Platform Trial in Orthopaedic Surgery

Dr. Nathan O’Hara (University of Maryland), Dr. Gerard Slobogean (UC Irvine), and Dr. Sheila Sprague (McMaster University) describe the launch and design of the Musculoskeletal Adaptive Platform Trial (MAPT)—the first major adaptive platform trial in orthopaedic surgery. The discussion covers MAPT’s master protocol structure, patient-centered endpoint framework, and operational strategies for multinational implementation. Focus areas include the FASTER-HIP domain’s use of Bayesian modeling with a hierarchical clinical endpoint and the standards established for adaptation, data coordination, and future scalability. Listeners gain insight into a trial infrastructure designed to lower barriers for evidence generation and facilitate ongoing evidence generation in musculoskeletal trauma care.

Key Highlights

• MAPT as a scalable, master protocol for orthopaedic intervention evaluation
• Hierarchical, patient-centered endpoint (survival, 4-level ambulation, days alive/out of hospital), analyzed with a Bayesian-modeled, non-parametric win ratio
• Domain-specific adaptation thresholds based on clinical differentiation
• Interim analyses after 100 patients, then every 50, informing early adaptation
• 40 sites across US, Canada, and Europe, centralized data management at McMaster
• A unified DSMB structure with capacity for domain-specific expertise as needed
• Tiered protocol access: open sharing, collaboration, direct integration
• Infrastructure enables rapid domain addition and multi-investigator participation

For more, visit us at https://www.berryconsultants.com/

Creators and Guests

Host

Scott Berry

President and a Senior Statistical Scientist at Berry Consultants, LLC

What is In the Interim...?

A podcast on statistical science and clinical trials.

Explore the intricacies of Bayesian statistics and adaptive clinical trials. Uncover methods that push beyond conventional paradigms, ushering in data-driven insights that enhance trial outcomes while ensuring safety and efficacy. Join us as we dive into complex medical challenges and regulatory landscapes, offering innovative solutions tailored for pharma pioneers. Featuring expertise from industry leaders, each episode is crafted to provide clarity, foster debate, and challenge mainstream perspectives, ensuring you remain at the forefront of clinical trial excellence.

Judith: Welcome to Berry's In the
Interim podcast, where we explore the

cutting edge of innovative clinical
trial design for the pharmaceutical and

medical industries, and so much more.

Let's dive in.

Scott Berry: All right.

Welcome everybody.

To In the Interim, I'm
your host, Scott Berry.

Today we are gonna talk
about a platform trial.

We're gonna talk about a platform
trial in kind of a a, a new, somewhat

unique area, uh, orthopedic surgery.

And I'm joined today by, uh,
three scientists here are gonna

tell us all about the trial.

So I have Dr.

Nathan O'Hara.

For, he is an associate professor
of orthopedics at the University

of Maryland School of Medicine, and
he specializes in clinical trial

design, health services, research,
and advanced statistical methods,

which we love here on in the interim.

And he serves as the
principal epidemiologists

and lead analyst for Mapped.

And that's the platform
trial we're gonna talk about.

We'll tell you all
about what mapped means.

Uh, I'm also joined by Doc Dr.

Gerard.

Uh, slab Bogen, who is a professor
of orthopedics and director of the

Biomedical and Translational Science
Master's Degree Program at the

University of California Irvine, and
he's an orthopedic trauma surgeon.

And Dr.

Sheila Sprague, who's an associate
professor in the Department of Surgery

and research Director of the Surgical
Methods Center at McMaster University.

And she has a long history, I won't
say how long, uh, in, uh, orthopedic

trials, orthopedic trauma surgery,
and her group at the university,

uh, at McMaster are going to be the
data coordinating center for mapped.

So welcome everybody to, in the interim,
and so tell me what, what mapped

is, and uh, the acronym of this is
Musculoskeletal Adaptive Platform Trial.

So what is that?

What is the trial?

Gerard Slobogean: Well, thanks Scott.

Thanks for having us.

Uh, we're really excited to be here.

Uh, and I think, I think you
highlighted a, a few things, right?

Which is, uh, orthopedic surgery
has not, uh, traditionally been

in adaptive platform trials.

Um, and a lot of times we haven't
actually been in the large trial space.

Uh, our, our group, um, has
really tried to push that

along with a handful of other.

Uh, individuals in the field, but
fundamentally, uh, MSK problems,

uh, really affect almost everybody
at some point over their lifespan.

And for us, we mainly focus on injury.

And after an injury, um, a lot of
patients are left with, um, you know,

still some degree of MSK impairment.

