Discover what the latest science from the RECOVER Initiative 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.
Doctors keep patient information in computer files called electronic health records (EHRs). RECOVER researchers can use these records to learn more about Long COVID, which is when someone feels sick for a long time after having COVID-19. But studying these records is not easy. It can be hard to get certain information from EHRs, when things that might explain why some people get Long COVID happen at different times. It can also be hard to put information from many hospitals together in one place and can cost a lot of money. In this study, RECOVER researchers made a new tool called ODACoR, which stands for “one-shot distributed algorithms for competing risks model.” This tool was used to look at the EHRs of 6.5 million kids and teens from 8 children’s hospitals. Researchers found that ODACoR was able to find information about things that could make children and teens more likely to get Long COVID. ODACoR could also combine information from different hospitals, which did not always work with old ways of studying health information. This tool gave the same results as if all the hospitals had shared all their information in one place, which is hard to do. This study is important because it can help doctors study other kinds of health problems using information from many hospitals.
This RECOVER study looks at how well and how long a COVID-19 vaccine, BNT162b2, works in children and teens. Researchers studied this before and during the wave of a new type of COVID-19, called Omicron. Researchers looked at the electronic health records from a group of children’s health systems across the country, known as PEDSnet. They checked 3 groups: teens ages 12–20 during the earlier Delta wave; and both children ages 5–11 and teens ages 12-20 during the Omicron wave. Researchers looked at data from more than 77,000 teenagers during Delta and over 167,000 kids and teens during Omicron. Some of these kids were vaccinated, and some were not. Researchers compared those who got the first dose of the BNT162b2 vaccine to those who didn't get any COVID-19 vaccine. During Delta, researchers found that the vaccine stopped almost all the teenagers (98%) from getting sick. In the Omicron wave, the vaccine helped stop 74% of the kids and 86% of the teenagers from getting COVID-19. It also helped keep kids from getting really sick and needing to go to the hospital. Children and teens who got vaccinated were also less likely to have heart problems during Omicron. But they found that the vaccines didn’t work as well 4 months after the first dose. This information helps doctors better understand how to keep kids healthy when COVID-19 is going around.
This RECOVER study looked at why some people may have long lasting symptoms or new health problems after getting COVID-19. Researchers looked at the electronic health records of 3.7 million adults and children who were tested for COVID-19 between March 2020 and May 2021. They compared the records of people who tested positive for COVID-19 with people who did not.
The study found that both adults and children who were hospitalized with COVID-19 were more likely to have at least 1 symptom in the months after getting COVID-19, like shortness of breath. They also found that adults who were hospitalized with COVID-19 were more likely to have 3 or more symptoms, feel very tired, or develop a new health condition. Some of the conditions were diabetes, blood disorders, or diseases related to breathing. Even adults with COVID-19 who were not hospitalized had a higher chance of certain symptoms or health issues compared to those who did not have COVID-19. This study is important because it shows that COVID-19 can impact people’s health for a long time. These findings can help doctors and scientists better understand how to treat and care for people recovering from COVID-19.
Research shows that the COVID-19 vaccine lowers the chance of children getting sick from COVID-19. But it is not clear whether the COVID-19 vaccine protects against Long COVID. RECOVER researchers did a study to look at how well vaccines work in protecting children, ages 5–17, against Long COVID. They studied data from a group of more than 1 million children. The vaccine was 42% effective in lowering the chance of getting Long COVID in kids ages 5–11 and 50% effective for kids ages 12–17. The vaccine works best against Long COVID within the first 6 months of getting it. After 6 months, it does not work as well, so getting the vaccine every year is important to prevent Long COVID. These results show that the COVID-19 vaccine can help children 5 years and older to keep from getting very sick. While this study helps scientists understand how vaccines can protect against COVID-19, they still need to do more research to understand how they protect against Long COVID.
RECOVER researchers studied how multisystem inflammatory syndrome (MIS-C) looks different in a large group of children. The researchers looked at electronic health record (EHR) data from 8 children’s hospitals from March 2020 to September 2022. They studied 1,139 children who were patients hospitalized for MIS-C. The researchers divided the patients into 3 groups and used age, sex, location, symptoms, conditions, test results, and medications to look for patterns in these groups. Researchers then looked at how the groups were similar and how they changed over time. The study showed that MIS-C has a range of severity, from mild to medium and severe. This means that MIS-C symptoms can be different for each child. The researchers also found that there are fewer severe MIS-C cases now than there used to be. The study might not have included some children if they had symptoms like MIS-C but had a different illness, had Kawasaki disease when they were in the hospital (because of how similar the symptoms can be to MIS-C), or had mild MIS-C symptoms that did not bother them that much and may not have been written in their EHR. This study can help doctors better understand and treat MIS-C.
An electronic health record, or EHR, is a digital file that contains information about a person’s healthcare. In this study, researchers studied anonymous EHR data from the National COVID Cohort Collaborative (N3C), a group of medical systems located across the United States. They compared a group of 8,325 people who had a Long COVID diagnosis or received healthcare at a Long COVID clinic to a group of 41,625 people who did not have Long COVID. The researchers found key differences between these 2 groups. They also identified risk factors that could make someone more likely to develop Long COVID, including being middle-aged (40 to 69 years old), being female, and having certain chronic (long-term) health conditions before they got COVID-19. Examples of these chronic health conditions were obesity and lung disease. More research is needed to better understand why and how the risk factors found in this study make someone more likely to develop Long COVID.
Researchers wanted to see if patients who had difficulty breathing while sleeping (Obstructive Sleep Apnea; OSA) were at higher risk for developing Long COVID compared to people without OSA. By comparing the electronic health record (EHR) data of COVID-19 positive patients, researchers looked at the risk of developing Long COVID in patients with and without a previous diagnosis of OSA.
Researchers found that adults with a previous diagnosis of OSA had an increased chance of developing Long COVID when compared to patients that did not have a previous diagnosis of OSA.
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.
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.
