This transcript has been edited for clarity.
Jagmeet P. Singh, MD, MSc: Hello, everybody. I'm Jag Singh from Medscape Cardiology. It's a pleasure to be here. I'm delighted to have my close friend and colleague from Massachusetts General Hospital, Dr Steven Lubitz, here with us. Welcome, Steve.
Steven A. Lubitz, MD, MPH: Thank you, Jag.
Singh: Steve is an associate professor of medicine at Harvard Medical School and a clinical electrophysiologist. Just yesterday, he presented a top-line late-breaker clinical trial, the Fitbit Heart Study. We're very excited to hear about this from him.
Steve, for those who were not there for yesterday's presentation, I was hoping you can give us an overview as to what the findings of the study were and what the design of the study was.
The Fitbit Heart Study
Lubitz: Sure, delighted to. Thanks, Jag. By way of background, we know that undiagnosed atrial fibrillation and flutter can cause morbidity, including strokes, which are debilitating. These can be prevented with early detection of atrial fibrillation. Many individuals these days are wearing smartwatches or fitness trackers that are equipped with optical sensors to measure photoplethysmography (PPG) — signals that can detect the heart rate. Software algorithms can now be applied to those PPG signals to infer the presence of atrial fibrillation.
We evaluated a novel Fitbit software algorithm that examines frequent and overlapping PPG signals collected by Fitbit wearable devices for detecting undiagnosed atrial fibrillation. We tested the algorithm's positive predictive value for concurrent undiagnosed atrial fibrillation in a large-scale remote clinical trial of existing wearable users in the United States.
Essentially, participants who enrolled were evaluated for the presence of an irregular pulse using this algorithm, and those in whom the algorithm detected an irregular rhythm were sent a notification and invited to schedule a visit with a telehealth provider. Then, if eligible, they were mailed a 1-week ECG patch monitor that they self-applied and returned to us for analysis.
Singh: There were, if I remember correctly, some 460,000 patients. That's a huge number. Before we get further into the findings, what were the challenges with remote recruitment, remote consenting, and remote follow-up? You're changing the landscape of clinical trials out there, and even remote engagement. What did you experience, and what can future clinical trials in this arena learn from your trial?
Lubitz: I think it's a great question. That's right — over 455,000 participants enrolled. Thank you to all participants who engaged in the study and participated in roughly a 5-month period of time in the middle of a pandemic.
There were a number of challenges. One was that there was overwhelming enthusiasm for participation in the study, as reflected by that sample size. We weren't sure at the beginning what the degree of engagement would be or what the enthusiasm and enrollment would look like, but it was overwhelming.
As we marched through the study flow, we saw that there was some attrition of participants who enrolled in the study. This has been observed before in other remote, large-scale clinical trials. It presents a major challenge, I think, for remote trials because we really don't have that warm-touch face-to-face, person-to-person interaction during these types of trials. We're not engaged with the healthcare system and the providers who the participants are accustomed to interacting with, so we lose that personal degree of engagement from participants in trials like this. It presents a major challenge for remote trials in the future, I think.
Comparisons With Apple Heart Study
Singh: That's very useful. One of the other large studies, which obviously everyone knows, was the Apple Heart Study, which looked at a PPG algorithm, too. What do you think the Fitbit Heart Study contributes beyond what we've learned already from the Apple Heart Study? Besides validation of the algorithm, are there any other things that you think we should have as a takeaway message?
Lubitz: I think there are some common themes here between the studies, and there are some differences between the studies that we can highlight. The biggest common theme is that these algorithms that analyze PPG waveform data can accurately detect atrial fibrillation.
In our study, we saw a very high positive predictive value of 98% for concurrent atrial fibrillation when an irregular heart rhythm was detected during concurrent wearing of the ECG patch monitor. That's higher than has been reported in other algorithms to date.
We also saw that among the individuals who had an irregular heart rhythm detection and then subsequently returned an ECG patch monitor after wearing it, about 32% had atrial fibrillation confirmed during that period of time when they were wearing that ECG patch monitor. That's comparable to what Apple saw.
