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Circulation on the Run


Mar 29, 2021

For this week's Feature Discussion, please join authors Michael Ackerman, Christopher Haggerty, editorialist Michael Rosenberg, and Associate Editor Nicholas Mills as they discuss the original research articles “Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device,” “ Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead Electrocardiogram and Help Identify Those at Risk of AF-Related Stroke,” and “Trusting Magic: Interpretability of Predictions from Machine Learning Algorithms.”

 

TRANSCRIPT BELOW:

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 doctor 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, director of the Pauley Heart Center at VCU Health in Richmond, Virginia. Well Carolyn, this week's feature, it's kind of a new thing for us. It's more than our double feature; it's actually a forum, where we're going to have two papers discussed, we'll have both authors represented from each of those two papers, we'll have an editorialist, and we'll have one of our associate editors. And the topic, Carolyn, just to keep you in suspense, is really on machine learning and actually how that can be applied to 12 lead electrocardiograms. But before we get to that, how about we grab a cup of coffee and start off on some of the other articles in this issue? Would you like to go first?

Dr. Carolyn Lam:

Yes, I would, but you're really keeping me in suspense. But first, let's focus on health related quality of life. We know that poor quality of life is common in heart failure, but there are few data on heart health related quality of life and its association with mortality outside of the Western countries. Well, until today's paper. And it's from the Global Congestive Heart Failure, or GCHF study, the largest study that has systematically examined health-related quality of life as measured by the Kansas City cardiomyopathy questionnaire 12, or KCCQ, and its association with outcomes in more than 23,000 patients with heart failure across 40 countries, in eight major geographic regions, spanning five continents.

Dr. Greg Hundley:

Wow, Carolyn. That KCCQ 12, that has been such an interesting tool for us to use in patients with heart failure. So what did they find in this study?

Dr. Carolyn Lam:

Really important. So the health-related quality of life differs considerably between geographic regions with markedly lower quality of life related to heart failure in Africa than elsewhere. Quality of life was a strong predictor of death and heart failure hospitalization in all regions, irrespective of symptoms class, and in both preserved and reduced ejection fraction. So there are some important clinical implications, namely that health-related quality of life is an inexpensive and simple prognostic marker that may be useful in characterizing symptom severity and prognosis in patients with heart failure. And there is certainly a need to address disparities that impact quality of life in patients with heart failure in different regions of the world.

Dr. Greg Hundley:

Very nice, Carolyn. Well, I'm going to turn to the world of basic science and bring us a paper from David Merryman from Vanderbilt University. So Carolyn, myocardial infarction induces an intense injury response, which ultimately generates a collagen dominated scar. Cardiac myofibroblasts are the cells tasked with depositing and remodeling collagen and are a prime target to limit the fibrotic process post myocardial infarction. Now Carolyn, serotonin 2B receptor signaling has been shown to be harmful in a variety of cardiopulmonary pathologies, and could play an important role in mediating scar formation after MI. So Carolyn, these investigators employed two pharmacologic antagonists to explore the effect of serotonin 2B receptor inhibition on outcomes post myocardial infarction and characterized the histological and micro structural changes involved in tissue remodeling.

Dr. Carolyn Lam:

Oh, that's very interesting, Greg. What did they find?

Dr. Greg Hundley:

So Carolyn, serotonin 2B receptor antagonism preserved cardiac structure and function by facilitating a less fibrotic scar, indicated in their results by decreased scar thickness and decreased border zone area. Serotonin 2B receptor antagonism resulted in collagen fiber redistribution to a thinner collagen fiber. And they were more anisotropic. They enhanced left ventricular contractility and the fibrotic tissue stiffness was decreased, thereby limiting the hypertrophic response of the uninjured cardiomyocytes.

Dr. Carolyn Lam:

Wow. That is really fascinating, Greg. Summarize it for us.

Dr. Greg Hundley:

Yeah, sure. So this study, Carolyn, suggests that early inhibition of serotonin 2B receptor signaling after myocardial infarction is sufficient to optimize scar formation, resulting in a functional scar, which is less likely to expand beyond the initial infarct and cause long-term remodeling. The prolonged presence of the antagonist was not required to maintain the benefits observed in the early stages after injury, indicating that acute treatment can alter chronic remodeling. So Carolyn, it's really going to be interesting to see how this research question is pursued in studies of larger animals, including us, or human subjects.

