seven days in the last month, a lot of our patients have quote-unquote reflux and are on acid-suppressive therapy as a baseline medication when they come in for procedures. If they had received one dose of acid-suppressive therapy during the eligible admission, if they had an active GI bleed or active H. pylori infection, if they were on high-risk medications, and as I alluded to previously, those included high-dose aspirin, some anticoagulation agents, specifically direct thrombin inhibitors in G2B3A because there are not reversal agents readily available for them in our patient population, and IV NSAIDs if they were planned to receive a course of that in their post-op care. We also excluded patients who had planned a recent GI surgery because most of the time, general surgeons will prescribe reflux medications and were proximal to that, and then patients who required mechanical support. This is a schematic of our study. Randomization was by permeated block, and it was across two treatment groups, and we pre-identified two strata. One was medical-surgical, and one was neonates versus infants. The intervention was an H2 receptor antagonist or placebo. The nurses received individualized dose syringes that were blinded to both the nurse and family and provider were all blinded to them. We selected an H2 receptor antagonist because that was our institutional preference for stress ulcer prophylaxis prior, and also, if I have a minute at the end, I will share, we conducted a survey, and 75% of pediatric cardiac ICUs around the country use H2RAs as their stress ulcer prophylaxis agent of choice. Our outcomes were defined as feasibility, and that was screening rate of greater than 80%, consent rate of greater than 20% based on historical interventional trials in the critical care environment in children, drug allocation, meaning receiving the study drug within 48 hours, and we hope to achieve that 80% of the time, and protocol adherence of greater than 80%. The safety outcomes were incidence of clinically significant upper GI bleeds. We adopted the adult, previous adult definition for clinically significant upper GI bleeds, which was a new bleeding event with either a decrease in your hemoglobin by two, decrease in your mean arterial blood pressure by 10, or increase in vasoactives, or an increase in heart rate by 20, which we also amended to make sure that it was in the absence of arrhythmias, which is a frequent thing we see after congenital heart surgery, if they had an unanticipated transfusion or an unanticipated GI procedure. We also identified hospital-associated infections or acquired infections and used the CDC guidelines for that, minor GI bleeds, which means they did not qualify under the definition of clinically significant upper GI bleeds in both medical and surgical NAC. In the results, we had 1,426 patients, of which we were able to screen 1,425. When we looked at ineligibility, the ones that stuck out the most were patients who did not require respiratory support for greater than 24 hours. We have a robust, enhanced recovery after surgery program at Boston Children's Hospital, which dictates the amount of time we hope they are ventilated postoperatively. And we also had nearly 100 patients who planned to receive IV NSAIDs, as that's one of our multimodal pain treatments for children that are over six months of age. From the 1,400-plus patients that were screened, we had 132 then that were eligible, and 70 were consented. Two patients were consented in their pre-op visit, but then did not require cardiac surgery and therefore did not go to the cardiac ICU, so we randomized 68 patients. In there, we had 34 in the placebo arm and 34 in the H2RA arm or study arm. And we had 28 complete the study in the placebo arm. One came out because they got a direct thrombin inhibitor after having a BT shunt placed, and four received open-label acid suppressive therapy. And then we had 30 complete in the study arm, of which, again, one came out because they had direct thrombin inhibitor after a BTT shunt placed, and then three received open-label acid suppressive therapy after randomization. A real-world figure here in that we started the study in 2019. Some might recall in September of 2019, the FDA recalled ranitidine, which is our original H2RA of choice, and that was due to concerns about increased level of a carcinogenic agent. And so we took an eight-week pause to amend our protocol and switch to famotidine. And then, unfortunately, 2020 came, and we had an eight-month pause for all research at our institution. And so that was the effect of the COVID-19 pandemic. When you look at results, so our overall cohort and then our placebo in our study, you can see that they're equally distributed. Specifically, the strata that we were worried about was age and surgical admission, and they are statistically similar. We had a good mix of patients with both normal, meaning arrhythmia or myopathy, isolated heart disease, complex by being single ventricle between the groups. A majority of our patients were surgically admitted. They had relatively high surgical complexity score with a stat score, median score of four. Their cardiopulmonary bypass times were similar, and their median time on study was similar as well. When we think about our feasibility outcomes, our screening rate was nearly 100 percent. Our consent rate was 53 percent, which comparing to