Uh, so whether that be a broken wrist,
a broken ankle, or major car accident.

You know, those are the, the populations
that we've traditionally focused on.

Uh, and there's millions of
people that suffer these injuries,

um, you know, in, in the us.

So we wanted to, uh, design a
trial that, uh, would have all the

properties that you con constantly
talk about on, on your podcast.

You know, something that can be adaptive,
uh, that can, um, be a platform,

meaning that we can focus on the larger
population and bring new interventions.

On and off the platform as we, as
we learn about them and, and reach a

certain level of decisional certainty,
um, and then have that flexibility.

You know, as I mentioned, we think
mainly in the trauma space, but

the, as you start to expand to just
musculoskeletal impairment, uh, you

can go across all of orthopedics, but
you can go even beyond orthopedics.

And so we started with a little
bit of a narrow focus, but really

wanting to use a, a broad lens.

Scott Berry: Okay, so, so, so
thinking about, um, and this, this

area is somewhat new to me, um, but.

Thinking about the patient here, you,
you talked about this sounds like acute

injury that causes a, a, a skeletal issue.

Um, and, and I, I saw your first
domain, so we'll, sort of, we, we,

we'll maybe focus a little bit on that.

We, and, and we all know people and
I'm now of the age of knowing lots

of people who get artificial hips.

My wife has an artificial
knee, for example.

This is not.

Uh, chronic, uh, type injuries at
least that you're worried about.

This is generally, uh, an injury
causing a skeletal problem.

Is that, do I, do I have that right?

Gerard Slobogean: Well, both.

I mean, I think the issue is is I'm
an orthopedic trauma surgeon, so

my clinical practice is focused on
the injury, but we tried to design

this from a, a patient centered
perspective and pick an outcome that.

Really captures, uh, an important
musculoskeletal, uh, endpoint, uh, for

people that have anything, you know,
including chronic knee pain or hip

pain, or, you know, that that gets a
hip replacement and things like that.

So it may not be sensitive
enough for every MSK, uh, issue.

And so, you know that that's why you
always pick a good platform, pick a

good trial to test your intervention,
but we think it's, you know, like

a, a 70% solution for a lot of key,
uh, orthopedic questions out there.

Scott Berry: Okay, so maybe it, maybe
it helps then to think about the,

the, the overall master protocol.

You're going to bring in domains
which ask specific questions,

and you have the first domain.

The first domain is funded and,
and, uh, apparently just opened.

You haven't enrolled patients
into the, so maybe the, and your

first one is called Faster Hip.

So what does a patient

look like for the faster hip
domain that, that you see, Gerard?

Gerard Slobogean: Yeah.

Yeah.

So, you know, I think on the master
protocol side, we're really trying to,

to focus on comparative effectiveness.

So decisional dilemmas, treatments
that are already in practice.

Uh, and so faster HIP is, is a focused
on a certain population of hip fractures.

Something that we call minimally
displaced femoral neck fractures.

So if you think of that, ball and socket
is right underneath the ball of the hip

joint and it's broken, but the two pieces
aren't widely separated from each other.

And so that's the population
that we're looking at.

Uh, and if they are widely s uh,
separated, they always get some sort

of hip replacement, either a total hip
replacement or a half hip replacement.

But when they're not set widely
separated like this, there's a dilemma

as to whether you should just do a
smaller surgery and perfuse screws

in there and kind of pin the hip as
people would say, um, or whether you

should replace it and replacing it.

Is a much bigger surgery.

So there's that patient centered dilemma.

From a surgeon perspective, one's
really small and quick and easy.

The other one's a little bit
bigger, so is the bigger surgery

justified for the patient?

Um, and as we know for older
adults, patients that have hip

fractures, some of 'em are, you know,
relatively active and some of 'em

are quite frail too, and and older.

So having that, having evidence
to guide this decision is

really important for our field.

Scott Berry: Oh, okay.

So, so this is, uh, uh,
somebody's a car accident.

Somebody's, you, you, I know we have
a good bit of Canadian representation,

maybe a hockey injury, a a skiing
accident where they come in and

they've got this, this moderate, right.

Now, what, which one
is generally done more?

Uh, and, and so is one thought to be,
I know standard of care is not right

thing, it's comparative effectiveness.

Both are done, but what is the
relative frequency of replacement

as opposed to fixation?

Gerard Slobogean: Yeah, I, I think, you
know, the other important part is that

these tend to be older adults, right?

So the hockey, the hockey accident
and the car accident tend to be higher

energy, younger, uh, population.

So this is focusing on lower energy,
a little bit older, um, uh, patients.