Dr. Carolyn Lam:

Wow, that is really interesting. And so is this next paper. Well, we know that genetic variation in coding regions of genes are known to cause inherited cardiomyopathies and heart failure. For example, mutations in MYH7 are a common cause of hypertrophic cardiomyopathy, while mutations in LMNA are a common cause of dilated cardiomyopathy with arrhythmias. Now, to define the contribution of non-coding variations, though, today's authors, led by Dr. Elizabeth McNelly from Northwestern University Feinberg School of Medicine in Chicago and colleagues evaluated the regulatory regions for these two commonly mutated cardiomyopathy genes, namely MYH7 and LMNA.

Dr. Greg Hundley:

Wow, Carolyn. So this is really interesting. So how did they do this and what did they find?

Dr. Carolyn Lam:

You asked the top questions, because the method is just as interesting as the findings here. They used an integrative analysis that relied on more than 20 heart enhancer function and enhancer target datasets to identify MYH7 and LMNA left ventricular enhancer regions. They confirmed the activity of these regions using reporter assay and CRISPR mediated deletion of human cardiomyocytes derived from induced pluripotent STEM cells. These regulatory regions contained sequence variants within transcription factor binding sites that altered enhancer function. Extending the strategy genome-wide, they identified an enhancer modifying variant upstream of MYH7. One specific genetic variant correlated with cardiomyopathy features derived from biobank and electronic health record information, including a more dilated left ventricle over time. So these findings really link non-coding enhancer variation to cardiomyopathy phenotypes, and provide direct evidence of the importance of genetic background. Beautiful paper.

Dr. Greg Hundley:

Very nice, Carolyn.

Dr. Carolyn Lam:

But let me quickly tell you what else is in this issue. We have an ECG Challenge by Dr. Lutz on flash pulmonary edema in a 70-year-old; there's an On My Mind paper by Dr. Halushka, entitled (An) Urgent Need for Studies of the Late Effects of SARS-CoV-2 on the Cardiovascular System.

Dr. Greg Hundley:

Ah, Carolyn. Well, in the mailbox, there are two Research Letters, one from Dr. Soman entitled (The) Prevalence of Atrial Fibrillation and Thromboembolic Risk in Wild-Type Transthyretin Amyloid Cardiomyopathy, and a second letter from Dr. Berger entitled Multiple Biomarker Approaches to Risk Stratification in COVID-19. Well Carolyn, now let's get on to that forum discussion and hear a little bit more about using machine learning in the interpretation of a 12 lead ECG.

Dr. Carolyn Lam:

Wow, can't wait. Thanks, Greg.

Dr. Greg Hundley:

Well listeners, we are here today for a double feature, but this double feature is somewhat unique, in that we are going to discuss together two papers that focus on machine learning applications as they relate to the interpretation of the electrocardiogram. With us today, we have Mike Ackerman from Mayo Clinic, Chris Haggerty from Geisinger, Mike Rosenberg as an editorialist from University of Colorado, and then our own Nick Mills, an associate editor with Circulation. Welcome, gentlemen. Well, Mike Ackerman, we will start with you first. Could you describe for us the hypothesis that you wanted to test, and what was your study population and your study design?

Dr. Michael Ackerman:

Thanks, Greg. The hypothesis was pretty simple, and that is could an artificial intelligence based approach, machine learning, deep neural network, could that solve the QT problem? Which is one of the big secrets among cardiologists, which, as you know, one of your associate editors, Sammy Biskin, published a sobering paper over a decade ago, showing and revealing the secret that cardiologists are not so hot at measuring the QT interval, and heart rhythm specialists sometimes don't get it right either. And we all know that the 12 lead ECG itself is vexed by its computer algorithms at getting the QTC just right, compared to those of us who would view ourselves as QT aficionados. And so we were hoping that a machine learning approach would solve this and help us glean, one, a very accurate QTC, as accurate as I can make it when I measure it, or core labs that do QT measuring for living.

Dr. Michael Ackerman:

And two, could we get that QTC from just a couple of leads to be as accurate as what the whole 12 lead ECG would be seeing so that we can move it to a mobile smartphone enabled solution? And so that was our hypothesis going forward, and we studied a lot of patients. And that's something that machine learning and the power of computation does, that in my world, I'm used to studying a hundred or a thousand patients with congenital long QT syndrome and thinking that I've assembled a large cohort, but for this study, we started with over two and a half million ECGs from over 650,000 people. And then ultimately, through training, testing, and validation of about 1.6 million ECGs from over a half a million individuals to sort of teach the computer or have the AI algorithm get the QT interval not too hot, not too cold, but just right. And as we'll discuss, I think we hit the mark.

