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Using AI to Advance Understanding of Long COVID Syndrome

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The COVID-19 pandemic continues to present considerable public health challenges in the United States and around the globe. One of the most puzzling is why many people who get over an initial and often relatively mild COVID illness later develop new and potentially debilitating symptoms. These symptoms run the gamut including fatigue, shortness of breath, brain fog, anxiety, and gastrointestinal trouble.

People understandably want answers to help them manage this complex condition referred to as Long COVID syndrome. But because Long COVID is so variable from person to person, it’s extremely difficult to work backwards and determine what these people had in common that might have made them susceptible to Long COVID. The variability also makes it difficult to identify all those who have Long COVID, whether they realize it or not. But a recent study, published in the journal Lancet Digital Health, shows that a well-trained computer and its artificial intelligence can help.

Researchers found that computers, after scanning thousands of electronic health records (EHRs) from people with Long COVID, could reliably make the call. The results, though still preliminary and in need of further validation, point the way to developing a fast, easy-to-use computer algorithm to help determine whether a person with a positive COVID test is likely to battle Long COVID.

In this groundbreaking study, NIH-supported researchers led by Emily Pfaff, University of North Carolina, Chapel Hill, and Melissa Haendel, the University of Colorado Anschutz Medical Campus, Aurora, relied on machine learning. In machine learning, a computer sifts through vast amounts of data to look for patterns. One reason machine learning is so powerful is that it doesn’t require humans to tell the computer which features it should look for. As such, machine learning can pick up on subtle patterns that people would otherwise miss.

In this case, Pfaff, Haendel, and team decided to “train” their computer on EHRs from people who had reported a COVID-19 infection. (The records are de-identified to protect patient privacy.) The researchers found just what they needed in the National COVID Cohort Collaborative (N3C), a national, publicly available data resource sponsored by NIH’s National Center for Advancing Translational Sciences. It is part of NIH’s Researching COVID to Enhance Recovery (RECOVER) initiative, which aims to improve understanding of Long COVID.

The researchers defined a group of more than 1.5 million adults in N3C who either had been diagnosed with COVID-19 or had a record of a positive COVID-19 test at least 90 days prior. Next, they examined common features, including any doctor visits, diagnoses, or medications, from the group’s roughly 100,000 adults.

They fed that EHR data into a computer, along with health information from almost 600 patients who’d been seen at a Long COVID clinic. They developed three machine learning models: one to identify potential long COVID patients across the whole dataset and two others that focused separately on people who had or hadn’t been hospitalized.

All three models proved effective for identifying people with potential Long-COVID. Each of the models had an 85 percent or better discrimination threshold, indicating they are highly accurate. That’s important because, once researchers can identify those with Long COVID in a large database of people such as N3C, they can begin to ask and answer many critical questions about any differences in an individual’s risk factors or treatment that might explain why some get Long COVID and others don’t.

This new study is also an excellent example of N3C’s goal to assemble data from EHRs that enable researchers around the world to get rapid answers and seek effective interventions for COVID-19, including its long-term health effects. It’s also made important progress toward the urgent goal of the RECOVER initiative to identify people with or at risk for Long COVID who may be eligible to participate in clinical trials of promising new treatment approaches.

Long COVID remains a puzzling public health challenge. Another recent NIH study published in the journal Annals of Internal Medicine set out to identify people with symptoms of Long COVID, most of whom had recovered from mild-to-moderate COVID-19 [2]. More than half had signs of Long COVID. But, despite extensive testing, the NIH researchers were unable to pinpoint any underlying cause of the Long COVID symptoms in most cases.

So if you’d like to help researchers solve this puzzle, RECOVER is now enrolling adults and kids—including those who have and have not had COVID—at more than 80 study sites around the country.

References:

[1] Identifying who has long COVID in the USA: a machine learning approach using N3C data. Pfaff ER, Girvin AT, Bennett TD, Bhatia A, Brooks IM, Deer RR, Dekermanjian JP, Jolley SE, Kahn MG, Kostka K, McMurry JA, Moffitt R, Walden A, Chute CG, Haendel MA; N3C Consortium. Lancet Digit Health. 2022 May 16:S2589-7500(22)00048-6.