historical studies conducted by the Pediatric Heart Network is within that realm. They range somewhere between 50 to 60 percent. Our drug allocation rate was 100 percent thanks to our investigational drug pharmacy, and our protocol adherence, which we're happiest about, was 85 percent with equal number of open-label acid-suppressive therapy between groups. We did both an intention-to-treat and as-treated analysis for our statistics, but they were similar, so I'll only show you the intention-to-treat analysis here. In none of the patients did we have a clinically significant upper GI bleed. We had one episode of tracheitis and CLABSI in the study arm, and we had one episode of mediastinitis in the placebo arm, so relatively similar distribution of hospital-associated infections. The minor GI bleeds were a little bit higher, although there were three events in one patient, and it still did not reach statistical significance in either the intention-to-treat or as-treated analysis. There was no medical MEC, and there was no surgical MEC. And so, therefore, our conclusions were that a randomized controlled trial assessing the safety of withholding stress ulcer prophylaxis in infants with congenital heart disease is feasible. We had no episodes of significant upper GI bleeds in the trial, importantly, and our next step is to conduct a multicenter trial powered to detect the safety, meaning the incidence of upper GI bleeds and benefits, which hopefully is a decrease in hospital-associated infections of withholding stress ulcer prophylaxis, and we're currently analyzing the gut microbiome profiles of these patients as potentially a mechanistic link between why we see an increase in hospital-associated infections. I think that there would probably be some questions about why we think we need a bigger trial, and so just as a carrot to get people excited, some of this is from a survey that we conducted that has not been published yet and from a review of the FIS database, so we often get the question, is there equipoise? Specifically, our head of cardiac surgery asked us, why are we doing this anymore? Just stop, and so when we surveyed across the countries, there's definitely equipoise about who is prescribing stress ulcer prophylaxis and for what indications, and the perceived incidence of upper GI bleeds is actually quite high, somewhere around 5 percent compared to what is actually experienced, I think in our future trial, we would likely modify the inclusion criteria. I think we felt that being younger was protective, and in fact, the younger patients had an increased incidence of upper GI bleeds, so potentially we would include older patients as well, and in our analysis of the FIS database, mechanical ventilation was actually not a risk factor, and IV NSAIDs were actually not a risk factor, so potentially we would not include those as inclusion or exclusion criteria, which would definitely open up patients eligible for the study. People ask us, do upper GI bleeds actually impact outcomes? And again, looking at the FIS database, it appears that from a resource utilization perspective, they do. They affect the duration mechanical ventilation, ICU length of stay, hospital length of stay, mortality, and costs, and are there adverse outcomes associated with stress ulcer prophylaxis in our patients? And again, universally, there's an increased incidence for ventilator-associated pneumonia, CLABSI, UTIs, surgical site infections, neck and thrombocytopenia, and so I think with all of that evidence, we're in a good position to advocate for a larger trial to see if this is an antiquated practice that we should be done with. So I will take questions after the session, and if anybody wants to partake in the larger study, we're looking for centers, and so please email us. Thank you. Thank you, Dr. Mills. So we're moving on to our last speaker in the session, and I would say that all the papers in this session and the session that you might have attended previously are collated together on the SCCM website under a new releases page, so if you want to check out the papers in more detail, then please check that out. So I'm delighted to introduce Dr. Julianne Barr, who is going to talk about improving outcomes in mechanically ventilated adult ICU patients following implementation of the ICU liberation bundle A to F, and that's across a large system, and this paper's been published in CCE. So over to you then, please, Julianne. Thank you. Thank you. Good afternoon, everyone. This is a great turnout for this very important session. We really all appreciate the fact that you showed up this afternoon, and everybody seems to be awake, so yay. My name is Dr. Julie Barr, and it's a pleasure for me to be here today to present this latest study demonstrating the value of the ABCDEF bundle in improving ICU patient care and outcomes across a large healthcare system. I have no financial disclosures in this instance, and prior consent was obtained for all pictures of VA patients depicted in this presentation. When I began my career as an intensivist over 30 years ago, the standard of ICU care was deep sedation and prolonged mechanical ventilation for our sickest patients until they got better. Over time, we came to understand that deep sedation and immobility comes at a price and actually worsens ICU patient outcomes, resulting in delirium, ICU-acquired weakness, prolonged