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.
Back to, in the interim where we talk about all things clinical trial science.
And I have a, uh, distinguished clinical trial scientists, uh, with me today.
So I have Dr.
Derek Angus.
Who is a distinguished professor and holds the Mitchell p Fink endowed
Chair in Critical Care Medicine at the University of Pittsburgh.
Uh, also secondary appointments in medicine, health policy and
management, clinical Trans Clinical and translational sciences.
He's been the chair of the Department of Critical Care Medicine since 2008.
Is, and since 2015, he is been the director of the, uh,
U-P-M-C-I-C-U Services Center.
He's done everything in critical care.
Uh, he's in Pittsburgh, uh, uh, in the Department of Critical Care.
He's also a senior editor at jama.
So Derek, welcome to in the interim.
Derek Angus: Thank you, Scott.
It's uh, it's my pleasure.
Scott Berry: Yeah, so this is a, a, a space.
We want to talk about all things clinical trials, and we, I think we have
several topics we'd like to go to here.
And the first topic, uh, I think is, is interesting in terms of clinical trial,
trial science, and something I don't know much about, but steroids for the
treatment of community acquired pneumonia.
And maybe we should set this up a little bit.
Um, my experience and, and Derek and I worked together on the REMAP CAP trial.
We've worked together on multiple things.
Uh, I, I consider Derek a very good friend.
Uh, so I'll get that, that, uh, conflict of interest outta the way.
But let's talk about steroids.
So the history of steroids.
It'd be better if you give this history of steroids from Cape Cod through Remap Cap.
What do we know about using steroids in community acquired pneumonia,
and why is it so controversial?
Derek Angus: It kind of tires me just to think about the topic, but, um,
all right, let's, let's, so first of all, steroids are incredibly
powerful pleiotropic agents.
They do all sorts of things, uh, that, broadly speaking, in this space of
critical illness can include dampening down the immune system and can
include some potentially beneficial.
Uh, effects on your cardiovascular system by helping, uh, uh, retain
fluids help improve resolution.
But they're pleiotropic in that they do all sorts of things that
are disadvantageous as well.
Uh, every medical student learns about all the pros and cons of prolonged
steroid therapy and so forth.
So they are powerful agents that do a lot of things and they're dirt cheap.
So if they were to work, uh, and you knew how to give them, uh, they
could be incredibly advantageous worldwide in all sorts of settings.
Um, in the space of community acquired pneumonia.
So first of all, we're not necessarily talking about the pneumonia where
you never come to hospital, but of all the pneumonia that gets you sick
enough that you need to go to hospital.
That also overlaps with getting sepsis.
If you come in with a pneumonia and you get organ dysfunction, you've
effectively met the criteria for sepsis.
And so when you talk about steroids for pneumonia, you're also
talking about steroids for sepsis.
In fact, pneumonia is the most common cause of sepsis.
So there's a big literature on trying steroids in both sepsis and in pneumonia.
And when you do a sepsis trial, uh, of steroids, half
of the patients have pneumonia.
And when you do a pneumonia trial, half of the pneumonia patients have sepsis.
So they're sort of, they're joined at the hip, um, back in the eighties.
Uh, people were giving huge boluses of methyl prednisone, like a gram of
prednisone, and people thought that would instantaneously bring people
back from the brink of otherwise dying within minutes of profound septic shock.
And then a couple of big trials said.
Oh no, this is actually killing people.
You, you might think that you get some temporary stabilizing effects
on the cardiovascular instability, but actually that's such a huge dose.
It probably causes secondary infections, et cetera.
And so then steroids disappeared and for a long time people thought,
yes, steroids always help in the short term, but as best as we can
tell, they're not actually helpful.
And then around the turn of the century, the French, uh, really led by
Jali and Ann started to suggest, um, we weren't using steroids properly.
Um, and he in particular started thinking
Scott Berry: And, and properly, is it mean, the right patient or the right kind?
Derek Angus: Yes, exactly.
So he came up with you.
He came up with, you need to use a different formulation in
a different subset of patients.
Um, so, uh, he started experimenting with.
