Research Summaries

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a condition that affects different parts of the body, including the brain, the immune system, and the autonomic nervous system, which controls things like heart rate and blood pressure. Many people with Long COVID experience similar symptoms to people with ME/CFS, like feeling more tired after physical or mental effort, which is called post-exertional malaise (PEM). 

This paper summarizes how researchers can use what they have learned about ME/CFS over the past 4 decades to better understand Long COVID. Researchers have made progress in explaining and categorizing known ME/CFS symptoms and findings, coming up with new ways to test for the illness and developing more reliable and standardized assessments (tests) researchers can use. These assessments check how well a person’s body and mind are working. By building on this existing research, RECOVER scientists hope to help other researchers better define and diagnose Long COVID, determine whether someone with Long COVID is likely to get better, get worse, or stay about the same, and identify treatments that could be studied in future clinical trials. This existing research can also help scientists create specific testing tools to help them get consistent information no matter where the test is done.

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 pregnant women. To do this, they are studying 2,300 pregnant women and their babies in the US to understand how often pregnant women get Long COVID; 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. RECOVER researchers are also studying how COVID-19 during pregnancy affects child development. This paper is important because it can show other scientists how to do their own research on Long COVID in pregnant women.

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.

Long COVID patients, caregivers, and community members contribute to all parts of RECOVER, including scientific research. These chosen members of the community are known as RECOVER Representatives. This paper, written by a team of RECOVER Representatives and researchers, talks about why they chose to work together and how their partnership has improved the study. They summarize some of the impacts of Long COVID and highlight the roles that patient advocacy can play in research. They also discuss what progress has been made so far in studying Long COVID and what challenges lie ahead. The authors state that the result of Representatives working together with researchers on study design, ways to include patients, and sharing community concerns has set a new example for the design of future biomedical research studies.

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.

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.