[2] A longitudinal study of COVID-19 sequelae and immunity: baseline findings. Sneller MC, Liang CJ, Marques AR, Chung JY, Shanbhag SM, Fontana JR, Raza H, Okeke O, Dewar RL, Higgins BP, Tolstenko K, Kwan RW, Gittens KR, Seamon CA, McCormack G, Shaw JS, Okpali GM, Law M, Trihemasava K, Kennedy BD, Shi V, Justement JS, Buckner CM, Blazkova J, Moir S, Chun TW, Lane HC. Ann Intern Med. 2022 May 24:M21-4905.

Links:

COVID-19 Research (NIH)

National COVID Cohort Collaborative (N3C) (National Center for Advancing Translational Sciences/NIH)

RECOVER Initiative

Emily Pfaff (University of North Carolina, Chapel Hill)

Melissa Haendel (University of Colorado, Aurora)

NIH Support: National Center for Advancing Translational Sciences; National Institute of General Medical Sciences; National Institute of Allergy and Infectious Diseases


All of Us: Release of Nearly 100,000 Whole Genome Sequences Sets Stage for New Discoveries

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Diverse group of cartoon people with associated DNA

Nearly four years ago, NIH opened national enrollment for the All of Us Research Program. This historic program is building a vital research community within the United States of at least 1 million participant partners from all backgrounds. Its unifying goal is to advance precision medicine, an emerging form of health care tailored specifically to the individual, not the average patient as is now often the case. As part of this historic effort, many participants have offered DNA samples for whole genome sequencing, which provides information about almost all of an individual’s genetic makeup.

Earlier this month, the All of Us Research Program hit an important milestone. We released the first set of nearly 100,000 whole genome sequences from our participant partners. The sequences are stored in the All of Us Researcher Workbench, a powerful, cloud-based analytics platform that makes these data broadly accessible to registered researchers.

The All of Us Research Program and its many participant partners are leading the way toward more equitable representation in medical research. About half of this new genomic information comes from people who self-identify with a racial or ethnic minority group. That’s extremely important because, until now, over 90 percent of participants in large genomic studies were of European descent. This lack of diversity has had huge impacts—deepening health disparities and hindering scientific discovery from fully benefiting everyone.

The Researcher Workbench also contains information from many of the participants’ electronic health records, Fitbit devices, and survey responses. Another neat feature is that the platform links to data from the U.S. Census Bureau’s American Community Survey to provide more details about the communities where participants live.

This unique and comprehensive combination of data will be key in transforming our understanding of health and disease. For example, given the vast amount of data and diversity in the Researcher Workbench, new diseases are undoubtedly waiting to be uncovered and defined. Many new genetic variants are also waiting to be identified that may better predict disease risk and response to treatment.

To speed up the discovery process, these data are being made available, both widely and wisely. To protect participants’ privacy, the program has removed all direct identifiers from the data and upholds strict requirements for researchers seeking access. Already, more than 1,500 scientists across the United States have gained access to the Researcher Workbench through their institutions after completing training and agreeing to the program’s strict rules for responsible use. Some of these researchers are already making discoveries that promote precision medicine, such as finding ways to predict how to best to prevent vision loss in patients with glaucoma.

Beyond making genomic data available for research, All of Us participants have the opportunity to receive their personal DNA results, at no cost to them. So far, the program has offered genetic ancestry and trait results to more than 100,000 participants. Plans are underway to begin sharing health-related DNA results on hereditary disease risk and medication-gene interactions later this year.

This first release of genomic data is a huge milestone for the program and for health research more broadly, but it’s also just the start. The program’s genome centers continue to generate the genomic data and process about 5,000 additional participant DNA samples every week.

The ultimate goal is to gather health data from at least 1 million or more people living in the United States, and there’s plenty of time to join the effort. Whether you would like to contribute your own DNA and health information, engage in research, or support the All of Us Research Program as a partner, it’s easy to get involved. By taking part in this historic program, you can help to build a better and more equitable future for health research and precision medicine.

Note: Joshua Denny, M.D., M.S., is the Chief Executive Officer of NIH’s All of Us Research Program.