Effectively giving a week of hydrocortisone, not a day of methyl
prednisone at a much lower dose of sort of total steroid dose.
He also combined it with food cortisone because he argued that that would
also be beneficial and he restricted it to these really sick septic shock
patients that he felt had not responded.
To fluids and, and, and, and who were so likely to die of the sepsis
that you should give them steroids.
That if you, if you used a milder or broader cohort, you might accidentally
give patients who weren't going to die anyway, but now you might increase the
likelihood of harm with the steroids.
And he, and he particularly also said there's even a subset in whom.
They're ex, we all make steroids and it, it's a stress response hormone.
And so he said you should even be doing a test to try to understand whether your own
endogenous stress response is compromised.
Uh, and if you can't mount a good enough internal endogenous steroid response, you
would be particularly likely to respond.
Bottom line is he had a spectacularly successful study.
Suddenly the international guidelines were saying, oh, you should give steroids.
They clearly work in sepsis.
And then people started playing around with broader patient
populations, again, like pneumonia.
Uh, they said, well, maybe the pneumonia that's particularly
pro-inflammatory might benefit the most.
Once you know you have antibiotics on, you should maybe also give steroids.
And there were a number of.
Studies that were sort of grumbling along, somewhat beneficial or not.
But meanwhile, in the broader area of sepsis, no one could repeat Ali's study.
The Europeans tried it in something called Corticus.
Um, uh, the, the international guidelines, which were published
every four years, all through the.
21st century.
If you pull up the steroid section, the, the literature was changing slowly, but
every four years the guidelines would be written differently depending on who
was in the room because this was, uh, what I would call an opinion rich data,
poor environment, and, and, and people just felt, they continued to feel like.
Uh, everyone had their favorite study.
If you wanted to give steroids, you liked the Annane study.
If you didn't wanna give steroids, you liked the Corticus study.
So then people said, enough is enough.
We need some bigger, more definitive trials.
And two groups decided to do much larger trials in septic shock, and they're much
bigger than any of the pneumonia trials.
So, I know you asked about pneumonia, but I feel like in many
respects, the sepsis literature.
Is the more dominant.
So, uh, the French did another much bigger study called APROCCHSS um, and it
was published in the New England Journal of Medicine, and yet again, in their
hands, they knocked it out of the park with a big improvement in mortality.
Meanwhile, the Australians ran the trial called Adrenal with
several thousand patients also published in the New England.
No Benefit from steroids.
It's like, are you
Scott Berry: A, any, any harm?
Any harm potential from adrenal?
Derek Angus: So Adrenal didn't really show any harm overall.
Um, but one obviously wonders if the French were successful and if
the int criteria weren't exactly the same and they were recruiting
a, a narrower subset was adrenal.
Neutral because it was including the people that just looked like the
French in the APROCCHSS trial, but also including other patients that
in whom there was no benefit and one canceled the other, if that makes sense.
Now, some of this is published, some of this is not published, but people have
then tried to find within adrenal the subset of patients that look like the
patients that were in APROCCHSS to see.
Well, is there at least some suggestion?
Uh, but thus far, no one can really, no one can find within the adrenal trial
a subset that looked like APROCCHSS who then have a similar effect to APROCCHSS
So people are confused along the way.
Uh, the French also did another trial called Cape Cod that looked
quite like the APROCCHSS trial only.
It was just in pneumonia, slightly lower, uh, severity of illness group.
There again, they showed fantastic benefit.
So every trial that's run with flu drug, cortisone based
outta France has been positive.
There's a lot of ity there.
I don't know.
I don't know whether it's about.
Being French or if it's about giving food, cortisone, no one has tested
food cortisone outside France.
Uh, and every time they test it, it's been beneficial.
Um, and then the French then also went back to approach, um, and looked at
the patients in approach the subset of sepsis patients who had pneumonia.
And there too, they found most of the signal in APROCCHSS appeared
to be in the pneumonia patients.
So, uh, Cape Cod was positive APROCCHSS was a positive and APROCCHSS was
most positive within the patients that had sepsis and pneumonia.
So this definitely.
