Circulation September 10, 2019 Issue

 


Dr. Carolyn Lam: Welcome to Circulation on the Run, your weekly
podcast summary and backstage pass to the journal and its
editors. We're your cohosts. I'm Dr. Carolyn Lam, Associate
Editor from the National Heart Center, and Duke National
University of Singapore.


Dr. Greg Hundley: And I'm Greg Hundley, associate editor from the
Poly Heart Center at VCU health in Richmond, Virginia.


Dr. Carolyn Lam: Greg, I'm so excited about the feature paper
this week. You know it deals with machine learning. It's such a
hot topic now, and this one particularly deals with machine
learning and the prediction of the likelihood of an acute
myocardial infarction. So everyone's going to want to listen to
it. Let's discuss a couple of papers and get to it, shall we?


Dr. Greg Hundley: Absolutely Carolyn, would you like to go first?


Dr. Carolyn Lam: I sure would. So my first pick is the first
study to investigate the overall importance of translational
regulatory networks in myocardial fibrosis. This is the study
from doctors Rackham and Cook from Duke NUS Medical School here
in Singapore.


Dr. Carolyn Lam: What they did is they generated nucleotide
resolution translatome data during transforming growth factor
beta one, or TGF beta one-driven cellular transition of human
cardiac fibroblasts to myofibroblasts. So this technique
identified the dynamic changes of RNA transcription and
translation at several time points during the fibrotic response,
revealing transient and early responder genes.


Dr. Carolyn Lam: Now, very remarkably about one third of all the
changes in gene expression in activated fibroblasts was subject
to translational regulation and dynamic variation in the ribosome
occupancy, affected protein abundance independent of RNA levels.
Ribosome occupancy in the hearts of patients with dilated
cardiomyopathy suggest that the same post-transcriptional
regulatory network, which was underlying cardiac fibrosis. Now
key network hubs included RNA binding proteins such as PUM2 and
QKI that worked in concert to regulate the translation of target
transcripts in the human disease hearts.


Dr. Carolyn Lam: Furthermore, the authors showed that silencing
of both PUM2 and QKI inhibited the transition of fibroblasts
towards profibrotic myofibroblast in response to TGF beta one.


Dr. Greg Hundley: You know, Carolyn, this whole aspect of
fibroblasts and how they turn on and turn off, become
myofibroblasts, such a hot topic in heart failure. What are the
clinical implications of this work?


Dr. Carolyn Lam: Yes, I agree. Well, threefold. First, these
authors identified previously unappreciated genes under
translational control, which could be novel candidates for
disease biology and therapeutic targets.


Dr. Carolyn Lam: Number two, they found that critical fibrosis
factors impacted cellular phenotypes at a protein level only, and
hence these cannot be appreciated using single cell, or bulk RNA
sequencing approaches. So that was significant. Finally, RNA
binding proteins was shown to be central to the fibrotic response
and represent unexplored gene expression regulators, and of
course potential diagnostic or therapeutic targets.


Dr. Greg Hundley: Very nice Carolyn. Well, my next paper is also
from the world of basic science, and it comes from Dr. Joseph
Hill. Have we ever heard of him? Well of course, he's our Editor
in Chief. He's going to discuss, he and his team investigated
Polycycstin-1. Well, what is Polycycstin-1? It's a trans membrane
protein, originally identified in autosomal dominant polycystic
kidney disease, where it regulates the calcium permeate cation
channel polycystin-2. So autosomal dominant, polycystic kidney
disease patients develop renal failure, hypertension, left
ventricular hypertrophy, atrial fibrillation and other
cardiovascular disorders. These individuals harbor PC1 loss of
function mutations in their cardiomyocytes, but the functional
consequences of this are relatively unknown.


Dr. Greg Hundley: Now PC1 is ubiquitously expressed in its
experimental ablation in cardiomyocyte specific knockout mice
reduces contractile function, and in this paper the authors set
out to determine the pathophysiologic role of PC1 in these
cardiomyocytes.


