2022 in Review Through a Cardiology Lens

Live Meetings, COVID, Wearables and AI, RCTs in China

; C. Michael Gibson, MD


December 19, 2022

This transcript has been edited for clarity.

Robert A. Harrington, MD: Hi. This is Bob Harrington from Stanford University, here on theheart.org | Medscape Cardiology. Over the past several years, I've had the pleasure of doing a yearly wrap-up in December with my good friend and colleague, Dr Mike Gibson, from Boston.

In these segments, what we'd like to do is cover the landscape of cardiology. What are some of the trends? What are some of the things that Mike and I are talking about with our broader circle of colleagues that really caught our attention in the past year?

This year, I thought we'd cover topics that will run the gamut from what I'll call high-level policy issues to some specific clinical trial observations. Mike and I will, in our usual way, go back and forth.

With that, let me introduce my good friend and colleague. Dr Mike Gibson is an interventional cardiologist at Beth Israel Deaconess in Boston. He's also a professor of medicine at the Harvard Medical School, and he is the CEO of the Baim Institute, a not-for-profit academic research organization. Mike, thanks for joining us here on theheart.org.

C. Michael Gibson, MD: It must be December. You and I are back and it.

In-Person Meetings

Harrington: The Bob and Mike show goes again. Mike, let's start with big, broad topics and then maybe filter down. The first one I want to talk about in 2022 is live meetings — they're back. We went to ACC, we went to ESC, we went to AHA, and there were smaller meetings in between. You cover many of the meetings from a news perspective. Give me your observations.

Gibson: We're back and it's great. I think the societies have done a great job of this whole virtual thing. I mean, it was amazing. People around the world could get on, watch what was going on, and participate in some way where they could get the information. They got the data, but meetings are about more than data. They're about relationships. We practice evidence-based medicine, but we also practice relationship-based medicine.

No matter what business you're in, you're in the people business, where 70%-90% of communication isn't the words but the body language. When you do things virtually, you lose the ability to connect and have that trust that develops from those more intimate face-to-face connections. Many of the meetings happen outside the rooms, and that's why it's so important to go to the meetings. It's the hallway conversation that's almost as important as what's said at the podium. There's what's said at the podium, but then everyone's walking around and saying, "What did you think?" They're looking for the digestion of that content.

I think it's been great. The meetings are smaller, but I'm running into all the people I really want to see. I do think they could maybe downsize the venues a little bit so the density is better. Sometimes when you have a great big convention center, there's just not that bumping into each other. It doesn't feel as hot or as exciting. The best thing is being able to see my friends — going up to you, Bob, and giving you a big hug. Can't do that online.

Harrington: I agree with you. The networking. How many times do I say, "Hey, Mike, there's this person I want you to meet," and I introduce you to somebody who ends up working with you, or you introduce me to somebody who ends up being a fellow here in the next couple of years. I do think that those sorts of interactions are critically important.

Then, as you and I always talk about, there's the, "Hey, I got this idea" conversation, where in the morning over a cup of coffee, or in the evening over a beer, you can sketch out a grant we want to put in together or a proposal we want to pitch to somebody. Those things only take place in that sort of intimacy.

At the same time, wow — to be able to share the education with people who, for whatever reason, either don't want to or can't go to a live meeting; they can still get the education. I agree that the societies and the other organizations have done a great job.

One thing, Mike, that I want to comment on. You just finished a very large clinical trial that enrolled many patients during the COVID-19 pandemic. One of the things that amazed me at the three big meetings in cardiology was that the late-breaking clinical trials didn't stop. Work was still getting done. You want to comment a little bit?

Gibson: Yes, it's dumbfounding. We got through COVID-19, we got through some unrest and war. Amazingly, the people in Ukraine are better able to follow their patients up than some of the US investigators. It is a testament to, I think, the dedication of people around the world to continue to get the job done.

On the other hand, Bob, I have to say that we are going to have the walking wounded after all this. I think many sites closed down. They made it through some of these trials, but then they closed down. We're going to have to go through a rebuilding phase. It's really a testament to everyone we work with that they got the job done.

