Researching COVID to Enhance Recovery
Posted on by Gary Gibbons, M.D., National Heart, Lung, and Blood Institute; Walter Koroshetz, M.D., National Institute of Neurological Disorders and Stroke; Hugh Auchincloss, M.D., National Institute of Allergy and Infectious Diseases
“I connected with RECOVER to be a part of the answers that I was looking for when I was at my worst.” Long COVID patient and RECOVER representative, Nitza Rochez (Bronx, NY)
People, like Nitza Rochez, who are living with Long COVID—the wide-ranging health issues that can follow an infection with SARS-CoV-2, the coronavirus that causes COVID-19—experience disabling symptoms with significant physical, emotional and financial consequences.
The NIH has been engaging and listening to Nitza and others living with Long COVID even before the start of its Researching COVID to Enhance Recovery (RECOVER) Initiative. But now, with the launch of RECOVER, patients and those with affected family or community members have joined researchers, clinicians, and experts in their efforts to unlock the mysteries of Long COVID. All have come together to understand what causes the condition, identify who is most at risk, and determine how to prevent and treat it.
RECOVER is unprecedented in its size and scope as the most-diverse, deeply characterized cohort of Long COVID patients. We’ve enlisted the help of many patient volunteers, who have enrolled in observational studies designed to help researchers learn as much as possible about people who have Long COVID.
Indeed, thousands of research participants are now providing health information and undergoing in-depth medical evaluations and tests, enabling investigators to look for trends. Additionally, studies of millions of electronic medical records are providing insights about those who have received care during the pandemic. More than 40 studies are being conducted to identify the causes of disease, potential biomarkers of Long COVID, and new therapeutic targets.
In all, RECOVER’s research assets are voluminous. They involve invaluable contributions from many people and communities, including research volunteers, research investigators, and clinical specialists. In addition, millions of health records and numerous related tissues and specimens are being analyzed for possible leads.
At the center of it all is the National Community Engagement Group (NCEG). The NCEG is comprised of people living with Long COVID and those representing others living with the condition, and it is truly instrumental to the initiative’s progress in understanding how and why SARS-CoV-2 impacts people in different ways. It’s also helping researchers learn why some people recover while others do not.
So far, we’ve learned that people hospitalized with COVID-19 are twice as likely to have Long COVID than those who were not hospitalized for infection. We’ve also learned that members of racial and ethnic minority groups with Long COVID were more likely to have been hospitalized with COVID-19.
Similarly, disparities in Long COVID exist within those living in areas with particular environmental exposures , and those who were already burdened by other diseases and conditions—such as diabetes and chronic pulmonary disease . We’ve also discovered that the certain types of symptoms of Long COVID are consistent among patients regardless of which SARS-CoV-2 variant caused their initial infection. Yet, people infected with the earlier variants have a higher number of symptoms than those infected with more recent variants.
Patient experiences have guided and will continue to guide the study designs and trajectory of RECOVER. Now, fueled by the knowledge that we have gained, RECOVER is preparing to advance to the next phase of discovery—testing interventions in clinical trials to see if they can help people with Long COVID.
To prepare, we are beginning to identify potential clinical trial sites. This important step will help us to find the right places with the right staff and capabilities for enrolling the appropriate patient populations needed to implement the studies. We’ll ensure that the public knows when these upcoming clinical trials are ready to enroll.
Of course, the design of these RECOVER clinical trials will be critical, and insights gained from patients have been key in this process. Results from RECOVER study questionnaires, surveys, and discussions with people experiencing Long COVID identified symptom clusters considered to be the most significant and burdensome to patients. These include sleep disorders, “brain fog” (trouble thinking clearly), exercise intolerance and fatigue, and nervous system dysfunction affecting people’s ability to regulate normal body functions like heart rate and body temperature.
These patient observations have effectively guided the design of the clinical trials that will evaluate whether certain interventions and therapies can help alleviate symptoms that are part of these specific clusters. We’re excited to be advancing toward this phase of the initiative and, again, are very grateful to patient representatives like Nitza, quoted above, for getting us to this phase.
Effective evaluation of those treatments will be important, too. Early in the pandemic, while many clinical trials were launching, most were not large enough or did not have the appropriate objectives to define effective treatments for acute COVID-19. This left clinicians with few clear options when faced with patients needing help.
Learning from this experience, the RECOVER trials will be harmonized to ensure coordinated and efficient evaluation of interventions—in other words, all potential therapies will be using the same protocols platforms and the same data elements. This consistency accelerates our understanding and strengthens the certainty of findings.
Given the widespread and diverse impact that the virus has on the body, it is highly likely that more than one treatment will be needed for each kind of patient experience. Finding solutions for everyone—people of all races, ethnicities, genders, ages, and geographic locations—is paramount.
RECOVER patient representative, Juan Lewis, of San Antonio shared with us, “In April 2020, I was fighting for my life, and today I fight for my quality of life. COVID impacted me physically, mentally, socially, and financially.”
For people like Juan who are experiencing debilitating Long COVID symptoms, we know that finding answers as quickly as possible is critical. As we look ahead to the next 12 months, we’ll continue the studies evaluating the underlying causes, risk factors, and outcomes of Long Covid, and we anticipate significant scientific progress on research leading to Long COVID treatments.
Keep an eye on the RECOVER website for updates on our progress, and published findings.
 Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover program. Zhang Y, Hu H, Fokaidis V, V CL, Xu J, Zang C, Xu Z, Wang F, Koropsak M, Bian J, Hall J, Rothman RL, Shenkman EA, Wei WQ, Weiner MG, Carton TW, Kaushal R. Environ Adv. 2023 Apr;11:100352.
 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.
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
Understanding Long-Term COVID-19 Symptoms and Enhancing Recovery, NIH Director’s Blog, October 4, 2022.
NIH RECOVER Research Identifies Potential Long COVID Disparities. NIH News Release, February 16, 2023.
NIH RECOVER Listening Session, June 2021 (NIH Videocast)
NIH RECOVER Listening Session: Understanding Long COVID Across Communities of Color and Those Hardest Hit by COVID, January 21, 2022 (NIH Videocast)
Note: Dr. Lawrence Tabak, who performs the duties of the NIH Director, has asked the heads of NIH’s Institutes, Centers, and Offices to contribute occasional guest posts to the blog to highlight some of the interesting science that they support and conduct. This is the 25th in the series of NIH guest posts that will run until a new permanent NIH director is in place.
Posted on by Lawrence Tabak, D.D.S., Ph.D.
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 . 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.
 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.
 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.
COVID-19 Research (NIH)
National COVID Cohort Collaborative (N3C) (National Center for Advancing Translational Sciences/NIH)
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