Circulation January 11, 2022 Issue

This week's Feature is a Panel Discussion. Circulation
invited the Young Investigator Finalists who had a Simultaneous
Publication for AHA's 2021 Sessions. Please join authors Amgad
Mentias, Matthew Burrage, Shaan Khurshid, Sevedeh Maram Zekavat,
and Neel Butala as they discuss their articles.


Dr. Greg Hundley:


Welcome listeners to this very special January 11th issue of
Circulation on the Run. And I'm going to tell you why it's
special. I'm Dr. Greg Hundley, director of the Pauley Heart
Center at VCU Health in Richmond, Virginia and also associate
editor at Circulation. Why is this issue special? Because we have
the opportunity to speak with finalists for several of the awards
that were presented or that these investigators presented for at
the American Heart Association sessions. And so we have with us
today, five early stage investigators and we are going to hear
about each of their research projects and the manuscripts that
are coming out and are published in this issue. Want to welcome
all five of you and we'll introduce one at a time as we work
through their research projects. And the first is Dr. Amgad
Mentias from Cleveland Clinic and he was in the session competing
for the Elizabeth Barrett-Connor Research Award for early career
investigators in training. Welcome Amgad. And we'll start with
you. Can you tell us a little bit about the background for your
study and what was the hypothesis that you wanted to address?


Dr. Amgad Mentias:


Hi, Dr. Hundley. How are you? Thank you so much for inviting me
today. A little background, we know that community economic
distress affects outcomes in patients with heart failure. It
actually affects both short term and long-term outcomes. What was
not studied on a nation level before is how is that impact
different or if it's actually different between different races.
In White patients and Black patients and Hispanic patients, what
would be the differentiated effect of community economic distress
on their short and long-term outcomes with heart failure after a
heart failure admission?


Dr. Greg Hundley:


Absolutely. And so that hypothesis that you were going to
address, what were you hypothesizing here?


Dr. Amgad Mentias:


We were hypothesizing that each race has probably some shared
risk factors but also some specific risk factors. We were
hypothesizing that the community level economic distress effect
on heart failure outcomes is not homogeneous or not exactly the
same between all races.


Dr. Greg Hundley:


Very nice. And so what was your study design? And describe a
little bit for us the study population.


Dr. Amgad Mentias:


We included the Medicare population. We included patients who
were admitted with a primary diagnosis of heart failure from the
years 2014 till 2019. We included patients from Black and White
and Hispanic races or ethnicities. And we only included the first
admission for a patient if the patient was admitted more than
once during these years. That was the study population. And we
had about 1.6 million White patients and about 205,000 Black
patients and around 89,000 Hispanic patients.


Dr. Greg Hundley:


Great. And so was this a cohort design?


Dr. Amgad Mentias:


Yes, it was a cohort design. The study start date was when they
were admitted to the hospital for our mortality outcome and when
they were discharged from the hospital for readmission outcomes.
And then we followed them in time up to one year.


Dr. Greg Hundley:


Very nice. And so tell us, what did you find?


Dr. Amgad Mentias:


Our primary exposure of interest, like we said, was economic
distress and race and our primary outcome was three things,
mortality, the second thing is readmission burden, which is
number of admissions over time the patient is alive and third
thing is home time how many days the patient spends at home, out
of the hospital and out of skilled nursing facilities and LTACs.
And we looked at these outcomes at three different points, at 30
days, at six months and at one year follow up. Initially we did
an interaction and that is to see whether our hypothesis, that
the effect is different or not between a race and term and the
economic distress term. And the interaction was significant, in
all three outcomes. Then we went deeper and we started to study
each race separately and see how economic distress affects their
outcomes. We defined economic distress by distress score called
the distressed community index, which is a composite measure of
seven things, including education of the people in the zip code,
unemployment, poverty rate and income in the zip code compared to
the state level and that stuff.


