In this episode, Tom Varghese, MD, MS, MBA, FACS, is joined by Anai N Kothari MD, FACS, MS, FSSO, from the Medical College of Wisconsin. They discuss Dr Kothari’s recent article, “Association of Daily Step Count and Postoperative Complication among All of Us Research Participants,” in which the authors used preoperative wearable device data from 475 patients to investigate the link between daily step counts and postoperative complications. Patients with fewer than 7,500 daily steps had a higher risk of complications, supporting the use of wearables for surgical risk assessment and preoperative fitness measurement.
Disclosure Information: Drs Varghese and Kothari have nothing to disclose.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:03):
You are listening to The Operative Word,
a podcast brought to you by the Journalof the American College of Surgeons.
I'm Dr Tom Varghese,and throughout the series, Dr Lillian
Erdahl and I will speak with recently
published authors about the motivationbehind their latest research
and the clinical implicationsit has for the practicing surgeon.
The opinions expressed in this podcastare those of the participants,
(00:26):
and not necessarilythat of the American College of Surgeons.
Hello loyal listeners.
Welcome to another episode
of The Operative Word, the podcastfor the Journal of the American College
of Surgeons,where we connect with amazing authors
who recently published incredible articlesin JACS.
(00:47):
I'm honored to be joined todayby Dr Anai Kothari.
Dr Kothari, welcome to the podcast.
Thanks so much.
So excited to be here.
And, excited to be here with you.
You probably don't remember.
Or maybe you do.
Actually, the first article I everpublished; you were the discussant on.
I was the discussant on, that is correct!
At the American College of Surgeons,
(01:08):
meeting. And so, it's just kind of fun
to get a chanceto talk to you about this article.
And now that's great.
And so for the listeners,Dr Kothari is an assistant professor of,
in the Division of Surgical Oncology,Department of Surgery,
at Medical College of Wisconsin.
He's also a member of the Collaborativefor Health Care Delivery Science
at, MCoW as well.
(01:30):
Dr Kothari, let's go,go ahead and get started.
Today we are going to be discussingyour incredible article called “The
Association of Daily Step Countand Postoperative Complication
among All of Us Research Participants.”
This is, published ahead of,
print, right now in JACS and was,
(01:50):
the abstract was formerly presentedat the American College of Surgeons
109th Annual Clinical CongressScientific Forum
that was held in Boston,Massachusetts, in October of 2023.
So Anai, let's go ahead and get started.
What inspired you to pursue this projectin the first place?
It's interesting that there'sactually a very good story around that.
(02:10):
So I was sitting in clinic,I do GI surgical oncology, and
I was preparing to do a gastrectomyfor a patient, and they were just
so excited to show me the informationfrom their Fitbit,
basically telling me how many steps theyhad been taking leading up to surgery.
And we have this unique opportunity,in surgical oncology to do some work
(02:31):
in prehabilitation as they lead upto surgery to a neoadjuvant therapy.
And, we always tell everyone kind ofdoes this generic message,
you make sure you're active,that you're, remaining physically fit.
And, and this was actually objectiveinformation that they were showing me.
And, that kind of setting,this serendipitously corresponded with,
(02:53):
one of the medical students who had cometo me, looking to do a project.
Carson Gehl, who's the first author,
amazing,human being and, going into surgery.
And so, someone that people look out for.
But, he found this data source,the ‘All of Us.’ Researcher Workbench.
And we found that,this is a voluntary program.
(03:13):
They're trying to recruita million participants.
And the data, through appropriate,accesses, can be used by anyone.
And one of the domainsthat's collected is, wearable device data.
So it's linked to the electronic healthrecord.
So here we had this opportunity to takea really relevant clinical situation.
And here it was, a data set.
(03:35):
Then it would let us actually interrogatethat question.
So that was kind of the inspirationand, background for why we,
began to think about,how do we understand the association
between physical activityobjectively and postoperative outcomes?
Well, that's amazing how,
serendipityled to this incredible project.
(03:56):
But I wanted to focus a little biton the All of Us research program.
A little bit further.
So, as you correctly pointed out, it'san NIH funded effort to collect
electronic medical record,crosslinking with health questionnaires,
looking at some genetic dataas well as digital health technology.
How is the sampling?
I mean,I know that there's a million patients,
(04:16):
but is this like, you know,is it a strategic sampling?
Is it a convenience sampling?
How would you reflectback on the sampling, or how the data was,
acquired in the first place?
Yeah, the All of Us program,
it has set this goaland set a million participants.
