Discover what the latest science from RECOVER means for our ability to understand, diagnose, prevent, and treat Long COVID.
This page contains descriptions of findings from RECOVER research studies. These descriptions use plain language and a format that is easy to understand.
If you want to learn more about the scientific discoveries described here, you can also browse and search the complete list of RECOVER Publications.
Acute Kidney Injury (AKI) can happen when people get very sick with COVID-19. People in the hospital with COVID-19 are more likely to die if they also get AKI. However, there is not enough research to understand how many people have had AKI since the start of the COVID-19 pandemic or what increases the chances of getting AKI. In this RECOVER study, researchers looked at the electronic health records (EHRs) from 53 hospitals across the United States. They studied adults who were in the hospital with COVID-19 between March 2020 and January 2022. To find out who had AKI, the researchers looked at blood tests that show how well the kidneys are working and diagnosis codes (what doctors use to say what’s wrong with the patient). To understand where people were getting sick, researchers split the country into 4 parts: Northeast, Midwest, South, and West. They also looked at different time periods during the pandemic. Out of 336,473 people in the study, 129,176 (38%) had AKI. People with AKI were also more likely to die than those without AKI. The South and the West had the most cases of AKI. Researchers found that AKI cases went down after the first big wave of COVID-19, but then went back up during the Delta and Omicron waves. This shows that different types of COVID-19 might affect the kidneys differently. This study is important because it helps us understand how COVID-19 can hurt people’s kidneys, and how it can change over time. It also helps us learn how COVID-19 is linked to things like age, sex, race, and other health problems in different areas of the country.
Cognitive issues, such as memory problems and difficulty thinking clearly, may increase the risk of death from COVID-19, especially for people with HIV. Many people with cognitive issues never receive a formal diagnosis. This is particularly true for people with HIV, who face higher rates of HIV-related cognitive issues and age-related thinking problems. Researchers wanted to understand how pre-existing cognitive problems in people with and without HIV affect their risk of death from COVID-19.
The research team studied 64 people with HIV who tested positive for COVID-19 between March 2020 and March 2021. They compared them to 463 people without HIV, matched by age, sex, race, and zip code. First, they checked electronic medical records for dementia diagnoses. Then, they reviewed additional information including HIV characteristics from medical providers and reviewed clinical notes from the year before COVID-19 to identify “cognitive concerns.” These included any documented worries about memory loss, thinking problems, or prescriptions for medications used to treat cognitive symptoms. These data were analyzed to determine the relationship between pre-existing cognitive issues and death after COVID in people with HIV and people without HIV.
In the group of people without cognitive issues, 3.9% of people without HIV and 10% of people with HIV died following COVID infection. However, in the group with cognitive issues, 18% of people without HIV and 40% of people with HIV died after COVID infection. People with HIV who had documented preexisting cognitive issues before infection had roughly a threefold-increased odds of death after COVID infection.
These findings suggest that assessing thinking and memory problems is crucial for COVID-19 care, particularly for people with HIV. Many cognitive issues go undiagnosed, especially in vulnerable populations. Healthcare providers should carefully evaluate cognitive function when determining COVID-19 risk. Better cognitive screening could help identify those at highest risk for severe COVID-19 outcomes.
To understand Long COVID, researchers must be able to figure out which patients have it. Our understanding of Long COVID is evolving and it has been difficult to know who had Long COVID, especially in children. We need a reliable method to identify who might have Long COVID using existing health data.
The purpose of this study was to create and test a computer program, called an algorithm, to find out which children have Long COVID based on their electronic health records (EHRs). EHRs (digital medical charts that have health data like doctor visits, lab results, and other health history) are an important source of data for research studies on Long COVID. The algorithm looks at EHRs to find patterns in the diagnoses, prescribed medications, procedures, and lab tests children received after having COVID-19. These patterns can be described as a phenotype, or a set of measured or visible traits, that can tell us who had Long COVID.
The algorithm correctly identified 67% of the patients who had a Long COVID diagnosis from the EHRs. Among the patients who the algorithm said had Long COVID, 91% had a Long COVID diagnosis. Overall, the algorithm was correct in identifying whether a patient had a Long COVID diagnosis 99% of the time. This means the phenotype can be used to recognize which children have Long COVID in EHR data for future studies, or to screen patients to participate in clinical trials.
This research looked at children with Type 1 Diabetes to understand if getting COVID-19 is related to more hospital stays or emergency department visits for diabetes-related issues such as diabetic ketoacidosis or severe hypoglycemia. Diabetic ketoacidosis happens when the body makes too many blood acids (ketones). Severe hypoglycemia is when blood sugar levels in the body are very low.