So in the older adult population.

Usually what's done is the
smaller, the smaller surgery

with the pins, the the screws.

Um, and, and that, you know, we thought
universally was a great idea and as

we, we investigated more, we we're
seeing complication rates up to 15%.

And so that has started to swing
the pendulum towards the bigger

surgery, the arthroplasty.

Scott Berry: Oh, fascinating.

Fascinating.

Okay, so, um, so Nathan, um, your, you.

When this trial reads out as, as sort
of the epidemiologist or in this, uh.

What, what impact is this gonna have?

Uh, just specifically this domain,
we'll come back to the larger one and,

um, uh, the building this adaptive
platform trial, is this regulatory, is

this funders, is this trying to, are,
are these trauma surgeons that are

going to then change their practice?

How does this impact, uh, uh,
when the result reads out?

Nathan O'Hara: Yeah, I think
the biggest target audience are

the surgeons and clinicians that
are treating these patients.

And so, um, you know, as Jared mentioned,
we've taken an, uh, what we think

is a very patient-centered lens to
designing the outcome for this study.

Historically, a lot of orthopedic
trials, in particularly the orthopedic

trauma research, would focus on
reoperation as a primary endpoint.

That was very important to surgeons.

Their complication rate is important
for reimbursement and hospital

administrators care about that.

But as we spoke to patients and
we did a number of what we call

discrete choice experiments to
look at patient preferences, the

kind of result that we kept on
seeing time and time again is that.

Reoperation was important, but it wasn't
the most important thing to patients.

patients.

wanted to resume their lives.

They wanted to regain their function.

Uh, and so ambulation was actually
much more important to patients.

Of course, everyone wants to survive.

That's the top of our hierarchy within,
within this adaptive platform trial.

But number two is an ambulation status
at four level ambulation status.

and and so they, they aspire for that.

But we, we recognize that
the re-operation complication

thing is also really important.

We think that rate is high.

And so, you know, we have this
hierarchical outcome, as I mentioned.

So survival is number one, ambulation
status, and then the third level

of this three level hierarchy as
days alive and out of hospital.

And so that's meant to capture
these complications, uh, both

their frequency and some indication
of their severity as well.

So not all complications
are created equal.

One that puts you in the hospital
for many weeks is gonna count worse

than one that maybe just requires an
ed ed visit or, or some, you know,

consultation, um, with your provider.

Uh, and so I think, you know,
having, going through that process,

creating a primary endpoint that is
patient-centered is also, I think, taken

very well, perceived very well by the
orthopedics community, uh, that we hope

to then communicate the results to.

Um, and share that, you know, we're lucky
to partner with a number of professional

societies in that space, um, and hope
that the result will, will be, you know,

able to influence practice in a way that
that provides patient centered care.

Um, it might not be one size fits
all for every patient, um, but we'll

hopefully provide clinicians with the
information that they need to make an

informed decision about the best care
for the patient that they're treating.

Scott Berry: Okay, so you, you
touched on this endpoint and patient

centered and the, the endpoint and
and it sounds like there's, there's

no single right answer, uh, for this.

And so it sounds like a good bit of effort
went into this and so maybe, maybe worth.

Uh, identifying.

So the, for at least for faster hip,
which is the first domain that we've

talked about, the the primary endpoint
for this is a hierarchical endpoint.

You're gonna do a win ratio, uh,
between them, and so you're gonna

rank patients as to their outcome,
and that is the first level is death.

Uh, for that.

And then the second level is their
ambulation status, and you've broken

that up into four ordinal categories

Nathan O'Hara: Yes.

Four level or no variable.

Yep.

So everything from, you know, the
worst level is unable to walk.

Uh, unfortunately that that
occurs some of the time.

And then the best level is you're walking
independently in the community without

an aid and two levels in between there.

Yep.

Scott Berry: Okay, so the primary
endpoint compares a patient, so a

i and so I got a fixation, um, and
another patient got a replacement.

You would compare our, our outcomes
and if, if I died and the other patient

didn't, that patient wins that comparison.

Um, if neither of us, if, if neither dies.

Then it's who has better
ambulation status.

And it's, uh, and it's
four categories to that.

If they're tied on that and we both are
able to, uh, walk without assistance,

we, we do really well on that.

The, how do you break the
tie between us after that?

Nathan O'Hara: Then it's the one who
has the most number of days alive

and out of the hospital within the
first 120 days after time zero.

Scott Berry: Okay.

Nathan O'Hara: yeah.

Scott Berry: And So,

we both, we we're both alive for 120 days.