And they've set upbasically regional centers to try to,
(04:37):
ensure that the final groupis representative or at least
more representative of the populationthan maybe a typical cohort study.
It kind of falls similar to other programsin other countries, like the UK
Biobank, where,
you know, that data set very,
you know, useful and has providedamazing insight on the population
from the UK, but has been limitedin some ways by kind of the distribution
(05:00):
of the population being slightlyolder and, mostly Caucasian,
so that the goal that All of Us researcha program is to try to be more diverse.
It is a convenience sample.
Ultimately,the participants have to volunteer
to be part of the program, and,I'm part of the All of Us program myself.
So basically, you, enroll,
(05:21):
you get permissionto link to your electronic health record,
you provide some blood samplesthat then get sequenced,
you get some genetic testing,you get all that information back,
and then you have the option of also,
if you have a Fitbit or a wearable deviceto link that
into the information, you completesome surveys as part of the intake.
So, it's really participant led,at the end of the day though,
(05:43):
when you talked about generalizabilityfor a study like this, you know,
it falls more until like conveniencesample, framework.
But let's, let's go to the,the actual study itself.
So the primary outcome of the studywas the development of any adverse
event within 90 days of surgery,which I applaud you for doing that.
(06:03):
You went beyond the usual 30 daysand really focusing on the 90 days.
And of course,
you looked at adverse eventsincluding pneumonia, respiratory failure,
pulmonary embolism, sepsis, cardiac
arrhythmia, renal failure,urinary tract infection and DVTs.
And obviously,
you were also trying to do a sensitivityanalysis, trying to figure out
not only the peoplewho were using the Fitbit app or the
(06:25):
the physical activity trackers,but trying to really get down to the -
Was there a cutoff or thresholdabout the number of steps?
Can you explain how you did thatsensitivity analysis or what what
led to down to the path where ultimatelydid figure out the cutoff was 7500 steps?
We started with
just having kind of a general ideaof where that number may fall, or
(06:47):
there's a couple of large studies donein cardiovascular outcomes where that 7000
to 9000 step threshold seems to be wherethere's an impact on health outcomes.
So that was kind of where we thoughtwe may land.
And then what we did is,is just that every 500 steps, looked at
how that essentially was associatedwith our primary outcome.
(07:08):
So there's a figure in the paperthere, just looks at this,
you know, at each thresholdabove and below,
you know, what does the adverse event ratelook like. And,
no, I love artificial intelligence,data science, machine learning,
some of those things.
But I'll say that at theat the end of this, it really was,
you know, kind of visually seeing,you know, where this threshold
(07:29):
looks like it actually was.
And so, that that methodologicallyis how we came near that threshold.
Absolutely amazing.
And so when you, when you went aboutdoing this, and reading this directly
from your paper, it's off, you know, 475participants met your inclusion criteria.
And of the included 475 patients,
(07:51):
60 patients, or roughly 12.6% experienced
an adverse event within 90 daysfollowing their procedure.
Was that surprising to youto find that 12% of
patients had an adverseevent of some type?
Yeah, probably slightlyhigher than you'd expect.
You know, given the cohortand the types of procedures
that were included, we use the NSQIP
(08:14):
inclusion criteria, the CPT, to basically,
kind of identify which proceduresto apply to our population.
I think it speaks to,
what actually happens to individualsin the real world, potentially.
So, you know, so much of the informationthat we have on postoperative
risk really centers aroundwell collected data sets.
(08:34):
What all of us offers now, is this, right,multicenter EHR, information.
So not just what happened to them,but the place where they had surgery,
but potentially other thingsthat occurred afterwards at other sites.
So, we think that may have ledto kind of a slightly higher incidence
incidence of perioperative outcomesand maybe more reflective
of what really happens to individualsonce they recover from surgery.
(08:57):
And what I loved was,you did your initial analysis,
you figured outit was about the 7500 step cutoff.
But I also really thought it was brilliantthat, after you matched active
to inactive participants, you really dida propensity score matching.
You had a 191 matched pairs.
(09:18):
Talk to us about that process.
I mean, I, you know,
we have some buzzwords out there,you know,
propensity matching and things like that.
But talk to us about the rationale for,you you had the cutoffs.
You already had kind of the outcome,of interest defined,
but then you went furtherto do the propensity matching.
So I think there's so many waysto try to adjust
(09:39):
for or think about confounding in studies,you know, regression being one which
you know, we we approached this problemwith multivariable regression as well.