I mean, if you are in Fran, if there was only the French literature, this
would all be tied up with a bow on it, which is you would give steroids
broadly to hospitalized pneumonia and broadly to sepsis, and you would expect
the largest benefit in the patients who had sepsis due to pneumonia.
And we would sort of wipe our hands and go on to one of the many other
unresolved questions in critical care.
Uh, but the problem is we cannot generate an evidence base outside
France that goes in the same direction.
There are some other pneumonia trials with non mortal endpoints that
are somewhat beneficial, but, uh.
Most of the positive signal is driven by these French trials, and so even
when you look at meta-analysis, I would say most of that is being
driven by the French experience.
The French are now leading another huge consortium, uh, to study sep
uh, steroids, particularly in sepsis.
But again, many patients who have sepsis due to the mor, I forget
the acronym for it, but it's just been funded by the European Union
and it's been led by Jali Nan.
And one of the things they're going to be trying to tease out
is, uh, well, let me step back.
So.
I've somewhat jokingly said there's the French experience versus the
rest of the world experience, but, but more pertinently.
I think what we can say is we don't get consistent results and across the
different trials, we give the drug slightly differently and we select
the patient slightly differently.
We could also be giving co interventions differently as well.
We also have a slightly different duration of follow up.
You asked earlier on about harm.
Steroids always run the possibility of being beneficial on the short end,
but then giving steroids has some unwanted sequelae that manifests later.
So it could also be the timing and the nature of the primary end part.
So when the evidence base is consistent, consists of variable, variable
drug and dose, variable patient selection and variable endpoint,
and then you don't get a consistent result, you're left saying, okay,
uh, what's driving the inconsistency?
Now, obviously the endpoint is relatively easy to fix.
In all of the different trials, you could collect multiple different
endpoints and then try to measure a common endpoint across all the trials.
So regardless of what was primary, if you could, if you had long
enough and rich enough follow up, you could, you could standardize.
Um, so then let's go back to the two other plausible domains of heterogeneity.
Um, there, there, there are, I should say, before I
Scott Berry: Well, we, we should wait.
Let's touch on, let, let's touch on a little bit.
But then there was C and Cape Cod, for example.
I don't think they enrolled influenza, so they stayed away from, uh, influenza.
But then during COVID recovery, REMAP CAP showed strong benefit of steroids.
For the treatment of hospitalized COVID.
Derek Angus: Yes, yes, yes.
So.
Uh, Hey, it's Friday.
It's Friday afternoon.
What can I say?
I'm not firing on all cylinders.
I should totally have brought that up that prior to COVID, I.
We were a bit on again, off again with steroids and we were moving
increasingly towards, well maybe in the sickest septic shock patients, in
part because of the approach trial.
But we'd got away from giving it more broadly in pneumonia 'cause it
just wasn't a strong enough signal.
Cape.
The, the non COVID Cape Cod hadn't yet been published in so on.
However, then during COVID, which is bad pneumonia, suddenly it looked
like steroids were beneficial.
And in the Dexamethasone experience and recovery, the, the benefit
was all in the sicker patients.
Uh, which again, that just almost reinforces that it's not
just in bacterial pneumonia.
It may be in general that, that they, this sort of.
The severe end of this syndromic experience of getting threatened lung
function from some invading body that then could be complicated by multiple organ
failure sepsis, regardless of whether that's driven by virus or by bacteria.
It seems.
Steroids are really doing something.
People also thought one of the advantages of COVID is that it's, it's a much more
homogenous insult that in some of the other trials you can't, sometimes you
can't even work out, is it pneumonia?
Like a ton of pneumonia, classic pneumonia trials.
Many patients have no identified organism, and so one of the longstanding things
was, well, you didn't even put the right patients in the trial, whereas.
The COVID trials people said, well now we, these patients are in hospital
for a pathogen driven pneumonia.
They're not just in hospital for a pneumonia complex.
That might sometimes actually just be heart failure that looks on
the chest X-ray, like pneumonia.
And so that, and so then people sort of said, well, you definitely
want to give it in COID and what's more, when we have a true.
A classically selected population in whom we have very high confidence that
they definitely have an infectious pneumonia and especially when they're
severely ill and getting altered, you know, distal organ dysfunction,
IE sepsis due to the pneumonia.