Dr. Carolyn Lam: Huh--very interesting. I liked the way you laid
that out. So what did they find?


Dr. Greg Hundley: What the investigators identified is that PC1
ablation reduced action potential duration in cardiomyocytes.
They decreased calcium transients and therefore myocyte
contractility. PC1 deficient cardiomyocytes manifested a
reduction in sarcoplasmic reticulum calcium stores due to reduced
action potential duration and circa activity, an increase in
outward potassium currents decreased action potential durations
in cardiomyocytes lacking PC1. PC1 coimmunoprecipitated with a
potassium 4.3 channel and modeled PC1 C terminal structure
suggested the existence of two docking sites for PC1 within the
end terminus of K4.3. Supporting a physical interaction between
the cells. Finally, a naturally occurring human mutant PC1
manifested no suppressive effects on this potassium channel
activity. Thus, Carolyn, Dr Hill and colleagues' results help
uncover a role for PC1 in regulating multiple potassium channels,
governing membrane repolarization and alterations in circa that
reduce cardiomyocyte contractility.


Dr. Carolyn Lam: Oh wow. What a bonanza of really interesting
papers in this week. Now my next pick is a secondary analysis of
the reveal trial. It hinges on the hypothesis that was generated
from prior trials that the clinical response to cholesterol ester
transfer protein or CETP inhibitor therapy may differ by ADCY9
genotype. So in the current study, authors Dr. Hopewell and
colleagues from Nuffield Department of Population Health,
University of Oxford examine the impact of ADCY9 genotype on the
response to the CETP inhibitor Anacetrapib within the reveal
trial.


Dr. Greg Hundley: Tell me, I've forgotten a little bit, but can
you remind me a little about what was the reveal trial?


Dr. Carolyn Lam: Yes, of course. So the randomized placebo
controlled reveal trial actually demonstrated the clinical
efficacy of the CETP inhibitor Anacetrapib among more than 30,000
patients with preexisting atherosclerotic vascular disease. Now,
in the current study, among more than 19,000 genotyped
individuals with European ancestry, 13% had a first major
vascular event during four years median follow up. The
proportional reductions in the risk of major vascular events did
not differ significantly by ADCY9 genotype. Furthermore, the
authors showed that there were no associations between the ADCY9
genotype and the proportional reductions in the separate
components of major vascular events, or any meaningful
differences in lipid response to Anacetrapib.


Dr. Carolyn Lam: So in conclusion, the reveal trial being the
single largest study to date to evaluate the ADCY9
pharmacogenetic interaction provided no support for the
hypothesis that ADCY9 genotype is materially relevant to the
clinical effects of the CETP inhibitor Anacetrapib. The ongoing
dal-GenE study, however, will provide direct evidence as to
whether there's any specific pharmacogenetic interaction with
dalcetrapib.


Dr. Greg Hundley: Oh, very good. So we've got some results coming
from dal-GenE.


Dr. Carolyn Lam: Mm.


Dr. Greg Hundley: Well, Carolyn, my last selection relates to a
paper regarding the incidence of atrial fibrillation among those
that exercise, and I mean really exercise.


Dr. Carolyn Lam: Ooh.


Dr. Greg Hundley: So the paper comes from Dr Nicholas Svedberg
from Uppsala University, and studies have revealed a higher
incidence of atrial fibrillation among well trained athletes. The
authors in this study aim to investigate associations of
endurance training with the incidents of atrial fibrillation and
stroke, and to establish potential sex differences of such
associations in this cohort of endurance trained athletes. They
studied all Swedish skiers, so 208,654 that completed one or more
races of the 30 to 90 kilometer cross country skiing event called
the Vasaloppet from 1989 through 2011, and they had a matched
sample of 527,448 non-skiers, and all of the individuals were
followed until their first event of either atrial fibrillation or
stroke.


Dr. Carolyn Lam: Wow. What an interesting and what a big study.
So tell us, what are the results and especially were there any
sex differences?