Links:

All of Us Research Program (NIH)

All of Us Research Hub

Join All of Us (NIH)


Preventing Glaucoma Vision Loss with ‘Big Data’

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Credit: University of California San Diego

Each morning, more than 2 million Americans start their rise-and-shine routine by remembering to take their eye drops. The drops treat their open-angle glaucoma, the most-common form of the disease, caused by obstructed drainage of fluid where the eye’s cornea and iris meet. The slow drainage increases fluid pressure at the front of the eye. Meanwhile, at the back of the eye, fluid pushes on the optic nerve, causing its bundled fibers to fray and leading to gradual loss of side vision.

For many, the eye drops help to lower intraocular pressure and prevent vision loss. But for others, the drops aren’t sufficient and their intraocular pressure remains high. Such people will need next-level care, possibly including eye surgery, to reopen the clogged drainage ducts and slow this disease that disproportionately affects older adults and African Americans over age 40.

Sally Baxter
Credit: University of California San Diego

Sally Baxter, a physician-scientist with expertise in ophthalmology at the University of California, San Diego (UCSD), wants to learn how to predict who is at greatest risk for serious vision loss from open-angle and other forms of glaucoma. That way, they can receive more aggressive early care to protect their vision from this second-leading cause of blindness in the U.S..

To pursue this challenging research goal, Baxter has received a 2020 NIH Director’s Early Independence Award. Her research will build on the clinical observation that people with glaucoma frequently battle other chronic health problems, such as high blood pressure, diabetes, and heart disease. To learn more about how these and other chronic health conditions might influence glaucoma outcomes, Baxter has begun mining a rich source of data: electronic health records (EHRs).

In an earlier study of patients at UCSD, Baxter showed that EHR data helped to predict which people would need glaucoma surgery within the next six months [1]. The finding suggested that the EHR, especially information on a patient’s blood pressure and medications, could predict the risk for worsening glaucoma.

In her NIH-supported work, she’s already extended this earlier “Big Data” finding by analyzing data from more than 1,200 people with glaucoma who participate in NIH’s All of Us Research Program [2]. With consent from the participants, Baxter used their EHRs to train a computer to find telltale patterns within the data and then predict with 80 to 99 percent accuracy who would later require eye surgery.

The findings confirm that machine learning approaches and EHR data can indeed help in managing people with glaucoma. That’s true even when the EHR data don’t contain any information specific to a person’s eye health.

In fact, the work of Baxter and other groups have pointed to an especially important role for blood pressure in shaping glaucoma outcomes. Hoping to explore this lead further with the support of her Early Independence Award, Baxter also will enroll patients in a study to test whether blood-pressure monitoring smart watches can add important predictive information on glaucoma progression. By combining round-the-clock blood pressure data with EHR data, she hopes to predict glaucoma progression with even greater precision. She’s also exploring innovative ways to track whether people with glaucoma use their eye drops as prescribed, which is another important predictor of the risk of irreversible vision loss [3].

Glaucoma research continues to undergo great progress. This progress ranges from basic research to the development of new treatments and high-resolution imaging technologies to improve diagnostics. But Baxter’s quest to develop practical clinical tools hold great promise, too, and hopefully will help one day to protect the vision of millions of people with glaucoma around the world.

References:

[1] Machine learning-based predictive modeling of surgical intervention in glaucoma using systemic data from electronic health records. Baxter SL, Marks C, Kuo TT, Ohno-Machado L, Weinreb RN. Am J Ophthalmol. 2019 Dec; 208:30-40.

[2] Predictive analytics for glaucoma using data from the All of Us Research Program. Baxter SL, Saseendrakumar BR, Paul P, Kim J, Bonomi L, Kuo TT, Loperena R, Ratsimbazafy F, Boerwinkle E, Cicek M, Clark CR, Cohn E, Gebo K, Mayo K, Mockrin S, Schully SD, Ramirez A, Ohno-Machado L; All of Us Research Program Investigators. Am J Ophthalmol. 2021 Jul;227:74-86.

[3] Smart electronic eyedrop bottle for unobtrusive monitoring of glaucoma medication adherence. Aguilar-Rivera M, Erudaitius DT, Wu VM, Tantiongloc JC, Kang DY, Coleman TP, Baxter SL, Weinreb RN. Sensors (Basel). 2020 Apr 30;20(9):2570.

