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Study Shows Benefits of COVID-19 Vaccines and Boosters

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Diverse group of smiling adults with band-aids on their shoulders
Credit: Shutterstock/Semanche

As colder temperatures settle in and people spend more time gathered indoors, cases of COVID-19 and other respiratory illnesses almost certainly will rise. That’s why, along with scheduling your annual flu shot, it’s now recommended that those age 5 and up should get an updated COVID-19 booster shot [1,2]. Not only will these new boosters guard against the original strain of the coronavirus that started the pandemic, they will heighten your immunity to the Omicron variant and several of the subvariants that continue to circulate in the U.S. with devastating effects.

At last count, about 14.8 million people in the U.S.—including me—have rolled up their sleeves to receive an updated booster shot [3]. It’s a good start, but it also means that most Americans aren’t fully up to date on their COVID-19 vaccines. If you or your loved ones are among them, a new study may provide some needed encouragement to make an appointment at a nearby pharmacy or clinic to get boosted [4].

A team of NIH-supported researchers found a remarkably low incidence of severe COVID-19 illness last fall, winter, and spring among more than 1.6 million veterans who’d been vaccinated and boosted. Severe illness was also quite low in individuals without immune-compromising conditions.

These latest findings, published in the journal JAMA, come from a research group led by Dan Kelly, University of California, San Francisco. He and his team conducted their study drawing on existing health data from the Veterans Health Administration (VA) within a time window of July 2021 and May 2022.

They identified 1.6 million people who’d had a primary-care visit within the last two years and were fully vaccinated for COVID-19, which included receiving a booster shot. Almost three-quarters of those identified were 65 and older. Nearly all were male, and more than 70 percent had another pre-existing health condition that put them at greater risk of becoming seriously ill from a COVID-19 infection.

Over a 24-week follow-up period for each fully vaccinated individual, 125 per 10,000 people had a breakthrough infection. That’s about 1 percent. Just 8.9 in 10,000 fully vaccinated people—less than 0.1 percent—died or were hospitalized from COVID-19 pneumonia. Drilling down deeper into the data:

• Individuals with an immune-compromising condition had a very low rate of hospitalization or death. In this group, 39.6 per 10,000 people had a serious breakthrough infection. That translates to 0.3 percent.

• For people with other preexisting health conditions, including diabetes and heart disease, hospitalization or death totaled 0.07 percent, or 6.7 per 10,000 people.

• For otherwise healthy adults aged 65 and older, the incidence of hospitalization or death was 1.9 per 10,000 people, or 0.02 percent.

• For boosted participants 65 or younger with no high-risk conditions, hospitalization or death came to less than 1 per 10,000 people. That comes to less than 0.01 percent.

It’s worth noting that these results reflect a period when the Delta and Omicron variants were circulating, and available boosters still were based solely on the original variant. Heading into this winter, the hope is that the updated “bivalent” boosters from Pfizer and Moderna will offer even broader protection as this terrible virus continues to evolve.

The Centers for Disease Control and Prevention continues to recommend that everyone stay up to date with their COVID-19 vaccines. That means all adults and kids 5 and older are encouraged to get boosted if it has been at least two months since their last COVID-19 vaccine dose. For older people and those with other health conditions, it’s even more important given their elevated risk for severe illness.

What if you’ve had a COVID-19 infection recently? Getting vaccinated or boosted a few months after you’ve had a COVID-19 infection will offer you even better protection in the future.

So, if you are among the millions of Americans who’ve been vaccinated for COVID-19 but are now due for a booster, don’t delay. Get yourself boosted to protect your own health and the health of your loved ones as the holidays approach.

References:

[1] CDC recommends the first updated COVID-19 booster. Centers for Disease Control and Prevention. September 1, 2022.

[2] CDC expands updated COVID-19 vaccines to include children ages 5 through 11. Centers for Disease Control and Prevention, October 12, 2022.

[3] COVID-19 vaccinations in the United States. Centers for Disease Control and Prevention.

[4] Incidence of severe COVID-19 illness following vaccination and booster with BNT162b2, mRNA-1273, and Ad26.COV2.S vaccines. Kelly JD, Leonard S, Hoggatt KJ, Boscardin WJ, Lum EN, Moss-Vazquez TA, Andino R, Wong JK, Byers A, Bravata DM, Tien PC, Keyhani S. JAMA. 2022 Oct 11;328(14):1427-1437.

Links:

COVID-19 Research (NIH)

Dan Kelly (University of California, San Francisco)

NIH Support: National Institute of Allergy and Infectious Diseases


Understanding Long-Term COVID-19 Symptoms and Enhancing Recovery

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RECOVER: Researching COVID to Enhance Recovery. An Initiative Funded by the National Institutes of Health

We are in the third year of the COVID-19 pandemic, and across the world, most restrictions have lifted, and society is trying to get back to “normal.” But for many people—potentially millions globally—there is no getting back to normal just yet.

They are still living with the long-term effects of a COVID-19 infection, known as the post-acute sequelae of SARS-CoV-2 infection (PASC), including Long COVID. These people continue to experience debilitating fatigue, shortness of breath, pain, difficulty sleeping, racing heart rate, exercise intolerance, gastrointestinal and other symptoms, as well as cognitive problems that make it difficult to perform at work or school.

This is a public health issue that is in desperate need of answers. Research is essential to address the many puzzling aspects of Long COVID and guide us to effective responses that protect the nation’s long-term health.