Harrington: Well said. I do think many sites did suffer, and research nurses moved off to the clinical domain because there was a need at their institution or they just were exhausted trying to do everything they were doing. I do agree with you. I think there's going to be a rebooting of research — not only in the United States but also around the globe — to see if we can continue to do this better. I do agree with you that the cardiovascular research community is amazing with their dedication to really wanting to answer questions.

COVID 19 Trials

Gibson: We moved over to doing COVID-19 trials, too.

Harrington: That brings you to my next topic, which is COVID-19. We're certainly not done with the pandemic. The vaccines have made a big difference. The antivirals and the monoclonal antibodies have made a big difference. We lost many people in the United States and we're not done yet. There are still people dying every day. Our own hospital still has a number of patients with COVID-19 this morning. I suspect that yours is the same.

Let's talk about two things, Mike, including lessons learned in the research realm and then a little bit about cardiovascular issues post–COVID-19.

Gibson: I think we learned many things about ourselves. One of the strengths of the United States is that we are fiercely independent. One of the weaknesses of the United States is that we are fiercely independent. What happened was that we had many well-intentioned people trying to answer the same question in 20 different ways. The UK got it right. They got together and they had a series of well-organized studies that showed a path forward with a few therapies like steroids.

We did get our act together in April 2020. Different agencies, including the NIH, the FDA, and the CDC, all came together and said, "Look, we have to get organized, we have to identify what we think some of the best shots on goal are, we have to create some master trial protocols so everyone's doing things the same way, and we have to develop some networks." That was successful.

The lessons weren't all positive trials, and that's okay. That's important. We learned that some things didn't work. We learned that therapeutic doses of heparin were no better than prophylactic doses. We learned that adding antiplatelets didn't help at all. We learned that things like ivermectin didn't work, and we learned that many of these new antibodies or combinations of monoclonal antibodies really didn't improve care either.

It's a little bit like what you're saying in Silicon Valley: Fail fast. It's important in trials to fail fast, too, and to discard therapies that aren't going to work so that we can go assess some that are going to work. One of the most important enduring lessons is getting organized and getting these master protocols together so that we can do this again in the future when the next pandemic happens.

Harrington: There were networks that have existed that were not designed for infectious disease pandemics, like the Cardiothoracic Surgical Trials Network, which turned their attention to doing COVID-19 studies. Guess what? They did it really well because they were an organized group of investigators with supported infrastructure that then did really well.

I love your comment on master protocols. I also like your comment, Mike, on failing fast. All of these studies that came out of NIH had built into them very aggressive, early interim analyses to discard the ones that weren't likely to be working. You're absolutely right that sometimes knowing what doesn't work is as important as knowing what works.

The final thing I'll say from a US perspective is that I do think we are uniquely positioned because of the type of investigators that work with NIH. We haven't even seen yet all the mechanistic work that's going to emerge and all the biologic insights that are going to be made. I think there is more to come about trying to understand this disease and our biologic response to it.

Gibson: We have many battles to fight. We have many patients with long COVID out there.

Harrington: Let's switch to long COVID, Mike, and post-acute syndromes. On this show, I have interviewed several people about the cardiovascular implications of having had COVID-19 infection. The analysis that came out of the VA in the spring that we talked about on this podcast pointed out that there was an increased risk for cardiovascular diseases. Rob Califf, our mutual friend who's the FDA commissioner, has called this the coming tsunami of the common chronic diseases.

What are you thinking about as we think about the world post–COVID-19?

Gibson: I read your group's study with interest, and surely COVID-19 is associated with long-term outcomes. I think we have to be very careful in understanding the causality there. The people who develop COVID-19 and the people who developed the worst COVID-19 cases tend to be those people at high risk. I think we have to be very careful in adjusting for comorbidities, frailty, and everything else so we can truly attribute just how much of all that badness is attributed to COVID-19. I'm sure there is some there. There are good reasons to think some is there because of all the endotheliitis, damage to the lining of your vessels, and the virus infiltrating your myocardium, etc. There are many good reasons why we're going to see the tsunami of long-term outcomes. I'm most worried about some of the cognitive effects and the long-term sequelae there.