Dr. Amgad Mentias:


We actually found that in White patients, economic distress was
actually associated with adverse outcomes in the short and long
term. In Black patients, it was affecting the outcomes more
robust and more evident in the long term, not in the short term.
We also found the geographic location and their approximate
location, whether it's urban or rural, residential zip code also
affected outcomes. We found that in all races being in a rural
distressed community had the highest posterity and the highest
admission burden and the worst home time compared to other
communities. In fact, people in distressed urban communities had
comparable outcomes. People in rural, non-distressed communities
had comparable outcomes to urban distressed. We found that the
rural location and approximate location near resources affected
outcomes in all races but specifically also in Black patients.


Dr. Greg Hundley:


Very nice. And how do you put your results in context with others
that are doing research in this area?


Dr. Amgad Mentias:


We show that the interplay between economic distress and societal
factors and different things for heart failure is very complex
and there is a complex interplay between different factors. I
think it's very important for health policies that targeting
improvements in community with economic distress and access to
care, they are key to improving outcomes and reducing racial
disparities among patients with heart failure.


Dr. Greg Hundley:


Beautiful. Well, Amgad, we want to congratulate you on this just
excellent work in identifying associations between community
level economic distress and risk of adverse outcomes across
different race ethnic groups. Congratulations to you.


Dr. Greg Hundley:


Well listeners, next we're going to turn to Matthew Burrage from
University of Oxford. And Matt was a finalist for the Melvin
Judkins Early Career Clinical Investigator Award. Matt, just like
with Amgad, could you tell us a little bit about the background
that went into your research project? And what was the hypothesis
that you wanted to address?


Dr. Matthew Burrage:


Yeah, certainly. And thank you very much for the invitation to
take part in this discussion. Really it's a pleasure to be here.
The inspiration for this study was really driven by the
difficulties that we've been having in trying phenotype heart
failure with preserved ejection fraction or HFpEF, given that
until very recently, there were really no therapeutic agents that
have significantly improved outcomes for this population. This is
despite the fact that around half of all heart failure is
classified as HFpEF. And so the thought is that this is a very
heterogeneous population but when you dig down into the
physiology, there seems to be a central mechanism which involves
impairment of myocardial relaxation and in a subsequent rise in
intracardiac filling pressures. And this is something that's
often unasked by exercise and typically this then results in
pulmonary congestion and symptoms of breathlessness. And so some
recent translational studies suggests that abnormal cardiac
mitochondrial function and energetics may be a unifying feature
in the pathogenesis of HFpEF.


Dr. Matthew Burrage:


And given that we know myocardial contraction is dependent on
cardiac energy metabolism and that diastolic relaxation is even
more energy dependent, we hypothesized that impairment of
myocardial energetics may underpin a lot of the physiological
changes in the heart that occur during exercise and thus could
potentially present a metabolic basis that underlies symptoms in
patients with HFpEF, with the hope that this could then lead to
new translational drug targets for HFpEF in the future. But then
alongside this as well, there's been some really pivotal work on
pulmonary congestion during exercise in HFpEF that's been led by
Barry Borlaug's group at the Mayo Clinic as the main determinant
of patients' symptoms. This has been very well validated against
invasive hemodynamics. The second component of our study was to
see if we could noninvasively assess pulmonary congestion during
exercise and HFpEF. And so to do this, we developed and
implemented a new MRI sequence that could quantitatively assess
changes in lung water.


Dr Greg Hundley:


Very nice. How did you address the hypothesis in terms of your
study design and your methodology?


Dr. Matthew Burrage:


This was a prospective study that followed essentially a basket
trial design, where we recruited four distinct groups of
participants that were felt to really encompass the spectrum of
worsening diastolic dysfunction in HFpEF, which was based on
clinical scoring systems, blood biomarkers and echocardiography.
We recruited 43 participants split across this group and so we
had a cohort of age matched, healthy controls. We had patients
with cardiometabolic risk factors for HFpEF like diabetes and
obesity who were included essentially if you think of it like a
pre-HFpEF group, patients with carefully clinically phenotyped
HFpEF and then a cohort of patients with cardiac amyloidosis. And
the amyloid group was recruited really as a positive control,
that the proof of principle lung imaging sequences, as the
presence of restrictive physiology in those patients meant they
would be the group that would be far most likely to develop
pulmonary congestion during exercise.