And just to really try to center
as much as we could, to addressthe confounding,
just did additional analysiswith propensity score matching as well.
Now, it's not perfect, and there's only somuch that you can ultimately control for
(10:01):
and likely more confounders out therethat would have, modified the association.
But I think that those stepsare important.
And, and this is always whereyou gotta give some credit to peer review
just because, yeah, it's a villagethat makes these studies work.
And, you gotta give creditto the one of the reviewers who said,
you know, it would be really usefulto get this initial analysis.
(10:22):
So, shout out to that process as well.
It's not all stuff that we concoct.
You know, this is probably the first,this is probably the first interview
in the history of mankind where we’regiving a shout out to reviewer 2.
That's good. That's great. But
that's fantastic.
But, no, I, I, you know, again,
I geeked out on a coupleof different points
that that was one of the thingsI was like, oh, that was perfect.
(10:44):
You know, you just address it right on.
Because again,we're going to get to this as well.
You know,
obviously the limitations of the study,are several and confounding is there.
But but the fact that you went anddid that extra step, it kudos to you guys.
Well, well, let's get right down to the,the take home message is so,
you know, I got reading from the paper,so in this retrospective observed
observational cohortstudy of the participants
(11:07):
that participated in the All of Usresearch program,
we found that preoperative dailystep counts
taken from an EHR-linked wearable
device data were an independent predictor
for the development of, adverse,post-operative outcomes.
And, you know, as youhighlight in the article, which again,
(11:28):
but encourage readers to go, listenersto go and read,
you know,there's some great resources there.
You know, consistent with the literaturekind of where, where
you thought it was going to be happening.
And then you also went on that,the wearable device, measured step counts,
can be
envisioned as a piece of datawith which surgeons,
(11:49):
can consider patient selectionas well as perioperative planning.
In additionto the traditional measures of risk.
I wanted to start thereand ask you a few questions.
It's it seems to be intuitive, right?
The more active a person is,the better that they should do.
Yet, you know, from personal experience,from all of our connections,
(12:12):
this has been so hardto tackle in the world of surgery.
What are your thoughts?I mean, you have these findings.
So what are your thoughts now of wheredo we go next with this piece of data?
It's amazingbecause I think about conversations
you and I have had in the past.
Obviously, you being,
Strong for Surgery, the things that,you’ve thought about a lot,
(12:33):
and have really championed.
But it's been challengingto operationalize that at scale,
I think somewhatbecause measurement is so hard
where it's of entering into this erathat where that measurement
piece is happeningwithout us doing anything.
You know, people are more apt to wearwearable, either on the wrist
or even just on their phone, you know,tracking the steps that they're taking.
(12:56):
So it's this piece of information and datathat I know used to be,
I think, very difficult for usto obtain, with some fidelity.
And now, patients are coming to us
with information about this already.
So, I just think the operationalizationof this information
and actually incorporating itinto a clinical setting has that that,
(13:19):
challenge has
become much less,which makes me optimistic
about using this, you know, itkind of, even beyond step count.
There's other things that these devicesare capturing and calculating
as well, things like sleep and heart rate.
So even more opportunityto to really rigorously understand
what happens before surgeryand that potentially improve postoperative
(13:41):
outcomes.
I guess one of the cynical views outthere has been,
everybody talks about value based care,
but we're still in a fee for, servicebased system.
And, you know,
the cynical views that I knowand you and others have kind of faced was.
Well, Tom, nobody'spaying for that. Right?
(14:01):
I it's like nobody pays for investmentin patients’ health
before we do electivesurgery.
They pay for the surgery, but,
but they don't payfor all the things ahead of time.
But I think that what you're highlightingis that patients are already doing this.
I mean, that's the reality
whether we want to acknowledge or not,patients are already engaged in the space.
(14:22):
It's really more a questionof trying to figure out why patients
or some patients are not just using,
activity trackers, but are really engagedwith this vs others who are not.
Is that a correct way of framing,the question of where we need to go next?
Yeah, I think that's totally right on.
And maybe one additional pieces is now
that patients are doing this on their own,what do we do with that information?
(14:45):
You know,how do we optimally, take that data
and apply it and, and on right now,you know what this study and only show is
okay, there is this association betweenstep count post-operative outcomes.
And, we try to highlight that.
Yeah, we use for risk stratification.
Another piece of informationto go into patient family discussions.
(15:08):
Surgical risk. Prognostication.
But it opens the door
for really trying to studycausality studies.