Dr. Greg Hundley:

Thanks so much, Mike, what did you find?

Dr. Michael Ackerman:

Ultimately, we were able to show that with this drill, we could get the deep neural network derived QTC to be give or take two plus minus 20 milliseconds from what would the standard of care, and that being a technician over-read QTC. But then we took, I would say, pretty unique to AI studies, as many AI studies, just do training, testing, and validation for study number one. And then a future paper of a prospective study. But we did that prospective study within this single paper with a subsequent about two year enrollment of nearly 700 patients that I evaluated in our genetic heart rhythm clinic at Mayo Clinic. And half of those patients have congenital long QT syndrome, half did not. And what we showed was that the deep neural network derived QTC from a mobile ECG approximated the subsequent or the just prior 12 lead ECG within one millisecond, +/- 20 millisecond territory.

Dr. Michael Ackerman:

And it's ability to say is the QTC above or below 500, which we all know is sort of a warning sign, that's a very actionable ECG finding, do something about it, that that 500 millisecond cutoff by the deep neural network gave us an area under the curve of 0.97, which from a screening perspective, that AUC is far higher than a lot of AUCs for a lot of screening tests done in the cancer world and so forth. And so we think we are very close to what I've called a pivot point, where we will soon pivot from the way we've been doing the QTC since Eindhoven over a century ago to a fundamentally new way of deriving a QTC that's precise and accurate and mobile enabled.

Dr. Greg Hundley:

Very nice, Mike. So using machine learning to accurately assess the QTC from just two leads of an electrocardiogram. Well Chris, you also have a paper in this issue of circulation that pertains to another application of machine learning and looking at the electrocardiogram. Can you describe for us your study population, study design, and then also the question you were trying to address?

Dr. Christopher Haggerty:

Sure. Yeah, thanks Greg. Great to be here with you all today. Very similar to Mike's study, the motivation for us was we believe very strongly that there's opportunities with using deep learning applied to ECG data to uncover not only new knowledge latent in the ECG itself related to the current patient context, but also to try to predict future outcomes, future events. And that was really our motivation, was to take that paradigm of looking forward, in this case to predict new onset of atrial fibrillation within a year. We used our Geisinger patient cohort, which is a largely rural population in central Pennsylvania. We have very longitudinal data for a lot of our patients, which allows us to have this kind of design going back in our electronic health records, in this case, our ECG database to 30 plus years.

Dr. Christopher Haggerty:

Similar big numbers that Mike described, and in our case, 1.6 million ECGs over 430,000 patients used to train the model. And we had several different study designs that we employed. One just being a simple proof of concept, asking can we accurately predict new onset atrial fibrillation one year? And then a second study design that was intended to simulate a real world deployment scenario. Obviously the main rationale for trying to predict atrial fibrillation is to then be able to treat and try to prevent stroke. And so we tried to, as best we can in a retrospective fashion, simulate a scenario in which we might use this model to identify patients who went on to have a presumably AFib associated stroke.

Dr. Greg Hundley:

And what did you find, Chris?

Dr. Christopher Haggerty:

So I think there are three main findings that we highlighted here. So first, obviously we were building on the great work that Mike and some of his colleagues at the Mayo Clinic have done, showing that looking at AFib using deep neural networks needs to be feasible. We extended it in this case by looking out further than just an acute sense, looking at that one-year outcome. And we had an area under the curve for our proof of concept of 0.85. So area under the curve of 0.85 to identify patients with new onset of atrial fibrillation within one year in our millions of ECGs. Looking at it another way, the second main finding was that that one year prediction was shown to have prognostic significance beyond that one year, which is really interesting and warrants a lot of further study. Looking over 30 years of follow-up, patients predicted to be at high risk at baseline had a hazard ratio of 7.2 for developing atrial fibrillation, compared to those deemed to be low risk.

