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Sharing our progress toward recovery

Research Summaries

Discover what the latest science from the RECOVER Initiative means for our ability to understand, treat, and prevent 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.

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20 Results

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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 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. 

Short Summary
EHR Adult

Pfaff, ER; Girvin, AT; Crosskey, M; et. al.N3C and RECOVER Consortia, Journal of American Medical Informatics Association

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.

To find another way to know if children had COVID, 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 in the past, had the COVID vaccine, or both. A positive PCR test means a child currently has COVID. 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 and didn’t have a positive PCR test or  COVID vaccine. If researchers used only PCR tests, they wouldn’t have known these children had COVID. Knowing if children had COVID is the first step to know their risk of Long COVID and if symptoms could be related to Long COVID.

Short Summary
EHR Adult

Leese, P; Anand, A; Girvin, A; et. al., Journal of the American Medical Informatics Association

Using computer processing to make data from medical records easier to use in research 

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:  

  • How long the patient received care 
  • The number and types of treatments and medical procedures the patient received 
  • The order in which the patient received these treatments and procedures 

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 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.  

Short Summary
EHR Adult

Brannock, MD; Chew, RF; Preiss, AJ; et. al.N3CRECOVER Consortia, Nature Communications

RECOVER researchers wanted to understand whether being vaccinated before having COVID lowered the chance of developing Long COVID. They used electronic health records (EHR) to study the effect of vaccination using EHR for two groups of people who had COVID. One group was based on clinic data and represented more than 47,000 people. In this group, 695 were diagnosed with Long COVID in clinics and more than 26,000 were fully vaccinated. The other group represented almost 200,000 individuals who had COVID. The researchers used a computer program to estimate who might have Long COVID in this group based on their medical and symptom information. In this group, more than 86,000 people represented were fully vaccinated. The researchers made sure that the people in the vaccinated and unvaccinated groups were as similar to each other as possible in terms of the same sex, age, race, and medical history.  

To test whether vaccination lowered the chance of developing Long COVID, they made comparisons within each group. They used several different definitions of Long COVID and several different statistical tests to figure out whether vaccination status affected Long COVID. For both of the study groups and for all definitions of Long COVID and each statistical test in the analysis, the researchers got the same answer: people who were vaccinated before having COVID were less likely to develop Long COVID.  

RECOVER researchers analyzed electronic health records (EHR) in order to define Long COVID. Researchers found up to 25 different symptoms that patients who had COVID were more likely to have than those who didn’t have COVID. The symptoms were related to many different organs, such as memory loss, hair loss, and feeling tired. They found that certain types of Long COVID symptoms were more likely to happen in patients who were older, had more severe COVID, or had more health problems before they had COVID.  

This research is important because the findings show that Long COVID affects many organs, and types of Long COVID symptoms differ between certain groups of patients. However, EHR findings are limited in that they can only look at data from the patients' past. In order to confirm these findings, future studies that follow patients' symptoms over time, into the future, are needed. 

Short Summary
EHR Pediatric

Jhaveri, R; Webb, R; Razzaghi, H; et. al.RECOVER consortium, Journal of the Pediatric Infectious Disease Society

RECOVER researchers explored Multisystem Inflammatory Syndrome in Children (MIS-C), a Long COVID symptom where there is inflammation (swelling) of multiple organ systems. Researchers looked to see if MIS-C can be identified and managed in the home (outpatient) instead of in the hospital (inpatient). 

An organ system is a collection of organs in your body dedicated to maintaining necessary functions. For example, your heart, arteries, and veins make up the Cardiovascular System--the system responsible for maintaining blood flow and delivering oxygen throughout the body.

Researchers were able to identify that in some patients, their MIS-C symptoms were mild enough to be treated at home and not require hospitalization. These results raise awareness that MIS-C can present in milder forms with the option to treat it at home if directed by a physician.

RECOVER researchers wanted to identify conditions and symptoms associated with Long COVID in children (also known as pediatric Long COVID). The researchers analyzed electronic health record (EHR) data to detect conditions and symptoms associated with pediatric Long COVID. The study identified multiple conditions and body systems associated with pediatric Long COVID related to many different organs, such as heart and lung problems. 

This research is important because these findings use a data-driven approach to detect several new or under-reported conditions and symptoms that should be studied further. Researchers believe that further study may reveal the biological processes that cause these Long COVID symptoms and conditions.

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