Where are yousee what happens as activity levels go up.
So, yeah, I think you're right on.
I guess I, let me ask you this,trying to put you on the spot.
You and I are both cancer surgeons, and,the cancer space now is really,
(15:31):
really embracing this concept.
And so, I'll just read youlike the NIH formal definition right now
where specifically in cancerprehabilitation.
So their formal definition is “it'sthe process in the cancer
continuum of carethat occurs between the time of cancer
diagnosisand the beginning of acute treatment.
And includesall physical and psychological assessments
(15:54):
that establish a baselinefunctional level, identify impairments
and provide interventions that promotephysical and psychological health
to reduce the incidenceand or severity of future impairments.”
I mean, that's kind of whatthe NIH definition starts with,
I'm trying to imagine the future.
I'm going to ask you toimagine the future.
I mean, you're oneof our really innovative thought leaders.
(16:17):
And I think in the House of Surgery,not just because you've embraced
artificial intelligence and you're doingall this cutting edge work, but, you know,
it's kind of like, I want to ask youand I what your futurist view is.
Like, imagine five, ten years from now,what do you think is going to happen?
Like your cancer patient comes to eitheryour or my clinic.
What do you think is where we're goingand what are the steps we need to get to,
(16:40):
hopefully that future?
That is an easy question.
Easy question. Of course.
But, I think
there's two fundamental shiftsthat are happening right now.
One is digitally enabled care.
So, you know,we have always viewed health care,
I think, within the walls of,the clinic in the hospital.
(17:01):
But the reality is most of healthcare is delivered beyond that.
And how do you ensure thatwhat's happening outside those walls,
in and inside are,
you know, together and unified?
And to me, that'sreally focusing on a digital strategy,
wearable devices being one part of that.
But, you know, ensuring that,
(17:22):
you know, health informationthat's collected in
one settingis easily exchanged across the other.
So I think that that's one part of itis, this, this concept of a really
the digital ecosystemaround patient recovery.
The second one is, you know,what do you do with all that information?
So, you know, I, I always, jokewith the residents about this.
Exactly.
(17:42):
But, you know, it's it's funny to methat we talk about postoperative recovery.
We all still look at the same thingslike what are their vital signs?
What are their labs, you know,what are their ins and outs?
And there's so much more information,at least in concept, that we could be
using to, help us make decisions and,
help guide that period of time.
(18:02):
And so I think it's layering on top of,you know, these digital adjuncts,
the type of intelligence,whether augmented,
artificial or our own to help,you know, further those things along.
So, yeah. And then what
you're saying is like informationthat is already being collected.
I mean, if I were to ask you to speculate,
like what percent of that informationbeing collected we’re actually using,
(18:26):
well,what would your estimate be for right now?
Oh, I don’t know, like 5%. Right.
It's totally random.But you know, it's a 5%.
Yeah. It's a it's a small amount.
Yeah.
It's a small amount meaning that there'sa huge data trove of information,
but we're barely scratching the surfaceright now.
How do we get insight from that?
Yeah.
It's still very much an open question,
a really exciting placeto be thinking about what comes next.
(18:50):
Well, and, in the final moments, we have,
Dr Kothari, any words of wisdom or,
for our amazing listeners who arelistening to this podcast episode today.
Yeah.
First, if a med student comes to you withan idea, you know, take it and run it.
Right. So.
So, Carson is, incredible.
And really, you know,did the bulk of of the heavy lifting here.
(19:11):
So just got to give him a ton of credit.
I think that we are at this inflectionpoint, you know, as what
we've been talking about here,where, it's fun to think about
how can we innovate around perioperativecare both before and after surgery?
And, how does wearable device, informationand data fit into that?
So I’m hopefulthat this study at least gets,
(19:35):
you know, the readers and our communitythinking about this.
Not that this is new, you know,thinking about preoperative fitness and,
wearable devices.
I think it's been around,but it's just that, availability access is
is expanded to the pointnow where it's really,
something for peopleto sink their teeth into.
Amazing.
Well, Dr Kothari, thank you for, joiningus today for today's podcast interview.
(19:58):
I was phenomenal to, deep dive into this,incredible article
that you and your coauthors
from the Medical College Wisconsinhave, brought forward.
And in this, latest issue of JACS ,
thank youfor all the work that you're doing.
And, I'm hoping thatwe'll have an opportunity to connect again
in the near future.
Really, really appreciate your time today.
Yeah. Always,always a pleasure talking to you.
(20:20):
Thanks so much.
