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We know that for each additional 10 minutes of early mobility in the ICU, hospital length of stay decreases by 1.2 days. What else do we know about the relationship between dose of mobility and time on the ventilator? Sarina Fazio, PhD, RN, shares with us her exciting discoveries in her latest research. E

Episode Transcription

Kali Dayton 0:00
Sarina, thank you so much for coming on the podcast. I’m thrilled to talk about your publication and can you introduce yourself to us? Yeah,

Sarina Fazio, PhD, RN 0:06
absolutely. Thank you so much for having me, Callie. My name is Serena Fazio, I am a clinical nurse scientist at University of California or UC Davis Health. And I work in the Center for Nursing science, which our mission is to promote the advancement of nursing science and practice. So I am able to conduct my own research, which really focuses on mobility and novel approaches to monitoring documentation and point of care, data consumption and display. But I also get to support clinical nurses and others who are doing research at the institution. So it’s really a fantastic place to be. And other than that, I have been a nurse for four years. And I’m really excited to talk about this study that we put out earlier this month.

Kali Dayton 0:57
And I love seeing nurses, getting into the space taking stewardship of mobility in the ICU and taking a cut of nurses angle into the research. Obviously, we need to be collaborative, but I’m a little biased, I think nurses should take a lot of stewardship in this. So tell me about the study, you just published it in the American Journal of critical care, just this recent month, so tell us how it all went down?

Sarina Fazio, PhD, RN 1:19
Sure. So this study, or the article is called early mobility index and patient outcomes or retrospective study in multiple intensive care units. And I just want to emphasize one thing, before I get into the nitty gritty of how this came about, is that it really is a team approach. And in addition to me, there is a team behind this work that is involved in not only mobility, but the research itself. And in this case, particularly we worked really closely with it. And I’ll tell you why a little bit.

But in terms of the background, in terms of how this study came about, it started a while ago, and this is one of our series of studies that followed some of our initial work. But in around 2018, I was working on another clinical trial that was evaluating an intervention that involved the wearable fitness trackers and nurse health coaching for patients with diabetes. And it was when all these commercial fitness trackers were really coming on the market, and wearables were becoming more common for folks.

And as a member of the team who is delivering the intervention, I really saw how valuable patients found having their physical activity data quantified. And as an ICU nurse, I had a lot of questions and was curious to say, would this technology work in the ICU and recognize that we’d have a gap in terms of how we quantify early mobility intervention specifically. And I worked with a number of folks in my dissertation research focused on validating fitness trackers in the ICU.

And ultimately, we found that there wasn’t great agreement. And it makes sense because we don’t see them everywhere today. But we did find that the EHR electronic health record documentation that nurses and physical therapists were entering into the record was actually quite accurate. And so that led us to really dive into looking at large data sets of EMR data entered by clinicians and trying to quantify mobility.

And so one of the things that we’ve seen is that we know that early mobility interventions in the ICU are safe and improve outcomes. But we know there’s an immense amount of variability in terms of how mobility is quantified, and we haven’t really gotten to that dose or what is the right dose, to yield the optimal patient outcomes.

And so because of that variability in the lack of dose quantification of mobility in many of the existing studies, I think it makes it really hard to translate a lot of the positive findings of mobility into practice and everyday practice. So the point of this study was to really try and close that gap or better understand what is the dose of mobility that is happening, at least in our institution on an everyday basis, not just one point in time,

Kali Dayton 4:33
you involve multiple ICU, so seven ICUs. We looked retrospectively

Sarina Fazio, PhD, RN 4:36
in seven adult ICUs at our institution over three years. So we extracted electronic health record data for all adult ICU patients hospitalized between the end of 2015 and early 2018. And we picked that time period because that was a time period when the EHR was relatively stable. There wasn’t a lot of fields changing at the time. And that teen was when early mobility activities were first added, at least in our institution in a structured way that we could extract.

And so our institution, we’re an academic medical center in Northern California, we have about 650 beds total, but about 100 ICU beds and our adult ICU populations are cardiothoracic, neurosurgical, burn, trauma, surgical medical and to medical, surgical specialty ICU. So really are an array of patients, which I think is one of the things that’s unique to our study.

Kali Dayton 5:43
And what were you measuring?

Sarina Fazio, PhD, RN 5:47
Yeah, so We’ve measured out of bed events that patients in the ICU took. And we picked out of bed events, specifically, because we found that in our previous study, which surprised us were the fitness trackers turned out not to be all that accurate, but the type of out of bed activity events specifically. And the frequency in which they took place were represented quite accurately.

Based on that study, we also found that out of bed events have been frequently cited or clumped together in that way in a lot of the point prevalence literature. So that would allow us to compare our findings to theirs. And while I know there’s a lot of interest right now, and looking at the highest level of mobility that was captured, from a nursing perspective, we really didn’t want on a discount other mobility that was happening throughout the day, that may have not been the highest, but still occurred, none we looked at out of bed events.