Dr. Greg Hundley: Well, interesting that you ask about those sex
and gender differences. So female skiers had a lower incidence of
atrial fibrillation than female non-skiers, independent of their
finishing time and the number of races, whereas male skiers had a
similar incidence to that of non-skiers. Second, skiers with the
highest number of races or fastest finishing times had the
highest incidents of the AFib, but skiers of either sex had a
lower incidence of stroke than non-skiers independent of the
number of races and finishing time. Third, skiers with atrial
fibrillation had a higher incidence of stroke than skiers and
non-skiers without atrial fibrillation. That's true for both men
and women. We would think that. Finally after one had been
diagnosed with atrial fibrillation, skiers with atrial
fibrillation had a lower incidence of stroke and a lower
mortality compared to non-skiers with atrial fibrillation.


Dr. Carolyn Lam: Very interesting. Could you sum it up for us?
What's the take home?


Dr. Greg Hundley: Couple things. One, female endurance athletes
appear to be less susceptible to atrial fibrillation than male
endurance athletes. Second, both male and female endurance
athletes have a lower risk of stroke independent of their fitness
level. Third, after the diagnosis of atrial fibrillation,
participants in a long distance skiing event with atrial
fibrillation had a 27% lower risk of stroke and a 43% lower risk
of dying compared to individuals from the general population with
the diagnosis of atrial fibrillation.


Dr. Greg Hundley: So there's some clinical implications. Although
very well trained men have a higher incidence of atrial
fibrillation than less trained men, the incidence is on par with
that of the general population and not related to a higher
incidence of stroke at that group level. This indicates that
exercise has very beneficial effects on other risk factors for
stroke. Then lastly, atrial fibrillation in well trained
individuals should be treated according to our other usual
guidelines for the population at whole.


Dr. Carolyn Lam: Wow. What a fantastic study to end our little
coffee chat on, but it's time to move on to our feature
discussion.


Dr. Carolyn Lam: Today's feature discussion touches on super-hot
topics. First of all, the perennially interesting and hot topic
of the prediction of acute myocardial infarction, or should I say
the more precise predictions that we can do these days. The
second part of the hot topic is machine learning. Oh my goodness.
This is creeping into cardiovascular medicine like never before.
So I'm so glad to welcome to this discussion corresponding author
of the featured paper Professor Nicholas Mills from the
University of Edinburgh, as well as our Associate Editor Doctor
Deborah Diercks from UT Southwestern. So welcome both, and Nick,
if I could start with you, tell us about MI Cubed.


Prof Nicholas Mills: First thing to say, it was a major
international collaboration, involved researchers from over nine
different countries and we got together to develop and test an
innovative algorithm that estimates for individual patients the
probability when they attend the emergency department with acute
chest pain that they may or may not have had a myocardial
infarction.


Prof Nicholas Mills: Machine learning is a really new area in
cardiovascular medicine as you say. Our algorithm called MI Cubed
uses a fairly simple algorithm which is a decision tree. It takes
into consideration really important patient factors such as age,
sex, troponin concentration at presentation, and troponin
concentration on subsequent testing, and the change in troponin
in between those two tests in order to estimate or calculate the
probability of the diagnosis. One of the really interesting
aspects of this is it's not just an algorithm for research, it's
a clinical decision support tool as well. So what we've done is
taken the output from that algorithm and translated it into
something that is meaningful for clinicians. We've kept it quite
simple. It gives an output between zero and a hundred, which is
directly proportional to the likelihood of the patient having a
myocardial infarct. We also provide estimated diagnostic metrics.
So sensitivities and specificities that relate to that individual
patient. It's really going to change the way we think about the
interpretation of cardiac troponin in clinical practice.


Dr. Carolyn Lam: Indeed, and first audience please, please look
up the beautiful figures of this paper. I think it summarizes it
all. The algorithm shows you what MI Cubed is and then compares
it to the ESC three hour algorithm, one hour algorithm. Then I
love the last figure, where you actually show us that very
important component that you just said. As a clinical support
tool, how it's going to work. So we actually have pictures of
your cell phone and showing you the pictures that you're going to
get from it. So super cool. Beautiful paper.