So then

it's, it's largely days in hospital.

Uh, between that

is, is,

Nathan O'Hara: 120 minus days in hospital.

Yeah.

Scott Berry: yep, yep, yep.

So large, so days free of hospital
within the first 120 days.

So if I, if the initial procedure,
I'm there for 30 days, but I have

complications, I have res surgery, I
go back in and spend another 60 days.

That's.

I'm gonna lose that comparison to
somebody that doesn't every surgery.

So in many ways, this endpoint,
you think categorizes that.

And, and, uh, within that is, is the
endpoint, uh, uh, very nice and um.

I suspect, and maybe this is a
question within orthopedic trials,

we do FDA devices for spinal implants
and, uh, bone, uh, bone putty for,

for, for things and different stuff.

And a lot of it is, is it's about
pain, it's about radiological

success and things like that.

Never done a win ratio in this.

Are you aware of any other orthopedic
trial scenarios that does a win ratio?

Nathan O'Hara: Uh, we've used it before.

Yeah.

Scott Berry: Oh, you have?

Okay.

Nathan O'Hara: of our group,
uh, I'm not sure of others.

Um, but it tends to be well received
because it, again, it depends on

what the research question is.

If the research question is.

You know, an antibiotic powder to
prevent infection after orthopedic

trauma, you should probably use infection
as your primary endpoint for that.

However, for these things were, you
know, these surgeries that we're

comparing the two different surgical
fixation methods, there are a few things

that are really important to patients
and there's a hierarchy within that.

And so we, we feel as though the
win ratio is patient-centered

and respects that hierarchy of
elements that are really important

to the patients and their recovery.

Scott Berry: Hmm.

Gerard Slobogean: Yeah.

One, one thing I wanted to add, you
know, particularly for the, for all

the statisticians and people that are a
little more savvy than I are am is some

of the statistical efficiencies we, we
realize with this approach as well, right?

So a lot of the re-operation type
clinical trials that have been done in

our field, you know, mandatorily have a
1500 patient sample size because it's.

You know, it's a 8% reoperation rate
versus a 5% reoperation rate or whatever.

And it, it doesn't really
capture the entire experience

for a lot of these patients.

Uh, and of course it requires
a much larger sample size.

And so we feel that we're,
we're doing a better job.

Patient-centered wise, we're doing
a more efficient study design,

uh, in that regard as well.

Scott Berry: Okay, so let's, let's go to
the design and, and, uh, uh, Sheila, the.

You get labeled the
statistician in the group.

Is that fair?

Uh, may.

Maybe I won't do that.

Okay.

Okay.

Uh, uh, well, uh, we, with
within the, within the design.

Uh, of this, and I know you all have,
have impacted the design, by the way.

Uh, Jason Connor put, put us in
touch, uh, about this very cool trial

that we, we should talk about this.

So thanks to Jason Connor, uh,
for, for, for putting in touch.

The design I think is quite innovative
and let's we within the, the

faster hip your design is Bayesian.

Within that, you've got an adaptive
design, so you're doing relatively

frequent interim analyses in this and
the the, so, so tell me about the design.

You're gonna start enrolling patients,
the data's starting to accrue.

What are your adaptations
in the design in the.

And, and, and I don't, I, I, I,
I didn't address that to anybody.

I don't know if, uh, Nathan,
you want to address that?

And then I'm gonna come back

to say, Sheila, how are you
gonna operationalize this thing?

Because I think it gets bigger than that.

So, Nathan, what, tell me

about the design.

Nathan O'Hara: sure.

So we'll start, you know, with
interim analysis after, uh, first

hundred patients have completed
their primary endpoint follow up.

Um, so that's in a Uh, two
treatment group design.

Obviously that would change depending
if we have more than two arms within

the trial, but start, uh, after the
first a hundred patients and then

every 50 patients thereafter that.

And so she mentions the win ratio
design, which is non-parametric.

Uh, but we worked with, uh, Jason and
another colleague, Vitali Drucker, who's

been fantastic to, and we have, um,
a Bayesian version of the win ratio.

So we're essentially taking the.

The log or yeah, the log estimate of
the win ratio and the standard error,

and then combining that with a prior,
um, within a Monte Carlo simulation to

get a posterior probability on that.

Uh, and one other kind of unique
feature of this adaptive platform

trial is we have two different bins.

And so the investigative team
will pre-specify whether this

is a high differentiation trial.

Or a low differentiation trial.

So faster hip is what we would
call a high differentiation trial.

These surgeries are very
different than one another.

We need more certainty in our
estimate to stop the trial.