But we also normalized that by the number of ICU days. So we call this affectionately the B or the UB index, which is the out of bed events divided by the ICU mobility eligible days. And we did that because we didn’t want there to be bias in someone who had a longer hospitalization, meaning that they may have more mobility. So we tried to normalize it. And I think one of the other reasons we did this is because in the ICU in particular, we look continuously at variables, whether it’s vital sign monitoring, we know our patients are dynamic.

And so mobility is not a checkmark that you did it once and you’re done. It’s really trying to think about mobility in this vital sign paradigm where you’re titrating to a target dose. And so we propose this Ooby index as this more granular quantification. So if someone’s eligible, are they out of bed

Kali Dayton 7:52
and out of bed activities includes the bed, cardiac chair, stretcher, chair, mode, bedside chair, stand at the bedside, marching and play cites this, that being walking, so from an active mobility versus passive mobility, that is almost the whole spectrum, you can hire someone to the chair, or they can be walking down the hall and everything in between.

And so that’s obviously one of the challenges of mobility studies is how we define early mobility. But that’s important to understand what these alphabet activities could have been when we try to understand the impact of them. But again, that’s where there’s a difference in nursing perspective versus therapy perspective, because therapists will look at this and be like, I don’t even want to call hoyering, to the chair out of bed because they’re in the same position in the chair than they are in the bed.

But from the nursing perspective that is technically out of bed that isn’t activities for me that helped understand what those activities were when I tried to discern or understand and apply the outcomes that were measured. Absolutely.

Sarina Fazio, PhD, RN 8:51
I think for nursing, they are a valuable piece in driving mobility. And at our institution, it really is a partnership between physical therapy and nursing. And so while the physical therapist I think, will often try oftentimes really try and individualized therapy to get someone at, you know, the safest and maximal point or highest intensity that they can oftentimes, the nurses will promote moving to a cardiac chair or stretcher chair sitting at the edge of the bed until they feel more comfortable and confident in their mobility practice.

And so we didn’t want to discount that we wanted to include that thing, something else. I really glad you brought this up someone, you know, another reader might say, well, getting up and going to the Como that’s not an early mobility intervention, but they’re getting up to a chair. And so the intent is multifaceted and you’re trying to get a twofer out of maybe having someone who’s more motivated to go to the bathroom in the chair versus the bed. It’s a way to get them up. So We also included that and so we didn’t only say that the intent of the out of bed activity had to be for mobility, we were more inclusive in that regard. And

Kali Dayton 10:11
I think getting to the commode as mobility, I think, and On a sidenote, the team that I’m training, one of the nurses made such a profound a comment or suggestion, because we are trying to make sure that the nurses are doing their own mobility. Beyond that outside of therapy, and so we are setting goals or doing to nursing mobility sessions a day, but the nurse I think mobility comes with a lot of baggage. That word for us as nurses feels like it’s physical or occupational therapists job, what do we call it activity, and nursing activity to activities during the day one evening, at minimum, and I really have been chewing on that ever since because I think I am so engrossed in the academic language, but also I’m very engrained with my therapy colleagues that I think that is something really interesting to maybe call it activity because therapy session could be 30 minutes of walking down the hall and doing sit to stand and things like that, but get into the commode is mobility.

But maybe it’s easier for nurses to understand if it’s a nursing activity, just a thought. So that made me think of that comment. Does this say getting commode is mobility, but do nurses maybe not see it as mobility, but it should still count as a session, but in this study, you didn’t necessarily measure duration. So someone’s sitting in a chair for 40 minutes, maybe it was the same a sudden someone a recliner versus someone sitting in a normal chair holding their own core upright for 40 minutes. That was measured the same walking the hallway was the same event, as in a chair for four hours after right?

Sarina Fazio, PhD, RN 11:46
Yeah, so the World Health Organization describes physical activity measurement and sort of four main ways, type of activity, or mobility, frequency of that activity, intensity, and duration. And in our previous study, where we looked at accuracy of EMR documentation, we found that duration was not possible to reliably obtain potentially from therapy notes, because how they document is different.

And at least in the United States, they document specifically for billing purposes, in a lot of cases, but nurses document predominantly in flow sheets, and either on a time period basis, so every one hour or two hours or depending on what’s going on with the patient, but often by exception. And so duration was not possible to reliably ascertain so we’ve had to give up on it is the wrong term. But put it aside.

But I know that there are studies, especially the randomized control trials that have a lot of personnel and resources to look at the intervention very carefully. But if we’re thinking about mobility in a real life environment, which this is over years, with 1000s of patients, we couldn’t get that. So that’s why we looked at frequency or sort of the number of daily events,

Kali Dayton 13:12
Which is why it’s so helpful to talk to you personally and try to understand why the study was conducted the way it was. I also wanted to ask about how did you discern between PT OT and RN charting? How do you make sure that there wasn’t double charting?

Sarina Fazio, PhD, RN 13:26
Yeah, that’s a great question. So in our analysis, we don’t differentiate between who conducted the activity so out of bed events, when we quantify them in that way, it could have been RN, it could have been RN, PT, it could have been PT, it could have been respiratory physician, we don’t want to emphasize, who’s doing what mobility is really everyone’s job. But I think that is a great question. So we spent a lot of time reviewing our documentation.