We seem to get this pretty strong homogenous sense of benefit from steroids.
That's one argument.
Uh, there are definitely.
Critics of these trials.
But for those who like them, they would say, not only should you give steroids in
COVID, but while we're at it, it's making us increasingly think maybe steroids.
You know, if you like peanuts, you'll love peanut butter.
And so now the guidelines are, they've totally swung to be very positive.
Current guidelines.
They all.
Differ slightly in who they say you should give it to, and even which kind of drug
you should give, whether you should give hydrocortisone or prednisone, et cetera.
But I would say in general, guidelines from the different infectious disease
and cial care societies, the WHO, et cetera, have all started to become much
more, they're, they're all down on, they're all pretty positive on steroids.
Scott Berry: so we then we were involved in remap cap in non COVI,
where we hydrocortisone and we were looking at, uh, stratifications of
patients by shock, by influenza.
Yes.
No.
And that result showed.
Reasonable probability of harm.
It showed worse mortality in all four of those groups.
We didn't have a whole lot of influenza, but, uh, it, it was
pretty convincingly negative, 90% probability of harm in, in cap, uh,
shock or not, uh, patience, again, a differential result to all of that.
Derek Angus: Yeah, I hated that result.
Um, that was incredibly disappointing.
Uh, it,
so
I, I think we would agree that.
That trial didn't run quite as well as we would've hoped.
Um, it was an open label trial and we had worked with site who said that they
have enough equip poison that if the patient is randomized to control, they
would be happy not to give steroids.
But there was bleeding of steroid use into the control arm.
It wasn't massive, but you never know whether.
You know, if it, if it's, if it's random bleeding, it
probably doesn't matter too much.
It's just slightly weakening any efficacy signal.
But if the physicians have some magic inside track on knowing just exactly who
they should treat, then you could imagine that any signal you would've found is
getting wiped out by, uh, contamination.
Having said that, I don't think contaminate, so I
wish there hadn't been that.
Steroid use in the control arm, but the steroid use in the
control arm wouldn't explain the, almost hitting the harm signal.
Like, uh, steroid use in the control arm would, if, if steroids would've
normally worked, all it would've done is reduce the size of the benefit.
It wouldn't have flipped it in the wrong direction.
Now obviously you, as you start to get smaller sample size,
some of this is just sort of.
Random error and so forth, but that, you know, it made me slightly uncomfortable.
We also, as you know, we had a.
Uh, there was a transposition error by one of our data vendors early
on in the trial that sent us a data set that had been mislabeled.
We, we, as soon as we found that out, we sort of fixed it all and they're no
longer a data vendor even in the trial.
But, um, that did mean that there was a period when we were probably randomizing.
Uh, with a more distorted sort of response, adaptive the response.
Adaptive randomization triggered a more aggressive randomization
away from what would've been ideal.
And again, the main consequence there was probably, you know, we had several
hundred patients in this analysis, but they weren't evenly distributed.
We would've had more power if it was more of a one-to-one distribution.
Um, and so that again, just introduces another level of uncertainty.
Uh, but, but what I don't want to lose in any of that is while none
of these things were ideal, we were a long way from looking anything
like the Cape Cod experience.
Scott Berry: Yeah.
Derek Angus: In, in other words, no matter how much you subset the trial,
no matter how much you tried to, is, for example, in the early part of the
trial before there was any response, adaptive randomization whatsoever, when
this was just like a straight head to head, traditional one-to-one trial.
Even there, the trend that, uh, the patient's getting steroids, their outcome,
the, the observed outcome rate was worse.
Um.
So is it all just the play of chance?
Uh, possible, but, but to your point, um, uh, uh, the, the probability
statement nonetheless would say that even, even accounting for chance, it's,
it's just highly unlikely that, um, that steroids were strongly beneficial.
Um, and.
I wanna remind people that the adrenal trial with 4,000
patients found no benefit.
So it's not as if remap cap was an outlier.
Um, remap cap in many ways was one more trial consistent with the many trials
that have not actually found a benefit.
Uh, and earlier on you had asked about harm.