Dr. Matthew Burrage:


And so each participant underwent blood sampling, a targeted
echocardiogram, they had magnetic resonance spectroscopy to
assess myocardial energetics and cardiac metabolism. We do this
by measuring the phosphocreatine to ATP ratio and also a
cardiopulmonary exercise MRI. The exercise protocol for the study
was a fixed low intensity workload of 20 Watts for six minutes
with the patient supine within the MRI scanner using an
ergometer. And then during exercise, we did whole heart free
breathing cine stacks to assess cardiac volumes at rest and
exercise, as well as performing our custom proton density
weighted lung imaging sequence to look at changes in pulmonary
congestion. And if you're interested, the whole rest and stress
protocol together can be done within about 15 minutes.


Dr. Greg Hundley:


Wow. Boy, very exciting. Exercise during an MRI scan. Matt, we're
very anxious to hear, what did you find?


Dr. Matthew Burrage:


The key findings from this study is that there really is a clear
gradient myocardial energetic impairment that exists across the
spectrum of diastolic dysfunction and HFpEF phenotypes of
increasing clinical severity and worsening diastolic function.
And this gradient of impaired myocardial energetics was
associated with progressively abnormal exercise responses
compared to normal physiology in the age matched controls. And so
a greater degree of energetic deficit was linked to impaired left
ventricular systolic and diastolic functional reserve. It was
also linked to altered right ventricular reserve and abnormal
RV-PA coupling and also to exercise induced pulmonary congestion.


Dr. Matthew Burrage:


And we also showed that the pulmonary congestion or changes in
lung water could be quantitatively assessed using our new proton
density lung imaging sequence and that there is a subgroup of
patients with HFpEF who do demonstrate transient pulmonary
congestion during exercise that we can assess noninvasively.
Overall, the findings suggest a pathway where impaired energetics
are linked to patient symptoms and they do this by limiting
cardiac reserve during exercise and promoting pulmonary
congestion. There seems to be a really important role of resting
cardiac energetics in signaling the abnormal ability of the heart
to perform high energy consuming processes like active diastolic
relaxation and augmentation of contractility and then this leads
of course to the downstream effects that we see.


Dr. Greg Hundley:


Very nice. And you were able to even also observe the lung water.
It sounds like, help us put this in context for our listeners of
how do your results really advance some of the understanding of
the pathophysiology of heart failure with preserved ejection
fraction?


 


Dr. Matthew Burrage:


I think one of the key impacts of this study is the fact that the
heterogeneity of clinical HFpEF syndromes has been such a major
challenge to efforts to develop new therapies to improve symptoms
and prognosis in these patients. Pathophysiological phenotyping
may represent an important step towards targeting the right
therapies to the right patients and specifically targeting
myocardial energy metabolism may be a promising therapeutic
strategy to improve cardiac reserve and potentially reduce
pulmonary congestion in patients with HFpEF. And this really
builds on all the translational studies that exist today and have
gone before it.


Dr. Matthew Burrage:


Hopefully the mechanistic insights that we get from this could
lead to some new translational drug targets, which can be tested
against myocardial energetics in patients to see if this
metabolic substrate is modifiable. And if this then leads to
improvements in symptoms and outcomes. The second aspect very
quickly, relates to the evaluation of patients with
breathlessness, particularly because invasive hemodynamic
assessments may not be possible in all patients who have
breathlessness on exertion. The lung water imaging represents a
potentially new diagnostic tool that can help to differentiate
HFpEF from other causes of dyspnea and I think this is some
something that may have a lot of direct clinical applications for
patient diagnostics for a wide range of conditions in future.


