cardiology
Decoding Heart-Brain Talk to Prevent Sudden Cardiac Deaths
Posted on by Dr. Francis Collins
As a cardiac electrophysiologist, Deeptankar DeMazumder has worked for years with people at risk for sudden cardiac arrest (SCA). Despite the latest medical advances, less than 10 percent of individuals stricken with an SCA will survive this highly dangerous condition in which irregular heart rhythms, or arrhythmias, cause the heart suddenly to stop beating.
In his role as a physician, DeMazumder keeps a tight focus on the electrical activity in their hearts, doing his best to prevent this potentially fatal event. In his other role, as a scientist at the University of Cincinnati College of Medicine, DeMazumder is also driven by a life-saving aspiration: finding ways to identify at-risk individuals with much greater accuracy than currently possible—and to develop better ways of protecting them from SCAs. He recently received a 2020 NIH Director’s New Innovator Award to pursue one of his promising ideas.
SCAs happen without warning and can cause death within minutes. Poor heart function and abnormal heart rhythms are important risk factors, but it’s not possible today to predict reliably who will have an SCA. However, doctors already routinely capture a wealth of information in electrical signals from the heart using electrocardiograms (ECGs). They also frequently use electroencephalograms (EEGs) to capture electrical activity in the brain.
DeMazumder’s innovative leap is to look at these heart and brain signals jointly, as well as in new ways, during sleep. According to the physician-scientist, sleep is a good time to search for SCA signatures in the electrical crosstalk between the heart and the brain because many other aspects of brain activity quiet down. He also thinks it’s important to pay special attention to what happens to the body’s electrical signals during sleep because most sudden cardiac deaths happen early in the waking hours, for reasons that aren’t well understood.
He has promising preliminary evidence from both animal models and humans suggesting that signatures within heart and brain signals are unique predictors of sudden death, even in people who appear healthy [1]. DeMazumder has already begun developing a set of artificial intelligence algorithms for jointly deciphering waveform signals from the heart, brain, and other body signals [2,3]. These new algorithms associate the waveform signals with a wealth of information available in electronic health records to improve upon the algorithm’s ability to predict catastrophic illness.
DeMazumder credits his curiosity about what he calls the “art and science of healing” to his early childhood experiences and his family’s dedication to community service in India. It taught him to appreciate the human condition, and he has integrated this life-long awareness into his Western medical training and his growing interest in computer science.
For centuries, humans have talked about how true flourishing needs both head and heart. In DeMazumder’s view, science is just beginning to understand the central role of heart-brain conversations in our health. As he continues to capture and interpret these conversations through his NIH-supported work, he hopes to find ways to identify individuals who don’t appear to have serious heart disease but may nevertheless be at high risk for SCA. In the meantime, he will continue to do all he can for the patients in his care.
References:
[1] Mitochondrial ROS drive sudden cardiac death and chronic proteome remodeling in heart failure. Dey S, DeMazumder D, Sidor A, Foster DB, O’Rourke B. Circ Res. 2018;123(3):356-371.
[2] Entropy of cardiac repolarization predicts ventricular arrhythmias and mortality in patients receiving an implantable cardioverter-defibrillator for primary prevention of sudden death. DeMazumder D, Limpitikul WB, Dorante M, et al. Europace. 2016;18(12):1818-1828.
[3] Dynamic analysis of cardiac rhythms for discriminating atrial fibrillation from lethal ventricular arrhythmias. DeMazumder D, Lake DE, Cheng A, et al. Circ Arrhythm Electrophysiol. 2013;6(3):555-561.
Links:
Sudden Cardiac Arrest (National Heart, Lung, and Blood Institute/NIH)
Deeptankar DeMazumder (University of Cincinnati College of Medicine)
DeMazumder Project Information (NIH RePORTER)
NIH Director’s New Innovator Award (Common Fund)
NIH Support: National Heart, Lung, and Blood Institute; Common Fund
COVID-19 Can Damage Hearts of Some College Athletes
Posted on by Dr. Francis Collins
There’s been quite a bit of discussion in the news lately about whether to pause or resume college athletics during the pandemic. One of the sticking points has been uncertainty about how to monitor the health of student athletes who test positive for SARS-CoV-2, the novel coronavirus that causes COVID-19. As a result, college medical staff don’t always know when to tell athletes that they’ve fully recovered and it’s safe to start training again.
The lack of evidence owes to two factors. Though it may not seem like it, this terrible coronavirus has been around for less than a year, and that’s provided little time to conduct the needed studies with young student athletes. But that’s starting to change. An interesting new study in the journal JAMA Cardiology provides valuable and rather worrisome early data from COVID-positive student athletes evaluated for an inflammation of the heart called myocarditis, a well-known complication [1].