Harrington: I'll put a pitch in that the NIH has a long-term post–COVID-19 study that's beginning. It's a cohort study to begin with, but there are going to be randomized trials built off of that called RECOVER. Here at Stanford, we are a site, and I'm proud to say that I am a research participant. I have signed up and have had my first visit post-COVID to participate in the collection of information about what does happen to us over the course of the years ahead. I'm a longtime researcher, as you know, and I'm a research participant. To my colleagues out there, please do look up RECOVER and see if you can refer colleagues, friends, family, or yourself.

Gibson: I was a member of the HERO study. It's important to be not only a researcher but also a patient, and to see what patients go through and what it's like to do all these surveys and stuff.

Harrington: I will say that on my first visit, I was sitting there getting my blood drawn and I looked at the number of tubes and I said, "Are you going to take all of that from me?" I'm a pretty knowledgeable researcher, and it made me think, from a patient perspective, when lining up 15 tubes to say, "Yes, that's all coming from you." There might be a better way to communicate that information.

Gibson: Yes.

Artificial Intelligence and Wearables

Harrington: Mike, let's go to something that you know well: the whole world of wearables. You've been involved in a number of these studies. We here at Stanford have been involved with some of these studies. The COVID-19 pandemic was another time where telemedicine, wearable information, and monitoring at home all helped us accelerate. Do you remain bullish on the use of wearables and other digital devices for collection of information in the years ahead?

Gibson: I do remain bullish. I think we're going to learn so much. As you say, we have terabytes of information about each of these patients in some of our trials. When you put it all together, we then have petabytes of information where you can't say, "Here, take the database." You can't transfer the database because it has to live in the cloud. It's fascinating to talk about how you analyze the data and who has access to the data.

There's so much coming down the pike. When you monitor someone 24 hours a day, you're going to find things, but how much abnormal signal constitutes disease? How much AFib do you have to have to say you really have AFib, particularly when it comes to deciding whether someone needs a therapy that has some risk, like bleeding?

We have much to learn in terms of how much disease warrants treatment — that's going to be the next frontier. We also don't want to create the worried well. We don't want to medicalize every extra skipped heartbeat. We're trying to find a happy medium there.

Harrington: Yes, there are many lessons about what I'll call operational engagement, including how you find people for trials, how you get them engaged, and how you get them to keep sharing their information. Then there is much to learn about what to do with that information.

Gibson: Right. I have to say, though, enrolling people over 65 into a virtual trial, I thought these people just weren't going to keep using the app. They continue to use the app 80%-90% of the time months later. Maybe they didn't have anything to do at home during the COVID-19 pandemic except to use the app. We are finding positive news that they're remaining engaged, which we are very surprised by.

Harrington: The studies we've done here, we're finding that it's not just, as you say, the worried well. It's people with a history of coronary disease, with a history of hypertension, and with a history of ischemic heart disease who are signing up and who want to learn about their health and what we might learn from these devices that we're all wearing.

Mike, that brings me to the next question. Again, you've been doing this in your research group. You're talking about this huge amount of data. What interests me is that it's also very disparate types of data. It's EHR data, actual images, wearable physiologic signals, and environmental data. Some of the new tools are really old tools, but they're now being applied because of things like the cloud, artificial intelligence, machine learning, and neural networks.

You can't pick up a journal right now and not see a paper where somebody has used one of these computational methods to gain some insights into a topic. Now, some of that, as you and I know, is, Well, did you really need it for that?, but some of it is really pretty clever, pretty creative.

Gibson: My favorite in that regard is the Mayo Clinic algorithm that can look at the EKG and determine whether you're a man or a woman. I'm not sure we needed AI for that. One of the things we published last year, bringing together the wearables with the AI, for example, was using a single lead and being able to diagnose STEMI. That's really cool: 24-hour–a-day monitoring without a trigger and you can detect a heart attack. That's kind of cool.

On the other hand, we have to be careful. It's a black box and it's often overtrained on the training dataset. We can learn that something derived from all men in the VA system doesn't do that well when it comes to women. We have to make sure that the training will fit the general population.

One of my favorite examples in the imaging world is an algorithm that determines your prognosis based upon the melanoma image. When you look in the black box, it was just detecting a ruler. If you had a ruler there, your prognosis was very poor. Of course, the ruler meant it was bigger. It wasn't anything to do with the serpiginous border. We have to make sure we're looking at the black box.