Um, there was.
It's like the old one, the big European Cardus trial.
Um, they had thought that patients appeared to get slightly
faster resolution of shock.
And by the way, in remap cap, it looked like the observed rate of cardiovascular
dysfunction appeared to resolve sooner.
And that was also what the adrenal trial found.
That, that the early short term cardiovascular
instability resolved sooner.
But in Cardus, where they had done follow up on secondary infections,
secondary infections, which, you know, steroids can cause, uh, were higher.
There was a higher nosocomial infection rate, uh, in the steroid arm.
And I, it is possible that.
Uh, something was going on in Remap Cap where we might have actually been getting
some of the early cardiovascular benefits, but the net effect for the, for the cohort
in general was that steroids were worse.
And so now we get back to,
Scott Berry: Yeah.
Derek Angus: uh, can you better
Scott Berry: Okay.
So let's let Larry, yeah.
Right.
So one of the things that was sort of striking in the remap cap trial is
that mortality was against steroids, but organ support free days, which
includes mortality, but it includes time to resolution of organ support,
actually was better on the steroid arm.
Despite the fact that mortality was worse, it, it, it has the earmark of
another particular disease that was somewhat striking is the treatment of
acute stroke, where early on in the treatment of acute stroke and the use
of endovascular therapy, they saw this non-proportional effect that they, they,
they certainly improved the number of.
Really good outcomes.
They certainly increase that, but they increased the number of deaths
and it was a sign that there's differential heterogeneous treatment.
In fact, they were probably treating people they should not treat,
and they were causing harm on those, and they were treating some
that they absolutely benefited.
And it took a while before they kind of figured the right patients.
And then they saw huge benefits in that.
So, uh, the, the steroids has this hallmark of.
Heterogeneous effect across the population.
And I know you, you touched on, maybe it's the infection, maybe it's the host,
maybe it's the timing of the disease, maybe it's the steroid themselves,
dexamethasone, prednisone, that, the type of that there's so much here.
So it's got the earmarks of heterogeneity.
You know, should we be running trials that we enroll a thousand patients and
do a single analysis of is it beneficial?
Do we need better trials?
So let's sort of turn it to where do we go and if you could design the right trial,
you seem, you seem to not know which patients, which treatments, which time.
Shock based therapy, uh, a seven day dosing.
You know, where would we go if we could fund the right trial?
What's the right trial design?
Derek Angus: Yeah.
So right before, so I would love to talk about that, but can I just ask,
Scott Berry: Okay.
Derek Angus: just make a clarifying, so.
Uh, when, when you have the organ failure free days, if you died, any effect on
your organ dysfunction is sort of subsumed within the fact fact that you died.
But you did allude to this all being explainable by the right patient.
But it is possible with some of these early versus late outcomes.
That's just the timing of the measurement, even within an individual patient.
That that wouldn't be the case when it's organ failure free days
because if you died you're dead.
But, um, uh, we know in critical care, for example, um, low tidal volumes, which, um,
is a strategy of ventilating the patient in a way very differently from the way
that we did for decades, um, would almost certainly make the patient look bluer.
More hypoxic in the short run because greater expansion of the lungs
actually facilitates gas exchange.
You get better oxygenation, but it turned out that the low tidal volumes absolutely
protected the lung and improved survival.
And so high tidal volumes makes you look better in the long, better in
the short run, but then kills you and.
Scott Berry: Okay.
Derek Angus: and so, and, and with all of these interventions when we
move into these combined endpoints, do so at our peril because they
do tend to rely on conceptual models that could be wrong about.
Whether the intermediate outcome is on the path to greatness.
You know, you, you would think a short term measure to improve organ dysfunction
is surely helping you get out alive.
But the answer is not so fast.
So that's one issue.
But, but, but, but assuming we've got the outcome right, and the problem
is heterogeneous patients and you don't know which ones they are.
Um, I mean, this is a classic issue.
If, if you, if.
Uh, uh, so I feel, I feel like, um, uh, there were always what I would call the,
the, the hope and pray models, which is what everyone did before, which is you
just enroll everyone and hope that you got the right group, or that if there's
a bad group, it's not so bad, it doesn't drag it down and all the rest of it.