Dr. Carolyn Lam: Now I just have so much to talk about, first the
machine learning bit, always sexy sounding, but a bit scary for
clinicians. So I really like the fact that you broke it down to
actually say what components go in so that people aren't afraid
of this black box. We don't know what's going on. Is there like a
set time between samples, or how does this work? Do you need to
have it within a certain timing? How does that fall in? Is it a
particular type of troponin, what are some of the specs of the
model that a practicing clinician needs to know?


Prof Nicholas Mills: Well, in order to answer that question, I
might explain to you the rationale for developing it. So when
you're assessing a patient in the emergency department, we all
recognize in our daily practice that patients differ. So
interpreting troponin has been challenging. One threshold for all
may not be the right way to approach this really important
clinical diagnosis. Troponin concentrations differ in men and
women. They differ by age, and as a surrogate of the presence of
comorbidities. They differ depending on the timing of when you
take that sample and when you repeat that measurement, and that
has introduced some complexity. So many interesting pathways have
been developed for guidelines which try and apply fixed
thresholds and fixed time points, and it's pretty tough to
deliver in the real world setting of a super busy emergency
department. So the premise for developing this algorithm was we
wanted something that was really flexible, that recognized that
patients are different, they're not all the same.


Prof Nicholas Mills: That's why we went for a machine learned
approach rather than a more conventional statistical model. So
you asked about the specification. You can do your two troponin
tests whenever you like. So I had across the 11,000 patients huge
variation in the timing of samples, but that is okay for MI
Cubed. If you repeat the test within an hour, two hours, three
hours, six hours, it still provides the same diagnostic
performance. I think that's really important.


Prof Nicholas Mills: You also mentioned specification about the
assay. This algorithm has been developed using a particular high
sensitivity cardiac troponin assay developed by Abbott
Diagnostics. It will be effective for other high sensitive
troponin assays, but it's unlikely to be as effective using a
contemporary assay. So if your hospital uses a contemporary or
conventional cardiac troponin assay, this might not be the right
algorithm for you.


Dr. Carolyn Lam: Great. Thank you for breaking down the issue so
beautifully and practically. It really makes me think, oh my
goodness, this paper's just far more than about MI. Because you
know, natriuretic peptides, you could say the same thing. A
prediction of heart failure is the same thing, you know? So the
whole approach is novel. Deb, could you please share your
thoughts and perspectives on where this is going perhaps?


Dr. Deborah Diercks: I think this study is terrific because I
think it does, as Dr. Mills stated, reflect reality. We don't
draw measures at zero, exactly at zero, and exactly at one and
exactly at three, especially in a busy emergency department. So I
think it provides flexibility to the physician and provider in
using it to be able to interpret values in a world that doesn't
fit complete structure like the guidelines are written out. What
I find really interesting about this study, and I'd love to hear
more about, is how you decided the thresholds of where low risk
and high risk were cut at. It mentions by consensus, and I guess
I would have loved to have been a fly on the wall to hear how
those discussions went, and would love to hear more from you Dr.
Mills about that.


Prof Nicholas Mills: Fascinating discussions amongst all the
investigators on this project as to how we would define that. The
first point I would make though is we designed the algorithm to
provide a continuous output, a continuous measure of risk. So
your MI Cubed score is between zero and a hundred. You don't have
to apply a threshold, but we are used to in clinical practice
having processes that support our triage of patients, and
identifying people as low risk and high risk. Therefore we felt
upfront that we should evaluate specific low risk and high risk
thresholds.


Prof Nicholas Mills: So low-risk, we were completely unanimous on
how to define that, and it was based on some really nice work
done by emergency physicians in New Zealand. Martin Fan, who's
the first author on this paper, surveyed many emergency
physicians and asked about their acceptance of risk. They came up
with the concept that an algorithm to be considered safe in
emergency medicine would be acceptable if the sensitivity was
greater than 99% or the negative predictive value was greater
than 99.5%.