And like I said, I can share the the previous study that looked at the specific documentation, and compared it to clinician annotated video to understand the accuracy. But we did go through a validation process, which I believe is described in the supplement.

So before we went live with saying, our algorithm of these EMR records say that a patient got up X number of times per eligible day, in building the algorithm and reviewing documentation, we created these rules with a group of clinicians and our informatics colleagues to say if documentation is charted like this, that means that an IF physical therapy documented it in the tape sign timeframe that nursing did, don’t double count that.

So that process took a very long time because as a clinician, there are lots of ways to document and people do that. So we spent about a year developing that algorithm testing it. And then once we felt like it was solid, and we had specifically looked at edge cases or outliers or things that we thought would be complicated to ascertain or quantified if it was a series of events that happened, or to cardiac chair events, or a series of activities where a patient sat on the edge of the bed and then stood and then marched and then walked, how do you make sure that you capture that accurately?

I think the work and that process that we go through is really unique. And I work with a group at the Data Center of Excellence at UC Davis that really excels in that. But we did do a validation afterwards, where we pulled 100 charts. And we said, okay, algorithm tell us how many mobility events out of bed mobility events happened for this patient in this 24 hour period.

And then we had clinicians independently review those 100 charts for that same time period and say, How many events and then we compare those, and we ran an intraclass correlation coefficient was, which is a measure of reliability between these and it was point eight, which is it’s pretty good and, and we felt confident enough that we could move forward with the algorithm on a wide scale. And it would accurately represent the number of out of bed mobility events,

Kali Dayton 16:37
Which is so helpful. So I as the reader can have confidence that when you say, the single events, they really were single, we’re not getting measuring the impact of two different events that get got counted as one or vice versa. That’s really helpful. And when you say mobility eligible days, let’s talk about the safety criteria that was utilized. I know that in the article, it says it use the hots in 2014, and which I’ll put a citation to all of this. In the transcript on my website.

This is something I utilize when I’m training teams. It’s a beautiful graph, that’s from 2014. So again, it’s 10 years ago, and at the time, it was I know at the time, it was still new. Yet it gives for anyone that hasn’t seen it, it gives all these different kinds of criteria, things that patients might have a suppressor has ventilator settings, blood pressure, heart rate, and it’s beautifully categorized.

And it gives three different columns, or two different columns, three different criteria. So there’s in bed out of bed activity, and it determines for that criteria, if someone’s eligible for that kind of, and better out about activity, utilizing a green circle for no questions asked, go for it. Obviously, yellow triangle means stop and assess the red stop sign means stop and consult with a provider.

That doesn’t even mean absolutely not. That’s the ultimate ceiling. It just means make sure you have the full team collaborating and critically thinking together. So understanding that tool very well, that publication when it said that was the criteria you use, how was that utilized? In your study?

Sarina Fazio, PhD, RN 18:11
Yeah, that’s a great question. And a great point. And I think on an individual basis, and across the seven adult ICUs, determining safety or eligibility for mobilizing out of bed can be individualized and individualized based on that patient population. But remember, we were looking retrospectively and again, this is pre pandemic data.

And as you pointed out, the criteria we used were about now 10 years old, but because we were looking at such a large number of patients, so in our cohort, which we’ll get to was almost 9000 patients in total, we had to apply some standard criteria in terms of understanding who was eligible for mobility on that day or apply that screening criteria. That’s part of any standard mobilization protocol, I would say predominantly, we use a lot of the Aacn standard protocol and screen, but we wanted to be a little bit more in depth because we were working with some of these sub specialty surgical ICU patient populations.

So we applied that criteria. We didn’t do all of them. But again, because we were looking at large amounts of data retrospectively, and we couldn’t manually chart review all 9000 records to understand that nuance in that conversation with the provider. The physical therapists are T and nurse about who can safely get out of bed we had to apply criteria that we could reliably extract from the EMR. So if you see we chose a lot of eligibility criteria that are based on vital signs, labs, things that could be concepts that could be extracted from the EMR and a structured flow sheet.

I think the other thing that you’ll notice is, we didn’t necessarily, and this is where the nuance comes in, is that we didn’t necessarily say, Okay, if your peep was at for a couple hours, you were automatically excluded. Or if you had a heart rate that was elevated, and for a couple minutes, you were off, automatically excluded for the day, we did choose the median value for the day to account for some of those fluctuations, and nuance nuances that occurred,

Kali Dayton 20:48
Which is good to know. Because when I looked at I’m always looking at in mobility criteria, because some of our studies have been done so conservatively. And these guidelines are great that Carol Hodgson published, but sometimes they’re used to create policies, and that are hospitals that become barriers to doing early mobility and optimal mobility because it comes so conservative.