Dr. Greg Hundley:


Very nice. Well, thanks so much for what outstanding work,
identifying this myocardial energetic deficit and then linking
that to both cardiac performance, as well as the development of
pulmonary congestion.


Dr. Greg Hundley:


Well listeners, we are going to switch to our third author today,
Dr. Shaan Khurshid from Mass General and Shaan was a finalist for
the Samuel A. Levine Early Career Investigator Award. And so,
Shaan, can you give us a little bit of the background information
pertaining to your study? And what was the hypothesis that you
wanted to address?


Dr. Shaan Khurshid:


Thanks for having me, it's a pleasure to be here. A little bit of
background that predicting the risk of atrial fibrillation or AF,
may increase the efficiency of AF screening and effectively
prioritize individuals for preventive interventions that are
designed to reduce the risk of incident AF in the first place.
And to that end, risk of AF can be estimated with reasonable
accuracy using clinical factors. We already know that. And for
example, the CHARGE-AF score is a well validated score that been
used in multiple settings. More recently, work suggests that
artificial intelligence or AI enabled analysis of the 12 lead
electrocardiogram can extract latent information that may be
relevant for predicting AF risk. Past models however, have had
some limitations. They've utilized very short time intervals.
They have not incorporated survival time and censoring with is
important for prognostic models. They are kind of a black box and
therefore difficult to interpret and they haven't undergone a
broad external validation.


Dr. Shaan Khurshid:


Therefore, in this current study, we sought to develop a deep
learning model, utilizing the 12-lead ECG to predict risk of
incident AF at five years. We call this model ECG-AI quote
unquote and compared the performance of ECG-AI directly to the
CHARGE-AF clinical risk score that I was mentioning. We also
sought to assess a model that combines both ECG-AI and CHARGE-AF
to each score alone. We hypothesized that the ECG-AI model
utilizing 12-lead ECG could improve the ability to predict five
year AF risk as compared to clinical risk factors alone. And we
felt that such a model may have practical applications,
particularly since wearable devices like smart watches are
increasingly able to provide single lead ECGs.


Dr. Greg Hundley:


Really nice. Sounds like a very interesting application of
artificial intelligence with electrocardiograms in assessing
patients with atrial fibrillation. Can you describe for us your
study population and your study design?


Dr. Shaan Khurshid:


Of course. We trained our models utilizing a retrospective
cohort. The training population was 45,000 individuals receiving
regular primary care at Massachusetts General Hospital or MGH. We
then validated our models in three completely independent
samples, an MGH internal test set, so individuals from MGH but
were not included in training, a separate set of primary care
patients at Brigham and Women's Hospital and the UK Biobank
Prospective Cohort Study in the UK. The total population in which
the models were validated was over 83,000. ECG-AI itself was
trained as a convolutional neural network, which was inputted
with 10 seconds of the 12-lead ECG and utilized a specialized
loss in encoding function that incorporated survival time and
censoring in order to produce a five year risk estimate for each
individual. We trained models utilizing all ECGs available for
each person but evaluated the models utilizing a single ECG
alone. We compared each model, ie. ECG-AI, CH-AI and CHARGE-AF by
incorporating risk estimates into analogous Cox proportional
hazards model so we could compare them apples to apples and
calculated traditional epidemiologic metrics of prognostic model
performance, including discrimination, calibration and
reclassification.


Dr. Greg Hundley:


Very nice. And so what did you find, John?


Dr. Shaan Khurshid:


From our study, we had two major findings. First, the ECG-AI
model consistently discriminated five year AF risk comparably to
the CHARGE-AF 11 component clinical risk score with C statistics
ranging from 0.7 to 0.8. Second, the CH-AI model, which was the
combination of ECG-AI and CHARGE-AF, consistently offered greater
discrimination than either model alone. Both AI models were very
well calibrated across the three test sets with calibration error
consistently less than 1%. The ECG-AI and CHARGE-AF scores were
moderately correlated, suggesting that the AI model is able to
leverage clinical risk factor information extracted from the ECG,
yet also adds something further.