And then we wanted to start having smarter trials.
If you have the luxury of doing many sequential trials, you could always do
one large trial, see a group in whom you thought there was a benefit, and then
do a follow up trial in just that group.
Even there, you don't necessarily prove that there's heterogeneity.
You know, that was, I, I remember, I think it was the editorialist, very astutely
pointed out that that huge Jupiter trial in the New England, um, when they were.
Uh, treating acute myocardial infarction patients with some
sort of anti-inflammatory strategy just in those with a high CRP.
The authors then said, oh, look, we, we improved outcome showing the
inflammation hypothesis and so forth in myocardial infarction and, and, and,
and the edit in the editorial that said.
For that to be true, you would've had to have enrolled both the inflamed
and the non-inflamed and showed that the drug worked differentially.
You only showed that in the inflamed that the drug worked, but you haven't
ruled out that the drug could have been working in the other group.
Uh,
if, if, if we don't really have the luxury to do lots of sequential trials,
but want to be learning within one trial.
Then, uh, we, we can do a couple of things.
As you know, in remap cap, we've tended to have these a priority
subgroups, um, that, that you could.
If, if you think you have a rough sense of where the domains of heterogeneity
may lie, uh, across both the axis and where the cut point in the axis
might be, then you could start a trial with some embedded rules to then
effectively, um, uh, enrich over time.
Uh, uh, you can also do it in the other direction.
You could deen enrich you.
You could start with the subgroup that you think it works best in, and
as long as you're getting a benefit, then you could then widen your entry
criteria for a broader set later.
I, I think those kind of trials still rely on confident science or hubris
Scott Berry: Yeah.
Derek Angus: whether you have.
About whether you actually understand the axes of heterogeneity.
Um, and so, and, and so
Scott Berry: Well, well, doesn't, doesn't this now.
Yeah.
Doesn't this now demand a, a, a, a different approach where the, the
amount of people you're caring for in critical care that have sepsis cap,
moderate disease, severe disease, that we enroll a very large, try, a very
large population and try to learn Yes.
No in whom?
Derek Angus: Yes, so, so this is where you, so let's step back for a second.
So.
We're, first of all, you would say we are committed to cause and effect.
We're not really interested in association.
Uh, we want to know if something works, but what we've done is
we've begin and RCT tells you if something works, but it doesn't.
But it's like causality with a small C, causality with a
big C is, why does it work?
And in a way, heterogeneity of treatment effect is more
juda perel esque, if you like.
You know, it's more getting at not just explaining if the drug
worked, but why the drug worked.
In fact, I think it was one of Judah Pearl's, uh, postdocs, um, Barne who,
Elias Barnum I think, who started using Judah Pearl's sort of, um.
Causal inference language to try to understand generalizability in RCTs.
If you understood why, uh, effectively, if you understood all of the moderating
and median effects between the covariate structure of who was enrolled and
what the intervention was, then in a way from one trial you could predict.
Exactly what the outcome would be in another trial, even if it was a
slightly different patient population.
You tell me the admixture of patients, and I will tell you the likely mean effect.
What, what you're effectively doing is you're saying, I understand the
relationship between the baseline variables and the, and the randomly
exposed intervention, and I'll tell you what the effect will be.
Um.
The problem here is you quite quickly run outta sample size.
Um,
and, and I mean, there's no, there's no real magic around.
I mean,
in the ideal world, you could imagine every time.
You were faced with a clinical decision around which there was
reasonable uncertainty, then you would almost want to be, uh,
randomizing among the best options.
Uh, so if the entire trial experience was like a huge reinforcement learning
problem where you were sitting in some markoff state where you had a current.
Of the world where when a patient came into a particular state, based on the
cova structure among all the different actions that could be taken, you
would have, you know, a probability.
Uh, of the eventual best outcome rank, order for all the different options
and of the 30 treatments you could have given, it turns out that there's two
or three that all look reasonable, but none of, but they're all quite close.
And then you could say, okay, every time I see this, it's, it's reasonable
to randomize to any of those two or three options, but it's unreasonable
to randomize to the other 27.