So when I saw in your study that patients were excluded if they had vasopressors running, or if their pupils higher than 12, and FAO to higher than 60%. Of course, instinctively, I’m getting alarmed. But that doesn’t mean that mobility wasn’t done. It just wasn’t. Those mobility sessions were not included in this study, and I can’t appreciate if you say, okay, obviously, you can mobilize vasopressors, but maybe being on three vasopressors maxed out is our threshold, but that’s where the yellow triangle comes in.

And critically thinking, collaborating, discussing, that is hard to be consistent in a large study like this. Yeah, just so readers can understand that just because the things are published in here does that mean that is the mobility criteria that we cannot exceed? It was just what was done for this data collection?

Sarina Fazio, PhD, RN 21:54
Yeah, I think every research, design has limitations in what you can do. And you have to make decisions in order to that are limited. So in our case, it’s retrospective. So it’s in the past, we don’t have contact with patients. So we can’t really understand what happened unless it was documented in the medical record. And it’s all EMR based and with a large cohort, you can’t do that manual chart review to try and understand.

And so we had to make some decisions, you point out and we also mentioned this in our limitations is we were fairly conservative. And part of that is because of the limitation of using EMR data. And so at the time, while we could understand if a patient was on a vasopressor, or not understanding the nuance of the dose that they had that day, similarly, like how I described, looking at the median heart rate for the day was too complicated and labor intensive for anyone knows, the pharmacy, EMR build, I think they’ll understand, I think we’re getting closer to being able to figure that out.

But at the time that this study was conducted, we weren’t able to distinguish your dough. So like you said, it’s not that mobility didn’t happen, we just don’t count that in our UBI calculation of a mobility eligible day.

Kali Dayton 23:26
So when you’re comparing out of mobility events divided by eligible days, but your definition of eligible eligible days is very conservative. I just want to reinforce that these parameters did not mean that those patients were not eligible. But maybe there’s a gap of knowing where they mobilized when they were really eligible per the true hearts and the full Hutson guidelines. Maybe that’s, that’s obvious, I think you could measure?

Sarina Fazio, PhD, RN 23:52
Yeah, you could say that. I think based on our inclusion criteria, we probably are under estimating the prevalence of mobility, but we felt more comfortable going in that direction than over estimating, and being more liberal in our quantification of mobility that was occurring. And

Kali Dayton 24:13
I do appreciate being able to say that I’m working with this with teams that are we identified when they are eligible and are doing it as soon as they’re eligible. So maybe I didn’t fully capture that but I do like that sentiment, rather than treating every patient the same right? If they are on the air obsolete too unstable to mobilize that shouldn’t be discredited to the team that they failed to mobilize them.

But they were screened for it they were deemed ineligible because that criteria but this study what’s mentioned here is not to be taken as the ultimate criteria, which is always what I’m leery of because we skim through studies or articles and we don’t understand the context.

Now that we have the context we understand that almost eight almost 9000 patients were included over three years seven ICUs burn CV neurosurgical surgical med surg times to medical ICU. This is a very diverse population. What were you mainly measuring, as far as outcomes to understand the impact of mobility, so mobility.

Sarina Fazio, PhD, RN 25:06
For outcomes in reviewing the literature, and we focused on three sort of both patient and organizationally important outcomes, which were time on mechanical ventilation, ICU length of stay and hospital length of stay, we are working on some additional outcomes and analyses now. But as I think highlighted by our discussion, there are word limits for these papers. And there’s only so much you can fit in. And so we had to limit what we could share.

But you will see that we have a lot of supplemental sections that have additional tables and more information for readers. So we focused on those three outcomes. And we looked at those three outcomes for the entire patient population. And then we also looked at those three outcome by ICU specifically, because the patients were so different.

Kali Dayton 26:04
And what did you find? This is my favorite part.

Sarina Fazio, PhD, RN 26:07
In total, we included 8609 icu adults in the study across the seven ICUs. I think we’ll talk about the prevalence data maybe a little bit later. But in terms of those three outcomes we talked about if we start with mechanical ventilation duration, for each additional OB or out of bed event performed per mobility eligible day, was associated across the entire cohort with a 10% decrease in the number of hours that patients were mechanically ventilated,

Kali Dayton 26:46
which is, I think, very profound. Yeah. And

Sarina Fazio, PhD, RN 26:50
We looked at percentage, and that we worked really closely with a biostatistician to work with us on the statistics for this. And so when we say 10%, it’s individualized to the person which I think helps for interpretation, right, because some people have shorter durations of mechanical ventilation and longer. And so that equals that a little bit.

But I think one of the really interesting points, or sub points in this outcome was that when we looked individually, at each ICU, we found that the biggest effect was in the medical ICU patient population, where for each additional OB, an index, we found a 22% reduction in time on mechanical ventilation, which is, I think, pretty remarkable and speaks to the power of the intervention, even given the limitations that we looked at all out of bed events clustered together.

Kali Dayton 27:49
But don’t think I can’t think of a lot of other studies that have captured this distinction of impact between specialties or when I’m looking through teams data, and we’re looking at time to event time that I see you. I always requested, they sift them out the separate CV for medical because they’re such different patients again, and CB you’re looking at hours on the ventilator, versus days or weeks on the ventilator and a medical ICU just because of their diagnosis, their pathology, their morbidities.