Dr. Shaan Khurshid:


Saliency analyses, which are a method of determining which areas
of the ECG are most relevant for the model's prediction,
highlighted the P-wave and surrounding regions, which provides
important evidence of biologic plausibility in our models.
Importantly, in sub-analyses assessing the AI models, including
only one lead of the 12-lead ECG, we found that model performance
was similar, suggesting that AI models utilizing only single lead
ECGs may also be effective. We also found that the models
performed reasonably well in individuals with prevalent heart
failure and stroke, which are populations in whom AF risk
destination is particularly relevant.


Dr. Greg Hundley:


Very nice. And so clinically, moving forward, how do we put your
results in the context of really where you see this field moving
and how we might use it to identify patients at risk of atrial
fibrillation?


Dr. Shaan Khurshid:


We're excited. Our work we think provides an important
demonstration that ECG-AI based models can utilize a 12-lead ECG
to estimate future risk of AF up to five years. And importantly,
the AI models were generalizable, providing good discrimination
across three large and independent datasets spanning two
continents. We're most excited about this finding that models
perform well when utilizing single lead ECG data alone, which has
important ramifications for wearable devices. In particular, one
could imagine a future application of AI in which a wearable
device is able to not only be used to screen for atrial
fibrillation or AF, but also stratify an individual's risk for AF
utilizing ECG based analysis and therefore potentially
prioritizing that individual for preventive interventions and
also potentially determining how intensely to screen that
individual all in a single closed group.


Dr. Greg Hundley:


Excellent. Wow, Shaan, just beautiful presentation, listeners.
Really discussing how the artificial intelligence assessment of
these EKGs may enable efficient quantification of the future risk
of developing atrial fibrillation.


Dr. Greg Hundley:


Well listeners, we're going to turn now to our fourth presenter,
Maryam Zekavat from the Bird Institute, Yale University. And
Maryam was a finalist for the Genomic and Precision Medicine
Council's Early Career Investigator Award. Welcome Maryam. And
can you describe for us some of the background pertaining to your
study? And what was the hypothesis you wanted to address?


Dr. (Sevedeh)Maryam Zekavat:


Absolutely. And thank you for the invite to be part of this
podcast. The title of the work that we presented and that was
published in Circulation is Deep Learning of the Retina Enables
Phenome and Genome-wide Analyses of the Microvasculature. And so
as a little bit of a background, we know that the
microvasculature has key roles in maintenance of organ health and
that microvascular disease is implicated in conditions across all
organ systems. Here, to study the human microvasculature
noninvasively, we used data across about a 100,000 retinal fundus
photographs. And the purpose of our work was really to address
two main things.


Dr. (Sevedeh)Maryam Zekavat:


First, an unbiased assessment of the phenotypes associated with
the retinal microvasculature had yet to be performed and that
motivated us to ask our first question, namely, what information
can the retinal vasculature provide on future ocular and systemic
disease risk? And then secondly, therapies such as anti-VEGF,
which pharmacologically influence vascular density, are the
mainstay of treatment for multiple ocular conditions, including
wet AMD, proliferative diabetic retinopathy, as well as many
cancers. However, an unbiased screen of genetic targets for other
treatments that may influence the microvascular has yet to be
performed. And so that motivated us to ask for our second
question, namely, what genes influence the retinal vasculature?
And so from there, I'll go to our hypothesis, which was that
analyses of retinal fundus photos may enable an understanding of
the connection between microvascular geometric indices, diseases
and genetics.


Dr. Greg Hundley:


Very nice. And so boy, I heard you had almost a 100,000
participants involved in this study. Tell us a little bit about
your study design and clarify for us, where did you get all these
patients from? What was your study population?