Thank you for listening to
the Journal of the American Collegeof Surgeons Operative Word Podcast.
If you enjoyed today's episode,spread the word on social media
by using the hashtag #JACSOperativeWord.
Subscribe to The Operative Wordwherever podcasts are available,
or listen on the American Collegeof Surgeons website at facs.org/podcast.
You are listening to The Operative Word,
a podcast brought to you by the Journalof the American College of Surgeons.
I'm Dr Tom Varghese,and throughout the series, Dr Lillian
Erdahl and I will speak with recently
published authors about the motivationbehind their latest research
and the clinical implicationsit has for the practicing surgeon.
The opinions expressed in this podcastare those of the participants,
(00:26):
and not necessarilythat of the American College of Surgeons.
Hello loyal listeners.
Welcome to another episode
of The Operative Word, the podcastfor the Journal of the American College
of Surgeons,where we connect with amazing authors
who recently published incredible articlesin JACS.
(00:47):
I'm honored to be joined todayby Dr Anai Kothari.
Dr Kothari, welcome to the podcast.
Thanks so much.
So excited to be here.
And, excited to be here with you.
You probably don't remember.
Or maybe you do.
Actually, the first article I everpublished; you were the discussant on.
I was the discussant on, that is correct!
At the American College of Surgeons,
(01:08):
meeting. And so, it's just kind of fun
to get a chanceto talk to you about this article.
And now that's great.
And so for the listeners,Dr Kothari is an assistant professor of,
in the Division of Surgical Oncology,Department of Surgery,
at Medical College of Wisconsin.
He's also a member of the Collaborativefor Health Care Delivery Science
at, MCoW as well.
(01:30):
Dr Kothari, let's go,go ahead and get started.
Today we are going to be discussingyour incredible article called “The
Association of Daily Step Countand Postoperative Complication
among All of Us Research Participants.”
This is, published ahead of,
print, right now in JACS and was,
(01:50):
the abstract was formerly presentedat the American College of Surgeons
109th Annual Clinical CongressScientific Forum
that was held in Boston,Massachusetts, in October of 2023.
So Anai, let's go ahead and get started.
What inspired you to pursue this projectin the first place?
It's interesting that there'sactually a very good story around that.
(02:10):
So I was sitting in clinic,I do GI surgical oncology, and
I was preparing to do a gastrectomyfor a patient, and they were just
so excited to show me the informationfrom their Fitbit,
basically telling me how many steps theyhad been taking leading up to surgery.
And we have this unique opportunity,in surgical oncology to do some work
(02:31):
in prehabilitation as they lead upto surgery to a neoadjuvant therapy.
And, we always tell everyone kind ofdoes this generic message,
you make sure you're active,that you're, remaining physically fit.
And, and this was actually objectiveinformation that they were showing me.
And, that kind of setting,this serendipitously corresponded with,
(02:53):
one of the medical students who had cometo me, looking to do a project.
Carson Gehl, who's the first author,
amazing,human being and, going into surgery.
And so, someone that people look out for.
But, he found this data source,the ‘All of Us.’ Researcher Workbench.
And we found that,this is a voluntary program.
(03:13):
They're trying to recruita million participants.
And the data, through appropriate,accesses, can be used by anyone.
And one of the domainsthat's collected is, wearable device data.
So it's linked to the electronic healthrecord.
So here we had this opportunity to takea really relevant clinical situation.
And here it was, a data set.
(03:35):
Then it would let us actually interrogatethat question.
So that was kind of the inspirationand, background for why we,
began to think about,how do we understand the association
between physical activityobjectively and postoperative outcomes?
Well, that's amazing how,
serendipityled to this incredible project.
(03:56):
But I wanted to focus a little biton the All of Us research program.
A little bit further.
So, as you correctly pointed out, it'san NIH funded effort to collect
electronic medical record,crosslinking with health questionnaires,
looking at some genetic dataas well as digital health technology.
How is the sampling?
I mean,I know that there's a million patients,
(04:16):
but is this like, you know,is it a strategic sampling?
Is it a convenience sampling?
How would you reflectback on the sampling, or how the data was,
acquired in the first place?
Yeah, the All of Us program,
it has set this goaland set a million participants.
And they've set upbasically regional centers to try to,
(04:37):
ensure that the final groupis representative or at least
more representative of the populationthan maybe a typical cohort study.