It’s just a different animal you’re dealing with. So I thought that was really powerful to show the difference, each unit of mobility, out of bed mobility made it an impact by 10%, at least. But the medical ICU it’s 20 to 2% Decrease in time the ventilator is really significant.

Sarina Fazio, PhD, RN 28:34
One other thing that I’ll say about that is that in our cohort, because this was a real world population, and we included patients overall in the study who unfortunately passed away while they were in the hospital, we looked to see if the association and the results held for survivors only. And when we looked at the medical ICU population, the results held so we still were able to confer that 20 to 20. About 22% reduction in time on mechanical ventilation. Yeah,

Kali Dayton 29:07
That is interesting because time on mechanical, mechanical ventilation in one life ends. So that’s a really important distinction. What about time in the ICU? I thought this was really interesting as well.

Sarina Fazio, PhD, RN 29:18
Yeah. So this was really interesting. We actually found that for each additional UBI that was performed length of stay increased by 4%. And I will say we were really interested in this because there have been a lot of studies that have shown a decrease in ICU length of stay, not all of them.

But when we looked individually at the ICUs we found that not all of the ICU individually had this association, but it was significant for an increase in ICU length of stay related to patients in the burn ICU cardio three LASIK and neurosurgical. And so when we think about and we went back to try and figure out why this difference was, I think there are probably some organizational factors or individual factors that have to do with it, I think also the patient population, specifically.

So if you think about maybe burn ICU patients, for example, they may have really intensive wound care needs that may not be able to be met in acute care and things like that, where a patient may not be critically ill anymore and need to be in the ICU for their critical illness. I think in the burn CT ICU and neurosurgical ICU, there could be probably more frequently reasons why they need to stay in the ICU for intensive monitoring because of a device or something else.

Kali Dayton 30:56
And here’s something that I’ve seen, but in practice, in my personal practice my decision making, and I’m seeing in teams now that I’m training, but I don’t see it in the literature, when you have an ICU that’s really progressive mobility, you’re working with patients, you’re very invested in their functional status, and you know that your acute care floors are not going to have the same culture and focus, it is really hard to send these patients that you’ve invested so much and too often to the floor.

So I’ve absolutely seen my own decision making be impacted by Oh, they’re getting so much mobility here, they’re progressing, they’re doing so well, if he’s going to the floor, especially thinking might some of my frail geriatric patients, or I don’t mean to harp on our bone marrow transplant unit, where it came from, they came to us very debilitated, very fluid, overloaded very delirious. And now with this key was probably failures, what we’ve now rehabilitated them for weeks, and now they’re walking again. Now they’re functional. And so it’s hard to when they’re still frail, they’re still so dependent on that hands on very aggressive approach to send them to the floor.

So it becomes, well, we can probably find excuses to keep them they longer because we know they’ll benefit. Did they meet ISO criteria? Not always. But we sometimes make those decisions not based on ISO criteria, but instead, how much mobility will they get on the floor? And if they don’t keep this level of mobility up? Will they now have problems airway clearance? In a few days? And will it come back to the ICU? So I don’t know that we’ve really captured that. But I wonder if that’s part of it when you’re actually mobilizing patients, and you’re invested. But the acute care floors are not, does that maybe influence us?

Sarina Fazio, PhD, RN 32:35
And I don’t think that the acute care floors are not, I think that they have different patient to nurse ratios. And so you may not be able to give them as close attention and prioritize that or have the same resources. So absolutely. I am not going to argue with you. I think that could have played a part, especially in our cardiothoracic ICU.

If you look in our paper to we look and compare the ICUs in terms of the highest prevalence of obese and they have the highest they have a really strong culture of mobility, and really invested in that. And so like you said, I think there are a lot of service line decisions and organizational factors that could affect that there’s a lot of institutional priority on throughput. Depending on cases, we saw that in COVID.

Granted, this is earlier than that. So I think that ICU length of stay can be really individualized. And also back to the ratios, I just want to point out we’re practicing in California, that’s one of the few states that has state regulated nurse to patient ratios. And so in the ICU, the maximum number of patients that a nurse will have is two and so you do have more time and to devote to each patient.

And so we’re really lucky in that regard. But I think all of that supports your hypothesis in terms of potentially keeping patients in the ICU who are really benefiting from mobility so they can keep getting that same level and resources. So I think that’s really astute. I will also say that we looked at this in another way, because we know especially from prior studies, that timing of mobility is potentially really important.

And so in both length of stay outcomes, we looked at the timing, and we specifically looked at from the point of ICU admission to last day like the day of So essentially when they were intubated. And when we looked at the OBS prior to extubation, or up until the day of in the same cohort, we did find that ICU length of stay decreased by eight in the total cohort.

So it does seem like there’s some signal there in that when someone is mobilized matters, and that it should start when they’re intubated, which I think aligns with the previous results about mechanical ventilation. But also is interesting in these length of stay data too. And that in that third relationship did, and then was protective for ICU length of stay, and instead of elongating it, yeah.