We have other inter intervention
domains that we're considering, for

example, two different nutritional
supplementations that are pretty similar,

doesn't really affect the patient, you
know, differently one way or the other.

Maybe there's a treatment benefit, but
the, the actual burden of taking one.

Versus one treatment versus the
other is not that different.

That would be a low
differentiation comparison.

So the stopping criteria and the
posterior probability that we require

to trigger either superiority,
inferiority, equivalence is different.

Whether it's a high differentiation
or low differentiation.

So then as we, and it also, there's a
different prior applied to either one.

So there's a more skeptical prior
applied to the high differentiation,

not a skeptical with, um,
with a low differentiation.

And so then as we, you know, complete
each threshold, once you reach that

a hundred patient threshold, uh, you
know, the data goes from Sheila's

group at the method center, McMaster
to an independent statistician

and DSMC to review those data.

And compare the interim results against
the stopping criteria that have been

pre-specified, depending on whether
it's a high differentiation or low

differentiation, um, comparison.

Scott Berry: Oh, okay.

So you have this master protocol
and you're, you're expecting

this is the first domain.

You're expecting multiple domains
to come in, uh, within this.

And so at the master protocol
level, you've defined multiple

ways in which these domains are
gonna look like when they come in.

The high differentiation,
the low differentiation.

So in the first one, this faster hip.

Uh, scenario the, which you've referred
to this high, high differentiation.

It's really interesting because I
sometimes see comparative effectiveness

where you almost define one to be,
well, this is the standard of care, and

you almost treat it like a new novel.

Therapy being compared to that.

Is it statistically different?

And when it's not, you end up in
a really awkward thing because.

They're, you know, it's not a new
novel therapy to compare to something.

So you really are treating this,
I'll say symmetric, that both

of these are being compared.

You could define it as one is superior to
the other, and that's completely symmetric

as whether fixation or replacement wins.

You can also stop when you have high
evidence that they're equivalent.

Uh, within that.

Um, and so the trial is pretty much
we're gonna run, it's a two horse race.

We're not going to call one of
'em the standard of care, and

the other one's gotta beat it.

You're not giving anything a headstart
within this, and you're gonna say, does

one beat the other one superior or not?

Or at some point you're gonna
say, look, it's a dead heat.

It's they're non-inferior.

You'll publish the data, you'll
present all the secondary outcomes.

So that's the way this faster hip one
is set up and you're just gonna keep

doing interims till we find the answer.

Is that a reasonable
summary of the goal of that?

Nathan O'Hara: Exactly.

Yeah, that's,

yeah.

Very well put.

Scott Berry: Okay.

And, and the Bayesian part of it,
which is you said it's non-parametric.

So Bayesian and non-parametric
for our statistician, people

don't necessarily go together.

Uh, but that, but you have phrased
it as towards the probability of

a win is what's uncertain to us.

And that's what you're
modeling in a Bayesian way.

And so that's how you get your
posterior probabilities out, uh,

at this in the end of the day.

Very, very nice.

Nathan O'Hara: Yeah, not, not
a natural fit, but we think

is, is, you know, workable.

Scott Berry: Okay.

And now, um, for example, the
New England Journal of Medicine.

So we've had trials that are Bayesian
trials and that all goes over well, but

if you submit it, they want everything.

Bayesian is this set up.

So the whole thing is Bayesian.

The, the, the, the analyses
of all your endpoints is

Bayesian at the end of the day.

Nathan O'Hara: Yes, every, all the
secondary endpoints would be analyzed

in under Bayesian framework, um,
in a, in a more traditional sense.

Right.

The, the stopping is based
on the primary endpoint.

And then once you've stopped based
off the primary endpoint, then all the

secondary endpoints will be analyzed,
um, in a more traditional Bayesian sense

with, you know, logistic regression
models and, and more standard approaches.

Scott Berry: Okay, so, and, and
this is now, um, you would love this

to address all kinds of questions.

Um, but, and you've got this
first domain and now you're

hoping other domains gets funded.

You have this really unique thing that.

Somebody else could get something
funded and come in and, and, and use

all of the framework you've set up.

So, uh, uh, this is really a, a very
cool effort that in many ways this is a

learning environment that you're going
to try to fund additional questions

to come on here, but others may be
able to add on to this and just.

Increase the learning of this.

So you're gonna have these multiple
domains with interim analysis defined

every 50 patients and you, so, so Sheila,
let's say we've got three of these

running, we've got these multiple interim
analysis, um, operationalizing this gotta

be a little bit, sort of nerve wracking
in this for the data coordinating center.