It kind of falls similar to other programsin other countries, like the UK
Biobank, where,
you know, that data set very,
you know, useful and has providedamazing insight on the population
from the UK, but has been limitedin some ways by kind of the distribution
(05:00):
of the population being slightlyolder and, mostly Caucasian,
so that the goal that All of Us researcha program is to try to be more diverse.
It is a convenience sample.
Ultimately,the participants have to volunteer
to be part of the program, and,I'm part of the All of Us program myself.
So basically, you, enroll,
(05:21):
you get permissionto link to your electronic health record,
you provide some blood samplesthat then get sequenced,
you get some genetic testing,you get all that information back,
and then you have the option of also,
if you have a Fitbit or a wearable deviceto link that
into the information, you completesome surveys as part of the intake.
So, it's really participant led,at the end of the day though,
(05:43):
when you talked about generalizabilityfor a study like this, you know,
it falls more until like conveniencesample, framework.
But let's, let's go to the,the actual study itself.
So the primary outcome of the studywas the development of any adverse
event within 90 days of surgery,which I applaud you for doing that.
(06:03):
You went beyond the usual 30 daysand really focusing on the 90 days.
And of course,
you looked at adverse eventsincluding pneumonia, respiratory failure,
pulmonary embolism, sepsis, cardiac
arrhythmia, renal failure,urinary tract infection and DVTs.
And obviously,
you were also trying to do a sensitivityanalysis, trying to figure out
not only the peoplewho were using the Fitbit app or the
(06:25):
the physical activity trackers,but trying to really get down to the -
Was there a cutoff or thresholdabout the number of steps?
Can you explain how you did thatsensitivity analysis or what what
led to down to the path where ultimatelydid figure out the cutoff was 7500 steps?
We started with
just having kind of a general ideaof where that number may fall, or
(06:47):
there's a couple of large studies donein cardiovascular outcomes where that 7000
to 9000 step threshold seems to be wherethere's an impact on health outcomes.
So that was kind of where we thoughtwe may land.
And then what we did is,is just that every 500 steps, looked at
how that essentially was associatedwith our primary outcome.
(07:08):
So there's a figure in the paperthere, just looks at this,
you know, at each thresholdabove and below,
you know, what does the adverse event ratelook like. And,
no, I love artificial intelligence,data science, machine learning,
some of those things.
But I'll say that at theat the end of this, it really was,
you know, kind of visually seeing,you know, where this threshold
(07:29):
looks like it actually was.
And so, that that methodologicallyis how we came near that threshold.
Absolutely amazing.
And so when you, when you went aboutdoing this, and reading this directly
from your paper, it's off, you know, 475participants met your inclusion criteria.
And of the included 475 patients,
(07:51):
60 patients, or roughly 12.6% experienced
an adverse event within 90 daysfollowing their procedure.
Was that surprising to youto find that 12% of
patients had an adverseevent of some type?
Yeah, probably slightlyhigher than you'd expect.
You know, given the cohortand the types of procedures
that were included, we use the NSQIP
(08:14):
inclusion criteria, the CPT, to basically,
kind of identify which proceduresto apply to our population.
I think it speaks to,
what actually happens to individualsin the real world, potentially.
So, you know, so much of the informationthat we have on postoperative
risk really centers aroundwell collected data sets.
(08:34):
What all of us offers now, is this, right,multicenter EHR, information.
So not just what happened to them,but the place where they had surgery,
but potentially other thingsthat occurred afterwards at other sites.
So, we think that may have ledto kind of a slightly higher incidence
incidence of perioperative outcomesand maybe more reflective
of what really happens to individualsonce they recover from surgery.
(08:57):
And what I loved was,you did your initial analysis,
you figured outit was about the 7500 step cutoff.
But I also really thought it was brilliantthat, after you matched active
to inactive participants, you really dida propensity score matching.
You had a 191 matched pairs.
(09:18):
Talk to us about that process.
I mean, I, you know,
we have some buzzwords out there,you know,
propensity matching and things like that.
But talk to us about the rationale for,you you had the cutoffs.
You already had kind of the outcome,of interest defined,
but then you went furtherto do the propensity matching.
So I think there's so many waysto try to adjust
(09:39):
for or think about confounding in studies,you know, regression being one which
you know, we we approached this problemwith multivariable regression as well.
And just to really try to center
as much as we could, to addressthe confounding,
just did additional analysiswith propensity score matching as well.
Now, it's not perfect, and there's only somuch that you can ultimately control for
(10:01):
and likely more confounders out therethat would have, modified the association.
But I think that those stepsare important.