Kali Dayton 35:28
And when I think it’s pretty habilitation versus rehabilitation, and when you wait too long, and now you’re working on rehabilitation, and then that can maybe that’s part of the prolonging it. But if it’s a pretty you’re now preventing harm, which then expedites their transfer from the ICU. And we saw it similar in the team trial, that perhaps that with timing of the mobility was part of the lack of finding of increased the 12 minute increase in mobility.

So planting seeds for the researchers out there, let’s look more into the timing of this as well. And so it’s when you have such conservative parameters on what you’re measuring in your data, it’s hard to know what the timing may have really been. That’s obviously gaps. And all we can do is speculate.

Sarina Fazio, PhD, RN 36:11
And then just to round it out, the final outcome that we looked at was hospital length of stay. And in this case, we also found that each additional Ooby was associate with a shorter hospital length of stay of about 5% In total, and the effect was significant in four of the ICUs. And then we looked at mobility in the intubated patients while they were intubated, we found that association was even larger, and that hospital length of stay decreased by 11%.

So again, there’s something there with timing, we looked at it a little bit. But there’s, I think there’s more. There’s more there. And I think all of those things really get at trying to figure out that dose, when how much which patients. And I think that was one of the main goals of our paper was to better understand that dose response. And I think we answered some questions. But I think there’s more work to do here and to understand some of the nuances there.

Kali Dayton 37:19
Yeah, this really helps bring in more data into the conversation of what is an optimal dose a teen trial, lead people to kind of feel discouraged and feel like they’re six days of harassing and get a three and then moving them. X amount of time is the optimal because anything beyond that didn’t make a difference. But now you have more data saying, Well, no, the more they were out of bed, we did see an impact.

Now, one of my questions would be maybe for future studies is if we were to consider the timing, would we still have this? For every event, there’s a 10% increase or decrease in time of the ventilator? Because we’re looking at an Awake and Walking approach where they’re up and walking, even on day zero, like shortly after intubation. Do we still have that much of an impact for every event? That’s big?

Sarina Fazio, PhD, RN 38:06
Yeah, not sure. I think that’s a great question. I think with how complex and nuanced our electronic medical records have become, and the amount of information that’s in there, we can begin to answer these questions, especially as we develop these measures of mode that are continuous over time and can be measured on a day to day basis, not just a one off event. Overall.

Again, because our cohort was so large of around 8000 patients, the median ICU length of stay was 2.7 days for all patients, which is relatively short, a lot of patients are

Kali Dayton 38:49
Really short, which was really raised my eyebrows. Yet I had to understand too, that this is accumulation, including cardiovascular hours in the ICU, really impacting what we’re measuring from medical ICU burn ICU worth, those are significantly longer lengths to say,

Sarina Fazio, PhD, RN 39:05
yep, yep. And we did use median, because oftentimes this data is so right, skewed, you have these fewer patients, but they have really long length of stay. And so that sort of moves the needle. So this is median. When we look at intubated patients only or patients on mechanical ventilation, the median length of stay was 4.5 days, so a bit longer than that.

And we didn’t look at necessarily time from intubation to first out of bed event, but we looked at time from ICU admission to first out of bed event. And it was point six, like a median of point six days, so half a day, but that’s comparing the median to the median, which is short.

Kali Dayton 39:47
And if you were to take the cvicu out of that might be what would that data look like? I just feel like it’s easier to get a higher volume when you have a lot of post cabbage patients that take your elements later, but that they’re on there for what two, four hours hours, and you’re cranking through a lot more volume, versus patients that do stay for ARDS patients, right?

It’s just a completely different demographic. So that’s where I think measuring each individual patient population, and you found a big difference and their outcomes. And so as we look at eligibility studies in general, this is why it’s really important to read the the data, and you provide a lot of explanation. But when we skim through, we may not really get the full picture if we’re not using an AI discernment.

Sarina Fazio, PhD, RN 40:31
Yep. And in our supplemental table three, we break down the patient characteristics, mobility and outcomes by the specific ICU might be more nuanced than we have time to get in here. But if you look at the differences across the ICUs, we do see that the burn ICU is different, it has a larger median. And that’s because of the nature of that patient population.

And so I think one thing that we really want to highlight is one of our takeaways is that it is really important to look at these patients, these patients by population, because there are nuances and differences, even when you’re looking at sort of large, these large datasets as it is important to drill down to the patient population and the culture of the unit and differences in practice. And so that was one of the sort of unique findings that we had. And we were able to do that we hope others will take with them. Obviously,

Kali Dayton 41:35
We’ve gotten into the deep, nitty gritties. And a lot of this but even zooming out understanding the complexities involved in this study. Nonetheless, across such a heterogenous population, we found a dose dependent response between out of bed mobility, which again, maybe these were patients that had been sedated their RAS negative to slung to the chair, if that makes an impact by 10% conservatively on time of a ventilator, that should be enough to get us to do at least that, but then to see if we are earlier, if we’re more aggressive, we increase the dose, this provides confidence that we will increase the benefit to our patients. Yeah,

Sarina Fazio, PhD, RN 42:14
Yeah, and a future state and thinking about next steps for the UBI. I think because that intensity, and the ICU mobility scale, people have really in that critical care community have coalesced around it in terms of a measurement strategy. I think one of the things we’re going to be thinking about in the future is how do we incorporate the UBI?