Dr. (Sevedeh)Maryam Zekavat:


Yeah, of course. The study list we utilized the UK Biobank, which
is a cohort of half a million individuals, including over a
100,000 fundus photographs from about 50,000 individuals. We
first implemented deep learning to remove poor quality images and
then to segment out the vasculature fundus photos. And then from
there, we went on to quantify two vascular features, branching
complexity as measured using fractal dimension and also vascular
density. And lastly, we performed phenome and genome-wide
association studies to understand how these vascular geometric
indices influenced disease risk and what genetic factors
influence the vasculature.


Dr. Greg Hundley:


Excellent. And tell us, what did you find?


Dr. (Sevedeh)Maryam Zekavat:


Yeah. First using deep learning, we were able to successfully
perform image quality control and vessel segmentation to extract
two geometric features of the retinal vasculature. Next through
phenome-wide analyses, we identified that lower retinal vascular
fractal dimension and density were significantly associated with
higher risk for incident mortality, as well as cardiometabolic
conditions, including hypertension and type 2 diabetes, heart
failure and renal failure among others. And also multiple
incident ocular conditions, including future risk of retinal
detachment. Thirdly, genome-wide association of these two
geometric indices identified seven and 13 novel loci associated
with vascular fractal dimension and vascular density
respectively. And these were enriched in pathways linked to
angiogenesis, such as VEGF, angiopoietin and WNT signaling
pathways, as well as inflammation via interleukin and cytokine
signaling. And then fourth, through Mendelian randomization for
genetic causal inference analysis, we identified that a genetic
risk for hypertension and type 2 diabetes is associated with
lower microvascular density and that a genetic risk for lower
microvascular density is associated with increased risk of
retinal detachment.


Dr. Greg Hundley:


Wow. Really interesting. The intersection of this beautiful
phenotype characterization of the retina with this genetic
information. Where do you see this research moving forward in the
future?


Dr. (Sevedeh)Maryam Zekavat:


Yeah, so clinically these findings may support the use of retinal
microvascular indices for risk prediction and disease monitoring
of systemic and ocular conditions. And of course, further
assessment of the identified biological pathways influencing the
microvasculature can potentially lead to therapies for not only
retinopathies but also other conditions linked to microvascular
disease, including oncologic, renal and cardiovascular
conditions. And more broadly, our results illustrate the
potential for using deep learning on retinal imaging to
understand the microvasculature with wide applications across
diseases. And of course, more research is needed to evaluate the
added benefit, in addition to existing clinical predictors and
the feasibility for incorporation into clinical workflows.


Dr. Greg Hundley:


Just beautiful. And thank you so much Maryam and for highlighting
for us, the results of your study, indicating that the retinal
vasculature may serve as a biomarker for future cardiometabolic
and ocular disease and provide insights on genes and biological
pathways that influence microvascular indices.


Dr. Greg Hundley:


Well listeners, now we are going to turn to our last speaker
today and it's Dr. Neel Butala from Mass General, Beth Israel.
And he also was a finalist for the Samuel A. Levine Early Career
Investigator Award. Welcome, Neel. And could you describe for us
the background pertaining to your study and what hypothesis did
you want to address?


Dr. Neel Butala:


I appreciate the opportunity to be here and chat with you. And so
there's conflicting evidence on the optimal duration of dual
anti-platelet therapy, which is DAPT, after drug eluting stent
implantation. Older studies, such as the DAPT study show fewer
ischemic events with more bleeding, with longer DAPT duration and
our site and the guidelines for DAPT duration after PCI but newer
studies show similar ischemic events and actually less bleeding
with shorter DAPT, even among those with high ischemic risk at
baseline. We wondered whether the DAPT study, which is the only
study powered to detect ischemic endpoints and still influences a
major cardiovascular guidelines still applies to contemporary
practice.