Saurabh Rajpal and his colleagues at the Ohio State University, Columbus, used cardiac magnetic resonance imaging (MRI) to visualize the hearts of 26 male and female student athletes. They participated in a range of sports, including football, soccer, lacrosse, basketball, and track. All of the athletes were referred to the university’s sports medicine clinic this past summer after testing positive for SARS-CoV-2. All had mild or asymptomatic cases of COVID-19.
Even so, the MRI scans, taken 11-53 days after completion of quarantine, showed four of the student athletes (all males) had swelling and tissue damage to their hearts consistent with myocarditis. Although myocarditis often resolves on its own over time, severe cases can compromise the heart muscle’s ability to beat. That can lead to heart failure, abnormal heart rhythms, and even sudden death in competitive athletes with normal heart function [2].
The investigators also looked for more subtle findings of cardiac injury in these athletes, using a contrast agent called gadolinium and measuring its time to appear in the cardiac muscle during the study. Eight of the 26 athletes (31 percent) had late gadolinium enhancement, suggestive of prior myocardial injury.
Even though it’s a small study, these results certainly raise concerns. They add more evidence to a prior study, published by a German group, that suggested subtle cardiac consequences of SARS-CoV-2 infection may be common in adults [3].
Rajpal and his colleagues will continue to follow the athletes in their study for several more months. The researchers will keep an eye out for other lingering symptoms of COVID-19, generate more cardiac MRI data, and perform exercise testing.
As this study shows, we still have a lot to learn about the long-term consequences of COVID-19, which can take people on different paths to recovery. For athletes, that path is the challenge to return to top physical shape and feel ready to compete at a high level. But getting back in uniform must also be done safely to minimize any risks to an athlete’s long-term health and wellbeing. The more science-based evidence that’s available, the more prepared athletes at large and small colleges will be to compete safely in this challenging time.
References:
[1] Cardiovascular magnetic resonance findings in competitive athletes recovering from COVID-19 infection. Rajpal S, Tong MS, Borchers J, et al. JAMA Cardiol. 2020 September 11. [Published online ahead of print.]
[2] Eligibility and disqualification recommendations for competitive athletes with cardiovascular abnormalities: Task Force 3: Hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy and other cardiomyopathies, and myocarditis. Maron BJ, Udelson JE, Bonow RO, et al. Circulation. 2015;132(22):e273-e280.
[3] Outcomes of cardiovascular magnetic resonance imaging in patients recently recovered from Coronavirus Disease 2019 (COVID-19). Puntmann VO, Carej ML, Wieters I. JAMA Cardiol. 2020 Jul 27:e203557. [Published online ahead of print.]
Links:
Coronavirus (COVID-19) (NIH)
Heart Inflammation (National Heart, Lung, and Blood Institute/NIH)
Saurabh Rajpal (Ohio State College of Medicine, Columbus)
Using Artificial Intelligence to Catch Irregular Heartbeats
Posted on by Dr. Francis Collins
Thanks to advances in wearable health technologies, it’s now possible for people to monitor their heart rhythms at home for days, weeks, or even months via wireless electrocardiogram (EKG) patches. In fact, my Apple Watch makes it possible to record a real-time EKG whenever I want. (I’m glad to say I am in normal sinus rhythm.)
For true medical benefit, however, the challenge lies in analyzing the vast amounts of data—often hundreds of hours worth per person—to distinguish reliably between harmless rhythm irregularities and potentially life-threatening problems. Now, NIH-funded researchers have found that artificial intelligence (AI) can help.
A powerful computer “studied” more than 90,000 EKG recordings, from which it “learned” to recognize patterns, form rules, and apply them accurately to future EKG readings. The computer became so “smart” that it could classify 10 different types of irregular heart rhythms, including atrial fibrillation (AFib). In fact, after just seven months of training, the computer-devised algorithm was as good—and in some cases even better than—cardiology experts at making the correct diagnostic call.
EKG tests measure electrical impulses in the heart, which signal the heart muscle to contract and pump blood to the rest of the body. The precise, wave-like features of the electrical impulses allow doctors to determine whether a person’s heart is beating normally.
For example, in people with AFib, the heart’s upper chambers (the atria) contract rapidly and unpredictably, causing the ventricles (the main heart muscle) to contract irregularly rather than in a steady rhythm. This is an important arrhythmia to detect, even if it may only be present occasionally over many days of monitoring. That’s not always easy to do with current methods.