Sheila Sprague: Yeah.

Yeah.

It's been a, it's been a big lift to
get, to get this, uh, off the ground

at our data coordinating center.

We've been very well supported.

Uh, Joe Patterson has been the PI on.

The faster hip domain.

So we've already brought in somebody
different to orthopedic surgeon to

to lead that domain and it's funded.

We were fortunate to have job be
funded through pcori, which has

allowed us to have a patient-centered,
uh, focus for getting the.

The data coordinating center going as
well as a lot of engagement for the

research coordinators and research
teams at our 40 clinical sites.

Much of the infrastructure at the method
center is very, like, it's very similar,

and we were able to use templates from the
previous trials that we had, that we had

have run over the past few years and adapt
them to make them into, to make them in,

to make them suitable for the platform.

Platform trials

and we have a set of sop, like
we've adopted our SOPs and study

specific procedures to, to allow
for the conduct of a platform trial,

Scott Berry: Oh, very nice.

So 40 sites enrolling and
that's, uh, US and Canada.

Sheila Sprague: as well as, um,
several sites in Europe as well, but

mostly, mostly in the United States.

A handful of sites in Canada and
about 10, 10 or so sites in in Europe.

Scott Berry: Alright, interesting.

And, uh, uh, so a single database
for, for all the sites or you Okay.

Sheila Sprague: Yeah, everything goes.

The one database for
this intervention domain,

and we follow our usual like data.

The team, like my team at the Method
Center, follows the usual daily checks

of the data, their reports, and we
have the DSMC already established.

We had our, like our first meeting
with them a few weeks ago and went

over the processes and procedures
and reporting requirements with them.

So yeah.

Scott Berry: And so, uh,
DSMC is this, uh, uh.

Are, uh, safety, they're, are
they charged with safety, uh,

in monitoring this as well?

Sheila Sprague: yeah.

They're charged with safety as well as
looking at the interim analysis results.

Scott Berry: so they're going to be
involved in reviewing an interim.

Uh, it says one of these
two therapies is 99.3%

better.

They'll review that.

Make sure.

things things look good, and
then they make a recommendation.

Presumably back are are, do you have
1D SMB that's going to oversee the

entire platform, or are you going to
sort of create different D SMBs by

domain?

Sheila Sprague: Our vision
is that we go ahead.

Nathan O'Hara: yeah, I think vision
broadly have one unifying body,

uh, potentially with rotating
members if additional expertise

is required for certain domains.

Scott Berry: Yeah.

Okay.

Okay.

Nice, nice.

And, and just for a status of this
is, um, you've started opening its

sites, uh, within this, um, and, but
you don't have any patients enrolled.

So we're at the very,
very early stages of this.

Um.

Is, uh, is that where
the trial sits right now?

Sheila Sprague: We actually just opened
our first site for screening today, so we,

so we're really excited about, about that.

And then we have a few other sites
that will be, uh, open for enrollment

in the next couple of weeks.

So we're on track with our timelines
for our February 1st, uh, for

start date for patient enrollment.

Scott Berry: So, Gerard, what is the.

Um, is there excitement in the
orthopaedic surgery space for research,

for investigating these questions?

Uh, I, I, know oncology that
they're used to this, but what

is the reception from orthopaedic
surgeons about this whole effort?

Gerard Slobogean: Yeah, I, I,
that's a, a great loaded question.

Um, you know, I think there's
variability across our, our field, right?

So orthopedics, you know, some people
will specialize in joint replacements,

some people in fractures, you know,
so there's a, a wide variety there.

In my subspecialty in in fracture
care and, and trauma care.

Um, there's actually been a couple
decade sort of history of, of

doing clinical trials and so a
lot of people in our area are.

Um, familiar.

They may not be, you know, statisticians
and, and real experts like you see

infectious disease and, and oncology.

There's a lot of people that do, uh,
extra training in research, uh, that we

don't really have in our, in our field.

So there's, there's general interest.

There's a lot of excitement about, uh,
Bayesian analyses, you know, as we, as

they start to think in probabilities, the
way they make their clinical decisions.

So we've been kind of introducing
that to, to our field and people

have been really excited about that.

Platforms also really excite
people as they get the concept.

And, we were joking that, you know,
once a week somebody calls us and

says, Hey, we need a platform in, or
I wanna start a platform for this.

You know, so we're, we're sort of
navigating, uh, navigating that field.

But I, I wanted to actually bring it,
you know, sort of right back to the

beginning and to your last comment
about the learning health system.

We really see this as a big
contribution to the field in that space.

So.