Dr. Neel Butala:


And so we asked two key questions. Now, number one is a US
contemporary real world population of patients receiving PCI,
different from the DAPT trial population. And again, the DAPT
trial enrolled between 2009 and 2011. And here we hypothesized
that the populations are probably a little different. And number
two, we asked how would trial treatment effects change if a real
world population had been enrolled instead? And here we
hypothesized that perhaps the ischemic benefit of longer DAPT
would actually go away, would be similar to the newer trials that
have been done.


Dr. Greg Hundley:


Very nice. And describe for us your study design and your study
population.


Dr. Neel Butala:


Yeah. We compared characteristics between DAPT study patients,
with those of a more contemporary real world cohort of NCDR cath
PCI registry patients. And to do this, we used novel
transportability methods to really create a propensity score
model to predict an individual's likelihood of trial
participation based on patient characteristics. And this type of
propensity score model actually gives us inverse probability
weights, which we used to reweight the DAPT study patients based
on the distribution of characteristics in the real world
patients. The intuition here is really to up weight the trial
patients with characteristics more common in the real world and
down weight trial patients with characteristics less common in
the real world. We then compared treatment effects in the DAPT
study patients with those of the reweighted DAPT study patients
to understand whether DAPT study results would change if a real
world population had been enrolled instead.


Dr. Greg Hundley:


Very nice. And what were the results associated with these
comparisons?


Dr. Neel Butala:


First, we found that trial and real world populations were
different. We found that the contemporary real world population
was older, less likely to be White, more likely to have
comorbidities and then more likely to present with ACS.
Additionally, nearly a 100% of real world patients received a
second generation drug eluting stent versus only 58% of trial
patients. And then these differences led to differences in the
estimated average treatment effects of DAPT, to the results of
the DAPT study. And the real world treatment effect in reducing
ischemic endpoints is actually no longer present but the increase
in bleeding persisted. And so we found that the average top line
treatment effect in the DAPT study may not be applicable to
contemporary practice. We did find however, the DAPT score in the
subgroup did still identify subsets of patients who may benefit
from prolonged DAPT beyond one year after PCI in the contemporary
population.


Dr. Greg Hundley:


Very nice. And so clinically as we're managing patients, how
would we interpret your results and help us with clinical
management today?


Dr. Neel Butala:


Yeah, great question. These results really harmonize the DAPT
trial with results of newer clinical trials of DAPT duration,
which all demonstrate the safety of shorter duration DAPT to
reduce bleeding risk and these results more broadly illustrate
the importance, the nuance interpretation of clinical trials to
guide clinical decision making and really highlight the risk of
simply applying the top line trial results to all patients in
contemporary practice, even beyond the study. These results
emphasize the importance of accounting for patient specific
factors and leveraging risk scores when available in deciding how
clinical trials results actually apply to a particular patient.
And finally, the results actually illustrate the importance of
continually evaluating the generalizability of cardiovascular
trials to ensure that the guidelines reflect treatment effects in
contemporary clinical practice. And the methods that we use in
the study can actually be used to do this in RCTs more broadly.


Dr. Greg Hundley:


Very nice, Neel. And thank you for really bringing us this study
highlighting the differences between patients and devices used in
contemporary clinical practice compared with those in the DAPT
study. And how they were associated with attenuation of benefits
and greater harms attributable to prolonged DAPT duration.


Dr. Greg Hundley:


Well listeners, what an exciting day. Getting to see these papers
in print and also have these early stage investigators from the
2021 sessions that were finalists in many of these competitions,
we're so grateful to Amgad Mentias, Matt Burrage, Shaan Khurshid,
Maryam Zekavat and Neel Butala for their time today.


Dr. Greg Hundley:


On behalf of Carolyn and myself, we want to wish you a great week
and we will catch you next week on the run.


Dr. Greg Hundley:


This program is copyright of the American Heart Association,
2022. The opinions expressed by speakers in this podcast are
their own and not necessarily those of the editors or of the
American Heart Association. For more, please visit
ahajournals.org.