Prof Nicholas Mills: So we agreed up front that we would hold our
low risk thresholds to those bars. Those metrics. Where there was
less agreement was how you defined high risk. That didn't
surprise me hugely. The positive predictive value of troponin is
one of the most controversial topics around. Most cardiologists
[crosstalk 00:20:52] of troponin has been difficult for them in
clinical practice because with the improvements in sensitivity we
are seeing lower specificity and lower causative link to value.
If I put it into context, just measuring troponin and using the
99 percentile in consecutive patients gives you a positive
predictive value of around about 45 to 50% in most healthcare
systems for the diagnosis of type one myocardial infarction.
Therein lies the problem. So one in every two patients has an
abnormal troponin result but doesn't have the condition that we
have evidence based treatments for, and whom cardiologists who
are often quite simplistic in their approach to the assessment of
these patients know how to manage.


Prof Nicholas Mills: Every second patient we don't know how to
manage, and therefore we wanted an algorithm that would help us
identify those patients who can go through our often
guideline-based pathways and treatment pathways for acute
coronary syndromes more effectively. We eventually agreed that a
positive predictive value of 75% would be ideal. So three out of
every four patients would have the diagnosis that we knew how to
manage and treat. That was our target. We got pretty close to it
in our test set. I think the actual positive predictive value at
the threshold of around an MI Cubed value of 50 was 72%, so
pretty effective. Certainly a lot better than relying on a kind
of binary threshold such as the 99 percentile to identify high
risk patients.


Dr. Deborah Diercks.: Thanks for that great answer. My next
question is how do you think MI Cubed is going to integrate, or
will it even replace the need for other risk stratification tools
that we often use the emergency departments such as TIMI or the
heart score?


Prof Nicholas Mills: Fabulous question. In this analysis, we
haven't specifically compared the performance of MI Cubed with
TIMI or heart, so my answer is going to be a little speculative.
You can forgive me hopefully. Both those scores were developed
prior to the widespread use of high sensitive cardiac troponin
tests. I think what we've learned since the introduction of high
sensitive cardiac troponin is that we're using this test as a
risk stratification tool, and a lot of the power of the MI Cubed
algorithm comes from the way that it identifies extremely low
risk patients with very low and unchanging cardiac troponin
concentrations way below the diagnostic threshold.


Prof Nicholas Mills: TIMI and heart simply consider troponin as a
binary test, a positive or negative test, and do not take
advantage of the real power of the test to restratify patients.
All the evidence to date that has compared TIMI and heart with
pathways that use high sensitive troponin in this way, both to
restratify and diagnose patients show that these risk tools add
very little in terms of safety, but do make pathways more
conservative. So they identify fewer patients that are lower risk
and permit discharge of those patients.


Prof Nicholas Mills: So my concern about using an algorithm like
MI Cubed with an existing tool like heart is that it will
undermine much of the effectiveness of this tool which identifies
around about two thirds of patients as low risk. If you were to
combine that with a heart score, you would reduce the
effectiveness. I don't think you get a gain in performance, but
further research is required to do a head to head comparison with
these sorts of traditional restratification tools.


Dr. Carolyn Lam: I'm so grateful for this discussion, both Nick
and Deb. In fact, I was about to ask what are the next steps and
I think Nick you just articulated it. Deb, I want to leave the
final words to you. Do you have anything else to add?


Dr. Deborah Diercks: I think this study represents a real change
in how we can practice medicine, where we can actually take our
biomarkers that actually have really strong value and utilize
them in a manner that is pragmatic. It can actually introduce and
take full advantage of them, and so I think this is a great
opportunity for us to rethink our usual approach, which frankly,
especially for troponin has really been very binary and very
static. Thank you so much Dr Mills for the innovation and the
willingness to look into this area.


Dr. Carolyn Lam: Thank you so much. This paper is like a sneak
peak into the future of what we'll be practicing medicine like.
Well, audience, you heard it right here on Circulation on the
Run. Don't forget to tune in again next week.


This program is copyright American Heart Association 2019.