Links:

Glaucoma (National Eye Institute/NIH)

All of Us Research Program (NIH)

Video: Sally Baxter (All of Us Research Program)

Sally Baxter (University of California San Diego)

Baxter Project Information (NIH RePORTER)

NIH Director’s Early Independence Award (Common Fund)

NIH Support: Common Fund


Decoding Heart-Brain Talk to Prevent Sudden Cardiac Deaths

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Deeptankar DeMazundar in a white doctor's coat
Credit: Colleen Kelley/UC Creative + Brand

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


NIH’s All of Us Program Joins Fight Against COVID-19

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We’ve learned so much about coronavirus disease 2019 (COVID-19), but there’s still much more that we need to learn in order to defeat this devastating pandemic. Among the critical questions: why do some young people who appear healthy and have no history of chronic disease get very sick from the virus? And why do some people in their 80s or 90s seemingly just shrug off the infection? There’s something going on biologically, but we don’t yet have the answers.

We do, however, have some resources that will enable us to examine lots of data in search of biological clues. One of them is NIH’s All of Us Research Program, which is seeking the help of 1 million people to build one of the most diverse health databases in our nation’s history. Two years after its national launch, the program already has enrolled nearly 350,000 diverse participants from across the United States.

As its name suggests, All of Us is open to all people over age 18 in communities all around the country. An important strength of the effort has been welcoming participants from all backgrounds. Indeed, about 75 percent of people who have volunteered for the program come from groups that have traditionally been underrepresented in medical research. That includes people from many racial and ethnic minority groups, as well as those of many different ages, socioeconomic backgrounds, and geographic locations, including remote and rural areas.

Because of COVID-19 and the need for physical distancing to curb the spread of the potentially deadly virus, All of Us has been forced to halt temporarily all in-person appointments. But program leaders, including Josh Denny, chief executive officer of All of Us, and Kelly Gebo, the program’s chief medical and scientific officer, saw an opportunity to roll up their sleeves and help during this unprecedented public health challenge. In fact, Gebo reports that they’d already been hearing from many of their participant partners that they wanted to be a part of the solution to the COVID-19 pandemic.

To rise to this challenge, the All of Us Research Program has just announced three initiatives to assist the scientific community in seeking new insights into COVID-19. The program will:

• Test blood samples from 10,000 or more participants for the presence of SARS-CoV-2 antibodies, indicating prior infection. The testing will start on samples collected in March 2020 and work backward until positive tests are no longer found. This will show the prevalence of novel coronavirus exposure among All of Us participants from across the country, allowing researchers to sift through the data and assess the varying rates and timing of infections across regions and communities.

• Rapidly collect relevant information from more than 200,000 participants who have shared their electronic health records. A number of those participants have already either been diagnosed with COVID-19 or sought health care for related symptoms. The program is working to standardize this information. It will help researchers look for patterns and learn more about COVID-19 symptoms and associated health problems, as well as the effects of different medicines and treatments.

• Deploy a new online survey to understand better the effects of the COVID-19 pandemic on participants’ physical and mental health. This 20- to 30-minute survey is designed both for participants who have been ill with COVID-19 and those who have not knowingly been infected. Questions will be included on COVID-19 symptoms, stress, social distancing and the economic impacts of the pandemic. Participants are invited to take the survey each month until the pandemic ends, so researchers can study the effects of COVID-19 over time and begin to better understand how and why COVID-19 affects people differently.

As this data becomes available, researchers will look for new leads to inform our efforts to bring greater precision to the diagnosis, treatment, and prevention of COVID-19, including for those communities that have been hit the hardest. Another hope is that what is learned about COVID-19 through All of Us and other NIH-supported research will provide us with the knowledge and tools we need to avert future pandemics,

In case you’re wondering, I happen to be among the thousands of people who’ve already volunteered to take part in All of Us. If you’d like to get involved too, new participants are always welcome to join.

Links:

Coronavirus (COVID-19) (NIH)

All of Us Research Program (NIH)

Join All of Us (NIH)


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