Or how do we incorporate the intensity of types of mobility activities with the UI? Because I think the UI does really add to the conversation to allow for the over time monitoring, like we do everything else, and really saying, Okay, if the patient is eligible, let’s get them up. And then utilize that on that.

Kali Dayton 43:00
Technology has evolves, or our informatics has evolved beyond 2015, so that we can better measure things like bass suppressor dosing, ventilator settings, to allow for a less conservative eligibility criteria to then be able to say, are we really doing early prompt mobility as soon as patients really are eligible? Is this evidence based or cultural based?

What are we actually doing? And with future studies, many think about this technology that I’ve seen at conferences, where they’re the SED devices that track patient mobility, how long they’re in the bed, if they’re walking, how far they’re walking, and how long they’re standing, how long they’re sitting outside of the bed.

When I first thought two years ago, it couldn’t be integrated into the EMR. Now, what can what are your thoughts on that? I know, maybe you’re not the expert on it. But this seems to be in your wheelhouse. How do you think that could be utilized?

Sarina Fazio, PhD, RN 43:53
I would say to refer to our previous paper, and I will send you and make available in the show notes. The citation for that granted, that study was performed in about 2018. So we know that the technologies evolved, I think there still is a potential. But I think that technology that’s made for if we remember back to the beginning of the conversation, like the wearables that are made for patients in the community, can’t be applied directly to patients in the ICU.

And I think we’re finding that more and more with technology, some of these big algorithms or PPAs, that are coming out to be deployed, applied to big hospitals, is that you have to tune the algorithms to that unique patient population, because there is noise or other things that could be causing a signal or an alert or saying that a patient’s ambulating but we know they aren’t so until that happens, I don’t think we’re there yet.

And as clinicians, when we have technology, if you see something that’s not working, you just start to dismiss it and not have any faith in it. And then the technology is useless. So I think there’s a future state for it. But I think that there needs to be more nuanced in the application and tuning to the individual patient population. Yeah. And that is, if that company

Kali Dayton 45:24
If you are listening, I won’t name the company. But this tool is, if you want it done for research, because they’re always data tracking is a big part of the sale, engagement, but it needs to be validated, making sure that absolutely proven to be accurate, so that we can then utilize it for research moving forward.

Sarina Fazio, PhD, RN 45:41
and validated in that in the space in that in which you’re going to be implementing it? I think the other limit with devices potentially is scalability, right. And in terms of in a research study, it may be a smaller population, but one of the benefits of the approach of really trying to leverage the information that we spend so much time in entering information to be able to leverage and utilize that for future research as technology.

Kali Dayton 46:14
If they’re already wearing STDs. And these are, the technology admittedly is much easier. And STDs, they’re battery powered, you don’t have to plug them into a device, like from a nurse perspective, I would rather use that than have to unplug, plug them in deal with all the chords and a whole pump. But aside from that, if that tracks their mobility accurately.

Now, we don’t have to worry about how many feet do they walk? And how long were they in the chair? And how long we could be more liable? How could that impact our research moving forward? So just things to chew on? .

Sarina Fazio, PhD, RN 46:47
Absolutely. And I think where we are in sort of the technology landscape, there’s a potential for clinician documentation to really change and the next five years, and I don’t know where it’s going. But I think it’s an interesting time. And we know that the only constant in healthcare is that it’s always changing. And so we have gotten used to that

Kali Dayton 47:09
Medicine is a field of evolution. Right? Yeah, medicine is constantly evolving, or it should be. So I appreciate the study has given us another piece of the puzzle, to then spur on future studies. What would you what’s your next endeavor? For research? Where do you go from here?

Sarina Fazio, PhD, RN 47:26
Oh, gosh, there are so many things. So like I said before, we’re looking at some other outcomes with the UBI in terms of discharge disposition, bouncebacks, other patient and organizationally important outcomes. We just presented a poster on looking at mobility trends before, during and after the pandemic, which showed that there was a dip during the pandemic, but we have started to recover. But I will say, and I think it’s evident in our paper, we do have a ways to go and there’s so much opportunity for increasing the amount of mobility that we have, or and that we’re doing locally.

Kali Dayton 48:05
Yes. And what percentage of your intubated patients were actually mobilized out of bed?

Sarina Fazio, PhD, RN 48:11
Yeah, so we had in total in the total cohort, about 47%, of ICU days, patients got out of bed, and in general, and for the patients on mechanical ventilation, only 17%, which is quite low, and we want it to be higher. I will say though, if you look at point prevalence studies in the US, it does align with what’s been reported in the majority of centers. So I will say we’re in line with that.