And, and this is always whereyou gotta give some credit to peer review
just because, yeah, it's a villagethat makes these studies work.
And, you gotta give creditto the one of the reviewers who said,
you know, it would be really usefulto get this initial analysis.
(10:22):
So, shout out to that process as well.
It's not all stuff that we concoct.
You know, this is probably the first,this is probably the first interview
in the history of mankind where we’regiving a shout out to reviewer 2.
That's good. That's great. But
that's fantastic.
But, no, I, I, you know, again,
I geeked out on a coupleof different points
that that was one of the thingsI was like, oh, that was perfect.
(10:44):
You know, you just address it right on.
Because again,we're going to get to this as well.
You know,
obviously the limitations of the study,are several and confounding is there.
But but the fact that you went anddid that extra step, it kudos to you guys.
Well, well, let's get right down to the,the take home message is so,
you know, I got reading from the paper,so in this retrospective observed
observational cohortstudy of the participants
(11:07):
that participated in the All of Usresearch program,
we found that preoperative dailystep counts
taken from an EHR-linked wearable
device data were an independent predictor
for the development of, adverse,post-operative outcomes.
And, you know, as youhighlight in the article, which again,
(11:28):
but encourage readers to go, listenersto go and read,
you know,there's some great resources there.
You know, consistent with the literaturekind of where, where
you thought it was going to be happening.
And then you also went on that,the wearable device, measured step counts,
can be
envisioned as a piece of datawith which surgeons,
(11:49):
can consider patient selectionas well as perioperative planning.
In additionto the traditional measures of risk.
I wanted to start thereand ask you a few questions.
It's it seems to be intuitive, right?
The more active a person is,the better that they should do.
Yet, you know, from personal experience,from all of our connections,
(12:12):
this has been so hardto tackle in the world of surgery.
What are your thoughts?I mean, you have these findings.
So what are your thoughts now of wheredo we go next with this piece of data?
It's amazingbecause I think about conversations
you and I have had in the past.
Obviously, you being,
Strong for Surgery, the things that,you’ve thought about a lot,
(12:33):
and have really championed.
But it's been challengingto operationalize that at scale,
I think somewhatbecause measurement is so hard
where it's of entering into this erathat where that measurement
piece is happeningwithout us doing anything.
You know, people are more apt to wearwearable, either on the wrist
or even just on their phone, you know,tracking the steps that they're taking.
(12:56):
So it's this piece of information and datathat I know used to be,
I think, very difficult for usto obtain, with some fidelity.
And now, patients are coming to us
with information about this already.
So, I just think the operationalizationof this information
and actually incorporating itinto a clinical setting has that that,
(13:19):
challenge has
become much less,which makes me optimistic
about using this, you know, itkind of, even beyond step count.
There's other things that these devicesare capturing and calculating
as well, things like sleep and heart rate.
So even more opportunityto to really rigorously understand
what happens before surgeryand that potentially improve postoperative
(13:41):
outcomes.
I guess one of the cynical views outthere has been,
everybody talks about value based care,
but we're still in a fee for, servicebased system.
And, you know,
the cynical views that I knowand you and others have kind of faced was.
Well, Tom, nobody'spaying for that. Right?
(14:01):
I it's like nobody pays for investmentin patients’ health
before we do electivesurgery.
They pay for the surgery, but,
but they don't payfor all the things ahead of time.
But I think that what you're highlightingis that patients are already doing this.
I mean, that's the reality
whether we want to acknowledge or not,patients are already engaged in the space.
(14:22):
It's really more a questionof trying to figure out why patients
or some patients are not just using,
activity trackers, but are really engagedwith this vs others who are not.
Is that a correct way of framing,the question of where we need to go next?
Yeah, I think that's totally right on.
And maybe one additional pieces is now
that patients are doing this on their own,what do we do with that information?
(14:45):
You know,how do we optimally, take that data
and apply it and, and on right now,you know what this study and only show is
okay, there is this association betweenstep count post-operative outcomes.
And, we try to highlight that.
Yeah, we use for risk stratification.
Another piece of informationto go into patient family discussions.
(15:08):
Surgical risk. Prognostication.
But it opens the door
for really trying to studycausality studies.
Where are yousee what happens as activity levels go up.
So, yeah, I think you're right on.
I guess I, let me ask you this,trying to put you on the spot.
You and I are both cancer surgeons, and,the cancer space now is really,
(15:31):
really embracing this concept.
And so, I'll just read youlike the NIH formal definition right now
where specifically in cancerprehabilitation.