The research found that young people with Type 1 Diabetes who got COVID-19 needed to use the healthcare system more and had worse blood sugar levels than those who did not get COVID-19. However, these findings could not confirm whether having COVID-19 will make Type 1 Diabetes symptoms worse or not. The authors of the paper suggest that researchers should keep studying children who have Type 1 Diabetes and get COVID-19.
To find another way to know if children had COVID-19, researchers compared health records of 2 groups of children: children who only had a positive antibody test and children who had a positive PCR test. A positive antibody test means a child had COVID-19 in the past, had the COVID-19 vaccine, or both. A positive PCR test means a child currently has COVID-19. Researchers used data from children’s hospitals in the National Pediatric Learning Health System (PEDSnet) network.
Antibody tests found 2,714 children who had COVID-19 and didn’t have a positive PCR test or COVID-19 vaccine. If researchers used only PCR tests, they wouldn’t have known these children had COVID-19. Knowing if children had COVID-19 is the first step to know their risk of Long COVID and if symptoms could be related to Long COVID.
Having COVID-19 can lead to new symptoms or symptoms that do not go away. This condition is called Long COVID. RECOVER researchers are working to answer questions about Long COVID in adults. To do this, they are studying more than 14,700 adults in the US to understand how common Long COVID is in adults; how the body changes when someone has Long COVID; what makes some people more likely to develop Long COVID, such as where a person lives and their age, race, and sex; and what happens in the body that might cause Long COVID. This paper is important because it can show other scientists how to do their own research on Long COVID in adults.
RECOVER researchers used data from the RECOVER observational cohort study in adults ages 18 and over. They compared symptoms reported in surveys by participants who did and did not have COVID-19. Researchers found 37 symptoms that participants who had COVID-19 reported more often after having COVID-19 compared to participants who never had COVID-19. A combination of 12 of these symptoms helped identify participants with Long COVID, including feeling tired and unwell after activity, feeling weak and tired (fatigue), and brain fog. A definition of Long COVID based on symptoms is important for future research and finding treatments. Read the Research Q&A
A quick way for scientists to identify patterns in a large set of data is by teaching computers to find those patterns for them. To do this, the scientist creates a set of instructions for a computer to follow, called an algorithm, to locate exactly what the scientist is looking for. When that algorithm is plugged into a software program, a computer can run the algorithm many times and learn from it. This process improves the algorithm and it becomes more accurate over time. This is called machine learning. Machine learning can be very helpful and accurate, but it is also challenging to share between researchers because each computer can learn things on its own that can be difficult to recreate. When a computer arrives at an answer that a scientist cannot recreate, it is known as a black-box algorithm.
In order for one research team to share their findings with another research team, they must be able to recreate the steps of the algorithm to “de-black-box" it. This was done by researchers from the National COVID Cohort Collaborative (N3C) as part of the National Institutes of Health (NIH) RECOVER Initiative. The N3C team first identified a phenotype (measured or visible traits) for patients who were at a higher risk of developing Long COVID by creating and training a machine learning-based algorithm. Then, with RECOVER’s support, the N3C researchers worked with researchers from another NIH study called All of Us. They were able to re-create the machine learning-based algorithm, leading to the same phenotype. This means that they were able to “de-black-box" their algorithm. This case can be used as a guide of best practices when sharing algorithm data between research teams. This way, algorithms can be used by many research teams to better understand Long COVID.
An electronic health record (EHR) is a digital medical chart that has health data like doctor visits, lab results, and other information. These data are useful for understanding trends in health information, including how Long COVID affects people. Because of this, EHR data from different settings can be difficult to compare and use in research.
Researchers from the National COVID Cohort Collaborative (N3C) looked at over 15 million EHRs from 75 hospitals and clinics. Their goal was to make data from different healthcare settings more compatible. To do this, they had to understand and describe how definitions of patient visits differ between healthcare settings so the EHR data from different healthcare settings would be compatible.
The researchers focused on identifying patterns in EHR data. They hoped these patterns would help them gain a better understanding of a patient’s complete care experience, including:
To detect these patterns, researchers created two sets of rules for computer processing of EHR data. These rules are called algorithms. The first algorithm allowed researchers to group EHR data in new ways to make it easier to understand how the information in an EHR is related and easier to analyze individual EHRs and to compare EHRs from different sources.
The second algorithm allowed researchers to identify when EHRs indicated that a patient had been admitted to the hospital. Better data about hospitalizations will help future researchers study COVID-19 and its long-term effects, including Long COVID.
By using algorithms, N3C researchers are trying to make large amounts of EHR data more consistent, manageable, and understandable. Algorithms like the two tested by these researchers can help other researchers enhance the quality of EHR data, making it more consistent and capable of producing important insights about conditions like Long COVID.