I think there's a large amount of hope there. I'm not sure it's as good as the hype that's out there. I do think that progress is mostly being made in imaging, which is great. I don't think the imaging will replace people. I think it will supplement our human readers. I have done research over on the decision-making side. AI and machine learning is a little bit better than logistic regression, but it doesn't really hit it out of the park. We have to be cognizant of that.

We're working on the fact that, rather than predict a year down the road what's going to happen now, just a little bit like the weather, I think we're going to do better with iterative looks: "Well, you haven't bled after 3 months [of antithrombin/antiplatelet therapy]. You're a different patient now. You declared yourself a non-bleeder on your bleeding stress test. Let's now look forward to iterate and make that prediction again." I think you're going to see an evolution in how we use these tools and more of an iterative approach as well. I think the future is exciting.

Harrington: I agree with you about some of the early wins around imaging. There have been a couple of things that have caught my attention. One is from your colleague in Boston, Calum MacRae, who has done some really interesting work with Shinichi Goto, a fellow from Japan. You and I know his dad quite well as a clinical trialist. They've done some really interesting work about how you can utilize imaging datasets and reconstruct information about more limited imaging so that you can extend those observations to that limited image.

The example here is that you get a lesser-quality image from someplace, maybe without well-trained echo techs, for example, and using machine learning and other datasets, you can actually help almost reconstruct the information that you might have gotten if you had had a more full echo. Boy, start to think about the opportunities for that.

I do agree with you, Mike. I think we need to test all of this stuff. Just because it uses artificial intelligence or machine learning doesn't mean that you can avoid the need for understanding it in the use, say, for example, of clinical trials.

Gibson: I think it could really help in rural areas where you don't have as many healthcare providers, for instance. If you look at data from the Mayo Clinic, they can detect LVH on the EKG using AI, and that's a trigger that you need an echo. Then you have a little echo probe that the nurse can use that says turn to the left, up, down, and then it interprets it using AI.

Harrington: I love how you said it. One of our radiologists here — I always paraphrase — when he's asked, "Will AI replace radiologists?" (and, we also could say, replace cardiologists), he answers, "No, but radiologists/cardiologists who use AI will replace those who don't."

Gibson: Correct.

RCTs From China

Harrington: Mike, let's move to randomized clinical trials coming from China — not as a participant in much larger trials, but the entire trial. There have been some very large trials and some very good ones. There was a terrific one that Gregg Stone presented on bivalirudin, which looked to be, at least from the presentation, exceedingly well done.

There was one that my colleague, Ken Mahaffey, gave the commentary on, which was a dietary supplement as an adjunct to ST elevation MI. Forget the topics that they studied, Mike; just comment broadly as a trialist. Wow! If China can really do some of these studies with their organized workforce and their population, they can make major contributions to the rest of the globe.

Gibson: Absolutely. In anticipation of our call today, I got on clinicaltrials.gov, I typed in "STEMI," and I typed in a search term of "China." Out of the 740 STEMI studies that are ongoing worldwide right now, 120 are being done in China. How many do you think are being done in the US?

Harrington: Less than 100.

Gibson: There are 78. They are really getting their act together, like we said earlier, right? They're getting organized. Matt Herper, on STAT, said that the single biggest risk in upcoming biomedical development is that in having new technologies, it's getting organized and doing randomized trials to assess the technologies. This is an issue of execution. If they're able to execute, which it looks like they can, they can do great things.

The other thing they're doing is taking many shots on goal. They're assessing things that we would have discarded in the US, such as alternate therapies that we may have discounted. I've always been worried that we're spending $1.2 billion to assess drugs. What if we could do it much more quickly and assess many more targets? That would be a better way for the ecosystem to find new therapies.

Harrington: Mike, as always, this has been a really fun discussion. My guest today has been my good friend and colleague, Mike Gibson, from Boston — an interventional cardiologist; professor at Harvard; and the CEO of the Baim Institute, a not-for-profit academic research organization. Mike, thanks for joining me on Medscape Cardiology.

Robert A. Harrington, MD, is chair of medicine at Stanford University and former president of the American Heart Association. (The opinions expressed here are his and not those of the American Heart Association.) He cares deeply about the generation of evidence to guide clinical practice. He's also an over-the-top Boston Red Sox fan.

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