Uh, and then you could imagine if hundreds and hundreds and hundreds of ICUs, for
example, were all contributing to this ongoing reinforcement learning model,
that this state could be a known state for the, the agent, the agent being the, the
collective body watching the accumulation of knowledge until say, several
hundred patients have gone through.
And, and then at then at that point you could update the, the probabilities and
it would turn out, oh, remember how we said there were three choices that looked
reasonable when you were in this state?
Turns out one of them is a dud and now we're down to two.
So you would, you would essentially, this is now you'd move into a new
state where you had sort of updated probabilities and you would run that,
and you could imagine that that's really.
You, you know, this notion of these freestanding RCTs
Scott Berry: Yeah.
Derek Angus: that give these single estimates of a patient population
with an average effect, uh, for a single drug at a single dose that
seems is a total misrepresentation of the degree of uncertainty in which we
exist, and it's failing to leverage.
All of the uncertain clinical decisions that are made in regular care.
And so in a perfect world then I am massively leaping over all sorts of
issues about research versus clinical care, the Belmont Report, et cetera.
But, but a, assuming you had the right ethical framework for this, uh, assuming
that everyone was read in on this, that, that they all approved, you could imagine.
A large federated set of learning health systems where all of their electronic
records are all sort of wired together and you have sort of some sort of
asynchronous API that could sort of read, write commands that was able to stream
the information so that just at the moment that the clinician was encountering
the patient and faced with uncertainty.
You could then pull from the data.
Aha.
I'll tell what this moment looks like is it's a patient with the following
baseline characteristics about whom we have uncertainty on the right option.
And so that's like a mini part.
It's like a mini randomization question.
Um, and then if people understood.
Yes, there's legitimate uncertainty and it's, it's within the bounds of
reasonableness to randomly choose, uh, what treatment they take or even
to randomly make a recommendation.
I mean, you could still, if you want, you could still say the
patient and the physician are allowed to override the recommendation.
Um, and if they did that a lot, then the trial is getting contaminated.
But if it turns out that they say, oh, that's, that's not
an unreasonable suggestion.
We're both happy with that.
If that was happening 99% of the time, then effectively you're
running these huge, large, embedded, ongoing randomizations.
And if it turns out that among all of pneumonia and sepsis, there are
a thousand different phenotypes.
Um, all with d and, and some of them cluster into net benefit, some of the
net, you know, net harm, et cetera.
Um, we could be learning about them over time if you, I mean,
these diseases are common anyways.
Have I got way too far out over my skis?
Uh.
Scott Berry: well, well, no.
Let's, uh, let's see where you got, you got that.
Currently, right now, we're not learning from any of these patients.
They're being treated.
They're, they're, they're, and so now we're gonna tie this all together.
We're going to embed randomization.
And we're going to favor randomization of treatments that are working.
So we're gonna care for those patients better.
But it would give us the possibility of learning about heterogeneous treatment
effects, the, the signatures that benefit, uh, uh, this we're gonna enroll.
Thousands of patients in this common disease.
You, you've described a learning healthcare system, and maybe in the
world of ai, this maybe the thought is, okay, AI is now deciding what to give.
It's learning from it, it's reinforcing it.
I mean, this is, this is the future and we kind of wonder why it's not now, but
the, uh, I, I love where you've gotten.
Uh, so we've gotten from, uh, these randomized trials that are showing
differential effect from steroids to a learning healthcare system.
And I want to come back and talk about that because I think
that's more than just a idea.
Uh, I think we, we have some really.
Uh, great hopes of embedded randomization learning healthcare
system, call it ai, call it modeling.
Uh, underneath that is, is pretty exciting.
So, Derek, I, I, I'm, I'm thrilled that you were able to
join us here in the interim.
Um, we do lots of interim analysis here.
Uh, so we're gonna, we're gonna have you back.
Uh, to talk about this, to talk about learning healthcare systems
and maybe we'll have resolution about steroids in sepsis and cap
Derek Angus: We can only hope.
Scott Berry: we go.
Alright, so thank you Derek for joining us here in the interim.
Derek Angus: thank you so much, Scott.