Dr. Christopher Haggerty:

And then really the third, and I think perhaps the most exciting finding that we had here, was this simulated stroke experiment that we had, where we identified patients from an internal stroke registry and identified patients who had new diagnosis of AFib at the time or up to a year after the stroke. So we can assume that they were an AFib associated stroke. And subsequently, or I should say previously, had an ECG that we could use to run through the algorithm to predict their atrial fibrillation risk. And we showed that the model performed well in this setting, that of the 375 strokes that we identified, for example, over a five-year period in our registry, we were able to identify 62% of them within three years based on that ECG. So a number needed to screen for an atrial fibrillation associated with stroke about 162, which compares favorably well to other screening techniques that are out there, obviously. So we took that as a great proof of concept that this type of AI technique might have benefits for screening for atrial fibrillation and preventing strokes.

Dr. Greg Hundley:

Well congratulations, Chris. Well, we're now going to turn to our associate editor, Dr. Nick Mills. And Nick, you have a lot of manuscripts come across your desk. What attracted you to these two papers, and what are the significance of the results as they apply to ECG applications as we move forward?

Nick Mills:

Thanks, Greg. Yeah, this is a rapidly growing field, where the availability of data scale with digital archiving and lots of really interesting new methodologies are available to our researchers. So we are receiving a lot of content in this area. What I loved about these two papers is not just the quality of the work, but also the really tangible benefits, potentially, for patients. So AI does not need to be complex, but it does need to solve a tangible problem. I guess what we look for in the journal, beyond the kind of innovation and methodology, is quality, and these studies used prospective validation, really reliable end points, ascertainments, transparency, reporting, all the things that we know are important for high quality clinical research. I think the idea that we can bring QT monitoring to the drug store on a portable device for our patients is potentially transformative. I also think that to take a technology, the electrocardiogram that we've been using for over a century, and provide new insights that go way beyond my ability to interpret the ECG, that might help us recommend a different course of action for our patients is also just really exciting.

Dr. Greg Hundley:

Very nice. Thank you, Nick. Well Mike ... we're going to turn to Mike Rosenberg now, listeners. And Mike wrote a wonderful editorial, and I would invite you to work through this. As you have an opportunity to read the journal and interact with it. Mike, there are two different types of machine learning, I think, that you described were used by the two respective investigative groups. Could you describe those for our cardiology listeners? What were the differences in those two approaches?

Dr. Michael Rosenberg:

Yeah, sure. And thank you for the opportunity to write the editorial. Two very fascinating papers. I should say that they both use the same approach of what's called supervised learning, where you basically have a set of data inputs, and you're trying to predict a labeled outcome. And what I talk about in the paper is that what we've learned is if you have enough data and enough computing power, you can predict almost anything highly accurately. What's interesting about the two papers, and what I sort of tried to contrast in the editorial, is that the one from the Mayo Group and Dr. Ackerman, was basically predicting what's already a known biomarker for sudden death, which is the QT interval. And essentially, almost trying to automate that process of predicting it accurately and in a way that, in essence, could allow a home monitoring of patients for QT prolongation, which obviously would be a huge benefit for clinicians, all those alerts and things, to be able to have patients taking drugs that are known to prolong the QT interval and feeling comfortable that if they have any prolongation, it could be detected accurately.

Dr. Michael Rosenberg:

The second one, which is sort of interesting, and in contrast is from the Geisinger Group and Dr. Haggerty, was the approach of ... where actually the prediction itself is actually the biomarker. And we don't actually know exactly what it's using, which I talk about a little bit of what that means and the implications clinically, but in essence, what they showed was that it actually is a very good biomarker and on par with what a lot of us would consider to be very strong predictors of agents. So I think it was two very interesting approaches to, again, applying the same type of machine learning, but really approaching it one from a more discovery side and another from sort of validated or almost automating something that we do on a daily basis.

Dr. Greg Hundley:

Thank you, Mike. So Mike, just coming back to you again, as we read the literature, and most of us are clinicians or researchers practicing, what should we look for when these new machine learning manuscripts and research studies come out as to gauge, "Ah, this is a really good study," or maybe not so much?

Dr. Michael Rosenberg:

Yeah. And it's a good question. I think one of the biggest challenges, as I talked about, is interpretability. I think in the clinical world, we're used to understanding the code for the variables that go into our risk prediction model. And so I think first and foremost is can I even understand what this is predicting or am I sort of expected to take the predictions as sort of a black box, it is what it is type of approach? I think that there's other things that I just look at when I'm reviewing these manuscripts. I mean, as I sort of mentioned, what these models are really doing, it's not anything magical. What they're doing is identifying patterns in the data and then using those to make predictions, again, toward whatever label that you've assigned them to.