But we’re not satisfied with that we see the benefit in that’s been evident in the literature elsewhere. And the results of our study really support that. And so one of the things that we’ve been doing as a result of this work is revamping our mobility program locally and post COVID. In order to take our findings and implement them.

We are looking at the screening criteria, we are not using such a conservative screening criteria and really having a large bucket of things that are precautions which really mean having a conversation with the interdisciplinary team about safety and how you can promote safety, with the risks that are in place. And so we are trying to streamline our EMR, so less documentation but more efficient documentation.

And then having those criteria available for people really readily so they can go to it become more confident in who they can mobilize who they need to have a conversation with, or who they really need to say okay, let’s wait until things have changed or tomorrow. row or something is different. And then I will say the the last piece is because of half of the UBI, we’re going to be leveraging OB for sort of a real time dashboard, that units can look at individually and saying, Where is our UBI today? Or this week or last week? And how can we increase it? Are we getting patients up when they’re eligible? Or are we not? And so we’re really excited about that to be able to translate the work we’re doing into real life action and quality improvement work.

Kali Dayton 50:38
Which thrills me because honestly, that’s a big gap that I see with teams that I’m working with. We’re doing a lot of manual auditing, putting a lot of pressure on the charge nurses to go room to room to room and having those discussions can immobilize what level of mobility redoing, why aren’t they up to meet these goals? But I just keep thinking, There’s got to be an easier way and look at all the it manpower that we have, why are we not doing something in real time that actually changes our practice and streamlines it? So keep me posted on that, because that’s something I would love to help rollout and other facilities even? I’ve got a whole list in mind.

Sarina Fazio, PhD, RN 51:15
Yeah, I see clinicians were so data driven. And we haven’t had those metrics, aside from research to say, Okay, we don’t think we’re doing that well, or we think we’re doing well in mobility. But we don’t have the data to say, but actually, how much are we doing every day per patient. And it’s taken a lot of time to get there. But I think we’re really close to being able to instantiate that here. And I hope that we can share that work with others who also are struggling with this or have been doing mobility, but want to increase the amount and really make the programs more robust or revitalize them following COVID.

Kali Dayton 51:58
I see that similar to the sepsis bundle. If you remember where that was rolled out, you get these page papers on your door, as soon as someone came in with sepsis and you the clock is ticking. And you were to give antibiotics and time the fluid and time you have very time timely interventions that are to be met within that time line. And it’s measured. This auditing is tracking this I did we give these interventions, life saving interventions on time, at the right dose and duration.

But we don’t do that with mobility, we get this very Oh, it’s proving patients are out of bed more, and we don’t have a way to really measure it in the same way. So thank you for the work that you’re doing. I think what you’ve shared about your systems journey is very relatable, where there’s been a dip and now we’re recovering.

Sarina Fazio, PhD, RN 52:42
But then looking at what are we recovering to? was what we were doing pre pandemic, the level that we’re aspiring to, or did we see like in your data that maybe some 10% is not the level that we’re okay with? And so what do we do to learn from where we were at and how do we move forward like you’re doing better equipped to really achieve level of an awakened walk in ICU in which every patient possible is doing their highest level mobility as soon as they are eligible?.

Yeah, absolutely. That is the goal.

Kali Dayton 53:11
Love it. Thank you so much for everything that you’re providing to the ICU community and keep us posted on your future accomplishments.

Sarina Fazio, PhD, RN 53:20
Thank you so much for having me. It’s been a pleasure.

Transcribed by https://otter.ai

 

Resources

Every unit of out of bed mobility decreases time on the ventilator by 10%:
Fazio, S. A., Cortés-Puch, I., Stocking, J. C., Doroy, A. L., Black, H., Liu, A., Taylor, S. L., & Adams, J. Y. (2024). Early Mobility Index and Patient Outcomes: A Retrospective Study in Multiple Intensive Care Units. American journal of critical care : an official publication, American Association of Critical-Care Nurses, 33(3), 171–179. https://doi.org/10.4037/ajcc2024747

Every additional 10 minutes of early mobility in the ICU decreases length of stay by 1.2 days:
Jenkins, A. S., Isha, S., Hanson, A. J., Kunze, K. L., Johnson, P. W., Sura, L., Cornelius, P. J., Hightower, J., Heise, K. J., Davis, O., Satashia, P. H., Hasan, M. M., Esterov, D., Worsowicz, G. M., & Sanghavi, D. K. (2024). Rehabilitation in the intensive care unit: How amount of physical and occupational therapy affects patients’ function and hospital length of stay. PM & R : the journal of injury, function, and rehabilitation, 16(3), 219–225. https://doi.org/10.1002/pmrj.13116

Video-accelerometer :
 Fazio S, Doroy A, Da Marto N, Taylor S, Anderson N, Young HM, Adams JY. Quantifying Mobility in the ICU: Comparison of Electronic Health Record Documentation and Accelerometer-Based Sensors to Clinician-Annotated Video. Crit Care Explor. 2020 Apr 29;2(4):e0091. doi: 10.1097/CCE.0000000000000091. PMID: 32426733; PMCID: PMC7188433.

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