In our study, we also examined the burden of atrial fibrillation. We observed that participants who had atrial fibrillation during that ECG patch monitor wear time had a median burden of 7%, which is not trivial. In other studies in which patch monitors have been applied to screen for atrial fibrillation without any type of irregular pulse waveform prescreening or sampling prior, like we did in this study, the burden is, on average, only about 1% or so.
On average, the detection of atrial fibrillation only occurs in up to about 5%, whereas we saw 32% in our study. This are marked enrichment for atrial fibrillation and enrichment for people who have a substantial burden of atrial fibrillation as well.
Singh: One of the things that stands out with all these PPG algorithms is that they're not recording EKGs at the same time. I think many of these trackers and smartwatches have the ability to do that, but most of these are able to, I would say, confirm atrial fibrillation if it's greater than 30 minutes in duration, right? And also more so at nighttime, when patients are not moving their hands and they're inactive, the algorithm works better.
Talk to me about what the implications of the way the algorithm works right now could be on large-scale surveillance studies in the population. Will it or will it not have a role?
Role of Wearables for Patients
Lubitz: I think this is a great question as well. There are some practical takeaways here for clinicians who end up speaking with their patients about the use of these devices and for consumers who have these devices, and that is that these algorithms to detect atrial fibrillation are most operative when participants or users are not active. That's to minimize the interference that can occur and artifact that can occur during periods of motion and activity.
It's during periods of rest that these algorithms are most likely to collect analyzable data and potentially be of use. That's important for participants or users to know. If they can wear it during sleep, then all the better, because the probability of detecting atrial fibrillation may be highest during sleep.
At a broad scale, what we know now is that when these algorithms detect an irregular rhythm, there's a reasonably high probability that an individual may have atrial fibrillation and it warrants serious consideration by a clinician. We have to figure out how to bridge that gap between a user receiving a notification that they have an irregular heart rhythm detection and getting them plugged in with the right form of care.
We also have to figure out the proper ways in which these types of irregular heart rhythm detections need to be followed and evaluated by the medical community, because we don't have strict guidance. We know what we did in these studies, and that's one way of evaluating these irregular heart rhythm detections. We really do need a more robust way of integrating these data into our existing healthcare structure and thinking about new healthcare structures to handle these data in the future.
Singh: I couldn't agree with you more. I think it's an amazing first step, for sure. At the same time, giving watches to patients who are asymptomatic and have durations of atrial fibrillation that are less than 30 minutes — which we know can still be clinically meaningful in some patients who have high CHA2DS2-VASc risk scores and are predisposed to strokes —could give them a false sense of security that they don't have atrial fibrillation if it was not more than 30 minutes long. I think that certainly needs to be put in perspective, as you just said, and we need to have larger, longer-term outcome studies in specific populations to really understand what the clinical role and value of this algorithm in larger populations is going to be.
One thing I noticed, which is phenomenal, is that 70% of the patients recruited were women. Congratulations to you and your team for doing that. I think it's probably one of the very few studies — one can count them on one hand — where women have been recruited much more than men. Was that intentional, or did it happen organically? Can you give us insight as to how that occurred?
Lubitz: It more or less happened organically. It may reflect the demographics of the user base. It may reflect some other aspects of predilection to participate in this type of remote clinical trial among this user base. We also were able to recruit about 13% of individuals aged 65 or older, which obviously is an important subset, given the increased risk for stroke if they were to have atrial fibrillation detected.
To echo your point, we do really need to think about equity, in particular, with remote clinical care and mobile technology, including smartwatches and fitness trackers, and these types of algorithms. Within clinical trials and in the real world, we also really do need to be thinking about equity in healthcare disparities that may be amplified by this technology if we're not thoughtful.
Singh: Steve, that is terrific. On that wonderful note on diversity and equity and remote care, I want to congratulate you and your team for an amazing study, which is really going to change the landscape in how we practice clinical care and look after patients with atrial fibrillation.
Thanks again. It's been wonderful to have you here today. Take care.
Lubitz: Thanks, Jag. I appreciate it.
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Cite this: Lessons on AF Detection From the Fitbit Heart Study - Medscape - Nov 19, 2021.