So their formal definition is “it'sthe process in the cancer
continuum of carethat occurs between the time of cancer
diagnosisand the beginning of acute treatment.
And includesall physical and psychological assessments
(15:54):
that establish a baselinefunctional level, identify impairments
and provide interventions that promotephysical and psychological health
to reduce the incidenceand or severity of future impairments.”
I mean, that's kind of whatthe NIH definition starts with,
I'm trying to imagine the future.
I'm going to ask you toimagine the future.
I mean, you're oneof our really innovative thought leaders.
(16:17):
And I think in the House of Surgery,not just because you've embraced
artificial intelligence and you're doingall this cutting edge work, but, you know,
it's kind of like, I want to ask youand I what your futurist view is.
Like, imagine five, ten years from now,what do you think is going to happen?
Like your cancer patient comes to eitheryour or my clinic.
What do you think is where we're goingand what are the steps we need to get to,
(16:40):
hopefully that future?
That is an easy question.
Easy question. Of course.
But, I think
there's two fundamental shiftsthat are happening right now.
One is digitally enabled care.
So, you know,we have always viewed health care,
I think, within the walls of,the clinic in the hospital.
(17:01):
But the reality is most of healthcare is delivered beyond that.
And how do you ensure thatwhat's happening outside those walls,
in and inside are,
you know, together and unified?
And to me, that'sreally focusing on a digital strategy,
wearable devices being one part of that.
But, you know, ensuring that,
(17:22):
you know, health informationthat's collected in
one settingis easily exchanged across the other.
So I think that that's one part of itis, this, this concept of a really
the digital ecosystemaround patient recovery.
The second one is, you know,what do you do with all that information?
So, you know, I, I always, jokewith the residents about this.
Exactly.
(17:42):
But, you know, it's it's funny to methat we talk about postoperative recovery.
We all still look at the same thingslike what are their vital signs?
What are their labs, you know,what are their ins and outs?
And there's so much more information,at least in concept, that we could be
using to, help us make decisions and,
help guide that period of time.
(18:02):
And so I think it's layering on top of,you know, these digital adjuncts,
the type of intelligence,whether augmented,
artificial or our own to help,you know, further those things along.
So, yeah. And then what
you're saying is like informationthat is already being collected.
I mean, if I were to ask you to speculate,
like what percent of that informationbeing collected we’re actually using,
(18:26):
well,what would your estimate be for right now?
Oh, I don’t know, like 5%. Right.
It's totally random.But you know, it's a 5%.
Yeah. It's a it's a small amount.
Yeah.
It's a small amount meaning that there'sa huge data trove of information,
but we're barely scratching the surfaceright now.
How do we get insight from that?
Yeah.
It's still very much an open question,
a really exciting placeto be thinking about what comes next.
(18:50):
Well, and, in the final moments, we have,
Dr Kothari, any words of wisdom or,
for our amazing listeners who arelistening to this podcast episode today.
Yeah.
First, if a med student comes to you withan idea, you know, take it and run it.
Right. So.
So, Carson is, incredible.
And really, you know,did the bulk of of the heavy lifting here.
(19:11):
So just got to give him a ton of credit.
I think that we are at this inflectionpoint, you know, as what
we've been talking about here,where, it's fun to think about
how can we innovate around perioperativecare both before and after surgery?
And, how does wearable device, informationand data fit into that?
So I’m hopefulthat this study at least gets,
(19:35):
you know, the readers and our communitythinking about this.
Not that this is new, you know,thinking about preoperative fitness and,
wearable devices.
I think it's been around,but it's just that, availability access is
is expanded to the pointnow where it's really,
something for peopleto sink their teeth into.
Amazing.
Well, Dr Kothari, thank you for, joiningus today for today's podcast interview.
(19:58):
I was phenomenal to, deep dive into this,incredible article
that you and your coauthors
from the Medical College Wisconsinhave, brought forward.
And in this, latest issue of JACS ,
thank youfor all the work that you're doing.
And, I'm hoping thatwe'll have an opportunity to connect again
in the near future.
Really, really appreciate your time today.
Yeah. Always,always a pleasure talking to you.
(20:20):
Thanks so much.
Thank you for listening to
the Journal of the American Collegeof Surgeons Operative Word Podcast.
If you enjoyed today's episode,spread the word on social media
by using the hashtag #JACSOperativeWord.
Subscribe to The Operative Wordwherever podcasts are available,
or listen on the American Collegeof Surgeons website at facs.org/podcast.
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