Um, you know, Sheila
mentioned Joe Patterson.

So, uh, Dr.

Patterson's a an
orthopedic surgeon at USC.

And so, you know, he had an
idea, it fit the platform.

It's been brought onto the
platform, and now we have another,

you know, uh, investigator that's
becoming savvy and understands

and can lead, uh, platform trials.

And we have a few other under review
that, you know, Nathan, uh, mentioned.

And similarly on the funder side.

So, you know, we, we really
want to acknowledge an iams.

Uh, they gave us.

An R 34 to go through the planning
work, which as, as you know, for some

academics that are doing other things,
uh, it took us a year to do this,

uh, to get all the simulations and to
understand it and to, and for Sheila's

team to operationalize it, right?

So.

But when we submitted it to NI
Iams, they were a little concerned

about the proposal because we listed
like 10 potential interventions

and they're like, whoa, whoa, whoa.

We can't fund 10 trials here.

What are we, what are we funding?

And so, you know, Chuck
Washabaugh is the program officer

there, really work with us.

Brought it to their leadership
and, um, they supported us.

And then now PCORI funds the first
one because it's very patient

centered and fits their mission.

So we really think that this
contributes to a system, a system

of research where we can learn.

And as we get the information
we need to improve care, we

move on to the next question.

Hopefully some, some of
these questions are superior.

But some of them may be,
keep doing what you're doing.

Have confidence that what you're doing
is equivalent, um, you know, in clinical

terms, uh, to the other intervention.

And let's, let's test the next thing
that may improve mobility and ambulation

and, and days at home for our patients.

Scott Berry: All right, so, so go.

Go ahead,

Nathan O'Hara: Yeah, I was gonna say
to build on that right there, so many,

um, orthopaedic surgeons, investigators
within the field that have great

clinical questions, but they don't
necessarily have the institutional

capacity or the support to get it off
the ground and run the clinical trial.

So our hope is not only to
generate evidence to support

their clinical practice.

As Drawn alluded to, you know, as
Joe Patterson being a great example

of this, it provides a platform for
investigators that have good research

questions but don't necessarily have the.

Support today that they need to
bring the questions to this platform.

And then much of the material
has already been developed.

The master protocol,

you know, some templates for intervention
domains, there's a database already

constructed, a method center.

You know, data safety
monitoring is already set up.

You know, it's, it's really a
much lighter lift for someone

who has a really good question.

Um, and I think dramatically increases
their ability to obtain funding right when

this infrastructure's already in place.

Scott Berry: Yeah.

And, and so it's the, the
whole argument is, is amazing.

And the learning healthcare system
that you brought up, Gerard, is,

is, something that's just, um.

Uh, a, an amazing potential,
uh, advancement right now.

If somebody, uh, at a site here
in Austin, Texas fits into this

and is treated and somebody does
fixation as opposed to replacement.

Nobody learns from that.

Maybe that surgeon learns from that,
but the data aren't accumulated.

The, the, we're treating lots of
people without learning from them.

Now you're creating this system
that we're treating them, we're

doing state-of-the-art, learning,
um, and treating at the same time

is we should be doing this in.

In every healthcare scenario.

So that, that, that whole idea is
incredibly compelling, uh, within that.

So tell me, um, in this.

I, when I've been in these efforts,
it can be almost intoxicating to now

that you've got a patient that you're
treating, are you gonna try to do

factorial things on those same patients?

You, you could go wider and you could
look at a completely different kind

of injuries, uh, an ankle injury or
a leg injury, uh, as opposed to hip.

But now that you've got this patient and
they agree to be part of this experiment,

are there other things you can do?

Do you plan to do factorial things
within the same patients you've got?

Gerard Slobogean: Yeah, uh, I'll,
I'll take that from the clinical side

and then, you know, anybody else can
jump in on the methods perspective.

But, you know, conceptually, we,
we've envisioned being able to learn

at every step of that patient's, uh,
journey because there's decisional

dilemmas for everybody, right?

So the moment you come in with your hip
fracture, should you have a temporary

nerve block to decrease the pain.

What type of anesthesia should you have?

What surgery should you have?

Obviously what type of
rehab should you have?

So we are making these decisions
constantly for patients

with imperfect information.

And so, as you've alluded to, we can
start randomizing all those things.

We don't have those intervention
domains, uh, up and running yet, but

conceptually that's what we envision.

Now, the question about factorial
or not, um, you know, I think.

Yeah, this is already
happening in everyday practice.

And so by not by randomizing that, that
helps, I think, helps even the, the

playing field a little bit, whether
it should be analyzed as a factorial,

um, interaction of, of interventions
or not, I think is up for debate.