mechanical ventilation, prolonged mechanical ventilation, iatrogenic complications, longer ICU and hospital lengths of stay, worse long-term outcomes, and death for many patients. In 2013, the Society of Critical Care Medicine published the Pain, Agitation, and Delirium Clinical Practice Guidelines, also known as the PAD guidelines, and launched the ICU Liberation Campaign to help promote these guidelines. In 2018, SCCM published an updated version known as the PADIS guidelines, which emphasized the importance of promoting early mobility and sleep in critical ill patients as well. As part of the ICU Liberation Campaign, the ABCDEF bundle was created to help translate these guideline recommendations into clinical practice. The A-F bundle takes an integrated approach to managing pain, sedation, and delirium in critically ill patients in order to facilitate ventilator weaning in mechanically ventilated patients, early mobilization of all ICU patients, and patient and family engagement. To date, three large studies have demonstrated that bundle performance is consistently associated with significant decreases in the probability of next day deep sedation, delirium, and restraint use in patients, prolonged mechanical ventilation, ICU readmissions, hospital mortality, and SNF discharges in ICU survivors. Bundle performance also has a dose response effect on these clinical outcomes. As bundle performance improves, these outcomes improve as well, with a 60% bundle performance rate being a significant inflection point for these outcome improvements. Other studies have shown that bundle performance is also associated with significant decreases in both ICU and hospital costs. Shea and colleagues demonstrated a 24% decrease in ICU costs and a 30% decrease in hospital costs with only partial bundle implementation. A more recent study by Fish and colleagues showed that bundle implementation in a single 24-bed medical-surgical ICU significantly reduced the duration of mechanical ventilation in ICU and hospital lengths of stay in patients. This saved the hospital over $1.6 million annually and $4.3 million over a five-year period following bundle implementation. In 2013, the Dignity Health System, now part of Common Spirit Health, conducted a multi-center prospective observational pilot study with grant funding from the Moore Foundation. The goal of this study was to implement the ABCDEF bundle over a 12-month period in 11 adult ICUs across six community hospitals in Northern California in mechanically ventilated adult ICU patients. The Moore grant provided partial salary support for one year for a study coordinator, a physical therapist, and a physician champion at each hospital. Dignity Health had previously implemented some elements of the bundle, including pain management protocols, SAT-SBT trials, and routine ICU family meetings. The goal was to ultimately spread the entire A-F bundle across all ICUs within the Dignity Health System. The pilot study included all adult ICU patients admitted for more than one day to these 11 ICUs, although only mechanically ventilated ICU patients were targeted to receive the full bundle. Patients with an ICU length of stay of longer than 30 days, baseline mobility limitations, and patients receiving end-of-life care only were excluded from the study. There were six phases to this study, including a 12-month baseline phase, a six-month bundle implementation phase, a three-month baseline bundle compliance phase, followed by a 12-month bundle performance phase, a healthcare system bundle implementation phase, and a 12-month sustainability and spread phase. Patient demographics and outcomes were assessed only in the six pilot study hospital during the baseline and performance periods in both mechanically ventilated and non-mechanically ventilated patients. Patient outcomes included ICU and hospital length of stay, duration of mechanical ventilation, the percentage of ICU patients with lengths of stay of a week or more, and hospital mortality. Bundle compliance metrics were measured at baseline following implementation and throughout the implementation phase in the same six study hospitals. Then modified bundle compliance metrics were measured during the 12-month sustainability and spread phase across all 34 Dignity Health hospitals. From 2015 to 2018, several changes were made across the 34 hospital healthcare system to facilitate bundle spread and sustainability system-wide. These included full integration of bundle metrics into their Cerner EHR platform, creation of bundle order sets, creation of bundle performance and outcome reports using aggregated EHR data, and making the A through F bundle a healthcare system priority with strengthened leadership support and greater resource allocation towards bundle QI efforts. Of note, these healthcare system changes were all made without any external funding support from the Moore Foundation or another entity. So what did this massive effort to systematically implement the ABCDEF bundle across the entire Dignity Healthcare system achieve? The size and type of the six community hospitals in the pilot study and their corresponding 11 ICUs that participated varied significantly. They included urban, suburban, and rural community hospitals ranging in size from less than 125 beds to over 250 beds. ICUs ranged from 80 to 40 beds and included medical, surgical, trauma, and cardiac