Here’s where the team, led by computer scientists Awni Hannun and Andrew Ng, Stanford University, Palo Alto, CA, saw an AI opportunity. As published in Nature Medicine, the Stanford team started by assembling a large EKG dataset from more than 53,000 people [1]. The data included various forms of arrhythmia and normal heart rhythms from people who had worn the FDA-approved Zio patch for about two weeks.
The Zio patch is a 2-by-5-inch adhesive patch, worn much like a bandage, on the upper left side of the chest. It’s water resistant and can be kept on around the clock while a person sleeps, exercises, or takes a shower. The wireless patch continuously monitors heart rhythms, storing EKG data for later analysis.
The Stanford researchers looked to machine learning to process all the EKG data. In machine learning, computers rely on large datasets of examples in order to learn how to perform a given task. The accuracy improves as the machine “sees” more data.
But the team’s real interest was in utilizing a special class of machine learning called deep neural networks, or deep learning. Deep learning is inspired by how our own brain’s neural networks process information, learning to focus on some details but not others.
In deep learning, computers look for patterns in data. As they begin to “see” complex relationships, some connections in the network are strengthened while others are weakened. The network is typically composed of multiple information-processing layers, which operate on the data and compute increasingly complex and abstract representations.
Those data reach the final output layer, which acts as a classifier, assigning each bit of data to a particular category or, in the case of the EKG readings, a diagnosis. In this way, computers can learn to analyze and sort highly complex data using both more obvious and hidden features.
Ultimately, the computer in the new study could differentiate between EKG readings representing 10 different arrhythmias as well as a normal heart rhythm. It could also tell the difference between irregular heart rhythms and background “noise” caused by interference of one kind or another, such as a jostled or disconnected Zio patch.
For validation, the computer attempted to assign a diagnosis to the EKG readings of 328 additional patients. Independently, several expert cardiologists also read those EKGs and reached a consensus diagnosis for each patient. In almost all cases, the computer’s diagnosis agreed with the consensus of the cardiologists. The computer also made its calls much faster.
Next, the researchers compared the computer’s diagnoses to those of six individual cardiologists who weren’t part of the original consensus committee. And, the results show that the computer actually outperformed these experienced cardiologists!
The findings suggest that artificial intelligence can be used to improve the accuracy and efficiency of EKG readings. In fact, Hannun reports that iRhythm Technologies, maker of the Zio patch, has already incorporated the algorithm into the interpretation now being used to analyze data from real patients.
As impressive as this is, we are surely just at the beginning of AI applications to health and health care. In recognition of the opportunities ahead, NIH has recently launched a working group on AI to explore ways to make the best use of existing data, and harness the potential of artificial intelligence and machine learning to advance biomedical research and the practice of medicine.
Meanwhile, more and more impressive NIH-supported research featuring AI is being published. In my next blog, I’ll highlight a recent paper that uses AI to make a real difference for cervical cancer, particularly in low resource settings.
Reference:
[1] Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY.
Nat Med. 2019 Jan;25(1):65-69.
Links:
Arrhythmia (National Heart, Lung, and Blood Institute/NIH)
Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)
Andrew Ng (Palo Alto, CA)
NIH Support: National Heart, Lung, and Blood Institute
Modeling Hypertrophic Cardiomyopathy in a Dish
Posted on by Dr. Francis Collins
Credit: Zhen Ma, University of California, Berkeley
Researchers have learned in recent years how to grow miniature human hearts in a dish. These “organoids” beat like the real thing and have allowed researchers to model many key aspects of how the heart works. What’s been really tough to model in a dish is how stresses on hearts that are genetically abnormal, such as in inherited familial cardiomyopathies, put people at greater risk for cardiac problems.
Enter the lab-grown human cardiac tissue pictured above. This healthy tissue comprised of the heart’s muscle cells, or cardiomyocytes (green, nuclei in red), was derived from induced pluripotent stem (iPS) cells. These cells are derived from adult skin or blood cells that are genetically reprogrammed to have the potential to develop into many different types of cells, including cardiomyocytes.
Wearable mHealth Device Detects Abnormal Heart Rhythms Earlier
Posted on by Dr. Francis Collins
Caption: Woman wearing a Zio patch
Credit: Adapted from JAMA Network Summary Video
As many as 6 million Americans experience a common type of irregular heartbeat, called atrial fibrillation (AFib), that can greatly increase their risk of stroke and heart failure [1]. There are several things that can be done to lower that risk, but the problem is that a lot of folks have no clue that their heart’s rhythm is out of whack!
So, what can we do to detect AFib and get people into treatment before it’s too late? New results from an NIH-funded study lend additional support to the idea that one answer may lie in wearable health technology: a wireless electrocardiogram (EKG) patch that can be used to monitor a person’s heart rate at home.
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