And, um, people ask us, ask us that a lot.

And, um, Nathan, you've
thought about it a little bit.

What's, what's the epidemiology
perspective on this from

your, per, from your point?

Nathan O'Hara: Yeah, I think, you know,
as we build the different intervention

domains, I think we'll consider each one
independently is, and look at what else

is kind of on the platform at any given
time and assess is there any clinical

evidence that there may be an interaction
between, you know, this intervention

domain and some other intervention domain
and, and address it accordingly being in

the comparative effectiveness space with.

You know, all likely intervention domains
to be happening out in the real world

as it is already, as Gerard alluded to.

I think, you know, randomly assigning
them is a step better than just

letting people make decisions along the
pathway and adapting their decisions

based off of other random assignments.

Um, and so, yeah, I think it would be
context dependent on, on what happens.

I, I would err on the
side of, I think, mm.

Fewer, we'll have clinical interactions
than we expect just considering some

of the inter intervention domains
that we're, that we're considering.

I think it's unlikely there's interaction
possible, but yeah, as we add new

intervention domains on, that's,
that's part of the consideration.

Scott Berry: Hmm.

So, uh, uh.

With the possibility that a single
patient might contribute to multiple

questions, and I know you can,
you can discuss whether you model

interactions there or you don't.

Part of this adding other domains
to this, other groups coming in.

Um, maybe the question, Sheila,
is, is what keeps you up at night?

Uh, are, are, are, are there
things, are there parts to this

that makes you make you nervous
about the future of the platform?

Sheila Sprague: Yeah, I think things
are, there's always something to to worry

about and there's also always the general
excitement of the next day to keep you,

keep you up.

That keeps me up the most.

I, I think, um, we really want
the platform to be widely used

and available for other people.

So we have three different levels that
we, we, we plan to have it available.

Like we, we hope to have it open access
and available for other investigators to

take, take the protocol, take the CRFs.

Some of our other documents and implement
it on at their method center, data

coordinating center with their team.

And then our second level is with
some, is basically open access,

but with a little bit more support
from Nathan Jar and myself.

And then the third level would be running
it on the, running it within the data

coordinating center infrastructure that we
have already developed with the, with the

initial funding and with with foster hip.

Scott Berry: Hmm.

Very nice.

Very nice.

So, so very, very exciting.

Um.

Uh, and, and I can't, it's it,
everybody seems bright-eyed and excited

about this, so I don't want to ask,
um, uh, various questions that you,

you're, you're moving forward on this.

Once results start coming out and the data
and all this, it'll be good to, to come

back and, uh, uh, ask these questions.

When do you, when are you, um,
maybe hoping for this domain to read

out what would be a great target?

Sheila Sprague: Careful.

Nathan O'Hara: Uh, we

have

Gerard Slobogean: don't

Nathan O'Hara: contractually
obligated target

Gerard Slobogean: don't
wanna say something.

Scott Berry: I, I,

Gerard Slobogean: Um, it's
gonna take us a few years.

Sheila Sprague: Yeah,

Scott Berry: Okay.

Okay.

Yep, yep, yep.

Very nice.

But, but, but very exciting as well.

Uh, plans to expand beyond 40 sites.

Is that potentially part of this?

If, if, if this becomes
successful in multiple domains

and multiple investigators, is
that part of the, the future?

Sheila Sprague: For sure we, we'll
definitely add additional sites for other

intervention domains as we add them on.

Scott Berry: Yeah.

Very nice, very nice.

Well, uh, an incredible effort, um, uh,
incredible learning healthcare system in

kind of a, a, a new research area, which
is, as you described, is very much needed.

Um, and it's not rare, um, uh, it
sounds like within this, so, um, um.

Very, very exciting effort.

I love the Bayesian approach to this.

I, I want to make sure we have you guys
back and hear how this is going, the

updates to this, what's being added to
it, and then how the process of this, this

master protocol is going, lessons learned.

I think it would be great, but thank you
very much for the first interim of this.

Uh, we'll have multiple interims of this.

We have no data yet.

Uh, within this, but, uh, appreciate
you and, and thank you all for

joining us here in the interim.

Nathan O'Hara: Thanks so
much for the opportunity.

Sheila Sprague: Thank you.

Gerard Slobogean: Yeah.

Thanks Scott.

Really appreciate the show.

We learn a lot from it actually.

Scott Berry: Uh, wonderful.

I, I enjoyed doing it.

Love to talk groups like this.

Uh, until the next time,
we'll be here in the interim.