ICUs. Nearly 12,000 patients were admitted to the 11 study ICUs during the baseline and performance periods. Eligible patients included over 1,900 mechanically ventilated patients and over 3,000 non-mechanically ventilated patients. There were no significant differences in the demographics of the baseline and performance groups of either cohort. Bundle implementation in mechanically ventilated patients was associated with significant reductions in ICU length of stay, duration of mechanical ventilation, and the percent of patients with an ICU length of stay of a week or more. Average ICU stay decreased by 0.5 days, average duration of mechanical ventilation decreased by 0.6 days, and the percentage of patients with an ICU length of stay of more than seven days decreased by 18%. There were also trends towards reductions in hospital length of stay and in-hospital mortality, but these differences were not statistically significant. By contrast, there were no significant improvements in observed outcomes in non-mechanically ventilated patients. Compliance with individual bundle elements varied during the performance period. Pain management and SAT-SBT trials were high at baseline and did not change over time. Re-intubation rates did not increase with bundle implementation. Sedation assessments significantly increased while the use of benzodiazepine infusions decreased. Delirium assessments increased while delirium prevalence decreased. Routine mobility assessments actually decreased, and the percentage of patients achieving a mobility score of two or more did not significantly change, and family engagement did not change. Three years after the initial study was completed, bundle sustainability in mechanically ventilated ICU patients varied across the six study hospital ICUs. SAT and SBT compliance had decreased by nearly half over time, while compliance with sedation and delirium management and the preferential use of non-benzodiazepine infusions for sedation remained high. Daily assessment and mobilization of mechanically ventilated ICU patients remained low, and routine ICU family meetings actually decreased over time. There was significant bundle spread to other ICUs across the healthcare system, however. Compliance with bundle elements in the ICUs of the 28 non-study hospitals was generally higher than in the study hospitals. A similar pattern was seen in bundle element compliance in these hospitals, with high compliance rates for sedation and delirium management and non-benzodiazepine use, and lower compliance rates for SAT and SBT trials, early mobility, and family engagement. So what did we learn from this study? Similar to the observations of previous large A through F bundle studies, significant improvements in ICU patient outcomes were seen in the study hospitals. Similar improvements were seen in this study with only partial bundle compliance. We observed a strong correlation between improved sedation and delirium management and decreased benzodiazepine use, and an observed decrease in delirium prevalence in this study. The lower compliance rates in this study for SAT and SBT trials, early mobility efforts, and family engagement may reflect the greater need for interprofessional team communication, and care coordination to execute these particular bundle elements. Previous studies have shown an improvement in ICU patient outcomes in both mechanically ventilated and non-mechanically ventilated patients. In this study, non-mechanically ventilated patients were not targeted to receive the full bundle, and no outcome improvements were subsequently observed. Providing real-time system-wide IT support to fully integrate the bundle into the EHR platform, and to provide real-time dashboard-driven data analytics helped to facilitate bundle sustainability and spread across the entire healthcare system. Strong leadership support from bundle implementation by making it a healthcare system priority, and by allocating the necessary resources to bundle implementation efforts helped to accelerate bundle adoption across the entire healthcare system. This is now the third large multicenter study demonstrating significant improvements in ICU patient outcomes following bundle implementation. But unlike previous studies, this study quantified the cumulative impacts of the bundle on ICU patient outcomes, and demonstrated successful spread and sustainability of the bundle across a large healthcare system outside of a one-time quality improvement project. The ICU liberation value proposition is that the ABCDEF bundle improves ICU patient care and outcomes, and reduces healthcare costs by optimizing pain, sedation, and delirium management to facilitate ventilator weaning and early mobilization of ICU patients, and to increase patient and family engagement and care. Even partial bundle performance significantly improves patient outcomes, and reduces healthcare costs. Yet many hospitals and healthcare systems have yet to fully implement the A through F bundle, citing staffing shortages, EHR limitations, and costs. ICU providers and leaders must partner with hospital and healthcare system executives to make the business case for ICU liberation. Thank you for your attention and time. Thank you.

ICU

decision support alerts

drug combinations

alert fatigue

nurse assignments

stress ulcer prophylaxis

congenital heart disease

ABCDEF bundle

mechanical ventilation