For the past two years, NIH’s National Heart, Lung, and Blood Institute (NHLBI), the National Institute of Allergy and Infectious Diseases (NIAID), and my National Institute of Neurological Disorders and Stroke (NINDS) along with several other NIH institutes and the office of the NIH Director, have been leading NIH’s Researching COVID to Enhance Recovery (RECOVER) initiative, a national research program to understand PASC.

The initiative studies core questions such as why COVID-19 infections can have lingering effects, why new symptoms may develop, and what is the impact of SARS-CoV-2, the virus that causes COVID-19, on other diseases and conditions? Answering these fundamental questions will help to determine the underlying biologic basis of Long COVID. The answers will also help to tell us who is at risk for Long COVID and identify therapies to prevent or treat the condition.

The RECOVER initiative’s wide scope of research is also unprecedented. It is needed because Long COVID is so complex, and history indicates that similar post infectious conditions have defied definitive explanation or effective treatment. Indeed, those experiencing Long COVID report varying symptoms, making it highly unlikely that a single therapy will work for everyone, underscoring the need to pursue multiple therapeutic strategies.

To understand Long COVID fully, hundreds of RECOVER investigators are recruiting more than 17,000 adults (including pregnant people) and more than 18,000 children to take part in cohort studies. Hundreds of enrolling sites have been set up across the country. An autopsy research cohort will also provide further insight into how COVID-19 affects the body’s organs and tissues.

In addition, researchers will analyze electronic health records from millions of people to understand how Long COVID and its symptoms change over time. The RECOVER initiative is also utilizing consistent research protocols across all the study sites. The protocols have been carefully developed with input from patients and advocates, and they are designed to allow for consistent data collection, improve data sharing, and help to accelerate the pace of research.

From the very beginning, people suffering from Long COVID have been our partners in RECOVER. Patients and advocates have contributed important perspectives and provided valuable input into the master protocols and research plans.

Now, with RECOVER underway, individuals with Long COVID, their caregivers, and community members continue to serve a critical role in the Initiative. The National Community Engagement Group (NCEG) has been established to make certain that RECOVER meets the needs of all people affected by Long COVID. The RECOVER Patient and Community Engagement Strategy outlines all the approaches that RECOVER is using to engage with and gather input from individuals impacted by Long COVID.

The NIH recently made more than 40 awards to improve understanding of the underlying biology and pathology of Long COVID. There have already been several important findings published by RECOVER scientists.

For example, in a recent study published in the journal Lancet Digital Health, RECOVER investigators used machine learning to comb through electronic health records to look for signals that may predict whether someone has Long COVID [1]. As new findings, tools, and technologies continue to emerge that help advance our knowledge of the condition, the RECOVER Research Review (R3) Seminar Series will provide a forum for researchers and our partners with up-to-date information about Long COVID research.

It is important to note that post-viral conditions are not a new concept. Many, but not all, of the symptoms reported in Long COVID, including fatigue, post-exertional malaise, chronic musculoskeletal pain, sleep disorders, postural orthostatic tachycardia (POTS), and cognitive issues, overlap with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).

ME/CFS is a serious disease that can occur following infection and make people profoundly sick for decades. Like Long COVID, ME/CFS is a heterogenous condition that does not affect everybody in the same way, and the knowledge gained through research on Long COVID may also positively impact the understanding, treatment, and prevention of POTS, ME/CFS, and other chronic diseases.

Unlike other post-viral conditions, people who experience Long COVID were all infected by the same virus—albeit different variants—at a similar point in time. This creates a unique opportunity for RECOVER researchers to study post-viral conditions in real-time.

The opportunity enables scientists to study many people simultaneously while they are still infected to monitor their progress and recovery, and to try to understand why some individuals develop ongoing symptoms. A better understanding of the transition from acute to chronic disease may offer an opportunity to intervene, identify who is at risk of the transition, and develop therapies for people who experience symptoms long after the acute infection has resolved.

The RECOVER initiative will soon announce clinical trials, leveraging data from clinicians and patients in which symptom clusters were identified and can be targeted by various interventions. These trials will investigate therapies that are indicated for other non-COVID conditions and novel treatments for Long COVID.

Through extensive collaboration across the multiple NIH institutes and offices that contribute to the RECOVER effort, our hope is critical answers will emerge soon. These answers will help us to recognize the full range of outcomes and needs resulting from PASC and, most important, enable many people to make a full recovery from COVID-19. We are indebted to the over 10,000 subjects who have already enrolled in RECOVER. Their contributions and the hard work of the RECOVER investigators offer hope for the future to the millions still suffering from the pandemic.

Reference:

[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 Jul;4(7):e532-e541.

Links:

COVID-19 Research (NIH)

Long COVID (NIH)

RECOVER: Researching COVID to Enhance Recovery (NIH)

NIH builds large nationwide study population of tens of thousands to support research on long-term effects of COVID-19,” NIH News Release, September 15, 2021.

Director’s Messages (National Institute of Neurological Disorders and Stroke/NIH)

Note: Dr. Lawrence Tabak, who performs the duties of the NIH Director, has asked the heads of NIH’s Institutes and Centers (ICs) to contribute occasional guest posts to the blog to highlight some of the interesting science that they support and conduct. This is the 18th in the series of NIH IC guest posts that will run until a new permanent NIH director is in place.


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


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