Dr. Michael Rosenberg:

It's important that your data sets are split and that you're training at one data set and then testing it in one that's separate. And again, you can't ignore epidemiology. Is the data set that you're training it reflective of the population that you're going to be using those models in? And we know from outside of healthcare, there's issues with models that have been trained in one population where it's potentially biased or it's potentially offering predictions that are using information we may not necessarily want to use. Recidivism is a big example of that. So I think that that's, first and foremost, it's sort of taking a step back as a clinician and saying, "If this was a biomarker that someone was proposing to use to predict some new disease, what would I expect to use to evaluate that?" And that's probably what I would start with.

Dr. Greg Hundley:

Excellent. Well, I'm going to turn back and go back to our panelists here, listeners. And we're going to ask each of our panelists in about 20 seconds to describe for us what they think is the next most important aspect of research in their respective areas. So first I'll start with Mike Ackerman. Mike, can you tell us what's coming next in this area of assessment of QT prolongation or other aspects of the electrocardiogram?

Dr. Michael Ackerman:

I think next is implementing this in the real world. We are having our suite of the AI ECG as a  hypertrophic cardiomyopathy detector. We've shown that as an ejection fraction detector, and now as a QT detector in AFib, from our work and Chris's work. And for the QT itself, I think where we are is we're really, really close to now having a mobile enabled digital QT meter. And a digital QT meter, once FDA cleared, then allows the QTC to truly emerge as the next vital sign. And it really deserves to be a vital sign. We use it as a vital sign. We know I want to know my patient's QTC every bit as I want to know his or her weight, blood pressure, saturation. It's an actionable finding, and we're now getting really close. We're just on the cusp of having a true digital QT meter.

Dr. Greg Hundley:

Excellent. Chris?

Dr. Christopher Haggerty:

I think for us to, in part address some of the comments that Mike brought up about the reproducibility of these types of models, we're very keen to demonstrate the prospective capabilities of our models to enroll patients in a prospective fashion, run their ECG through our predictor, and then screen them for AFib to determine how well we actually do moving forward, instead of just relying solely on our retrospective data. So we're very excited to do that. We're ramping up for that trial now and hope to be able to demonstrate similarly positive findings from our technique.

Dr. Greg Hundley:

Great. How about you, Nick?

Nick Mills:

I'd like to see the same quality and rigor applied to the implementation of these technologies as we have to other important areas in cardiovascular medicine. I think that's a really important step, not just to develop the tools, but to demonstrate their value. But I also think what we've done so far is relatively simplistic. We've taken an ECG and we've ignored almost all the other information that we have in front of us. And as these algorithms are trained and evolved, these and other vital clinical biomarkers and information, and integrating them into these neural networks will really enhance their performance for predicting things that are less tangible, like sudden death in the future or stroke.

Dr. Greg Hundley:

And then finally, Mike Rosenberg.

Dr. Michael Rosenberg:

Yeah, I actually see two challenging areas in this field. One is the access to data. And I think one of the things that a lot of companies are realizing is that even if they make hardware, that the data may be more valuable than the technology that they're getting the data from. So I think one is figuring out ways to get access to data so that people can reproduce findings from these studies. And the second is deliverable. A bottle like this is not like the CHADS-VASc score that I can calculate in my head in the clinic. I mean I need a way to actually run these models within an EHR, within a computer system like that. And I think it's going to be a big challenge to take a model like this and to deploy it at scale the way we would with the drug or any other innovation.

Dr. Greg Hundley:

Fantastic. Well listeners, we want to thank Mike Ackerman from Mayo Clinic, Chris Haggerty from Geisinger, Mike Rosenberg from University of Colorado, and Nick Mills from University of Edinburgh for really providing us with a wonderful discussion regarding the use of machine learning applications in one study to predict the QTC interval from two leads that may be applicable to wearable devices. And in the second study, predicting the future occurrence of atrial fibrillation and even stroke as an adverse event in people at risk.

Dr. Greg Hundley:

On behalf of both Carolyn and myself, I want to wish you a great week and we will catch you next week on the run. This program is copyright of the American Heart Association, 2021.