Research Summaries

This RECOVER paper combined research findings, patient experiences, and possible explanations about how the body works to understand how different germs and health factors might play a role in Long COVID. Researchers looked at how having a viral, bacterial, or fungal infection before, during, or after COVID-19 might make someone more likely to develop Long COVID. The combined information suggests that these infections can confuse the immune system, making it attack healthy cells or cause problems to the body’s organs. For example, the stress of fighting SARS-CoV-2, the virus that causes COVID-19, might “wake up” old viruses that were dormant (asleep or inactive) in the body, which can make a person sicker or more likely to develop Long COVID. This paper is important because it shows that Long COVID is complex and may involve different germs that change the body’s responses to infection. By learning more about these connections, researchers hope to find better ways to test for and treat Long COVID in everyone.

In this RECOVER study, researchers wanted to find out how Long COVID symptoms change over 15 months. To do this, researchers studied 3,659 adults who had COVID-19 before joining the RECOVER study or while they were enrolled. Researchers found that Long COVID does not look the same in everyone. Instead, people’s Long COVID symptoms usually fell into 1 of 8 different patterns over time. While most people felt better after recovering from COVID-19, about 1 in 20 participants had symptoms of Long COVID that lasted throughout the entire study. About 3 in 25 participants with Long COVID had symptoms that came and went. Other participants did not have Long COVID symptoms early on but started having health problems many months after having COVID-19. These findings show that doctors should continue monitoring patients for a long time after they have COVID-19 because their symptoms can stay, come and go, or start several months after getting sick. Understanding these patterns will help researchers find better ways to prevent and treat Long COVID in different groups of people.

The RECOVER-NEURO Cognitive Dysfunction (BrainHQ, PASC-CoRE, & tDCS) clinical trial tested 3 non-drug treatments to see if they improved thinking, focus, and memory for people with Long COVID. The 3 treatments included:

1. An interactive online brain training program called BrainHQ.
2. A virtually delivered, small group cognitive rehabilitation program called PASC-Cognitive Recovery or PASC-CoRE.
3. A cap that provided a non-invasive form of brain stimulation via electrical current called transcranial direct current stimulation or tDCS.

A unique aspect of RECOVER-NEURO was its decentralized design, enabling 328 adults across 22 U.S. sites to participate in this research despite living with Long COVID symptoms. The participants completed most study activities remotely during a 10-week study period. Some participants were assigned to a treatment group and received one of the three active treatments (BrainHQ, PASC-CoRE plus BrainHQ, or tDCS-active plus BrainHQ). Other participants were assigned to a comparison group and received either the BrainHQ active comparator or tDCS-comparator plus BrainHQ. Researchers found that all of the treatment groups and comparison groups reported similar results in their improvement in thinking, focus, and memory. All participants reported some improvement over time and many said they felt better overall after they completed their assigned treatment, even though no one treatment outperformed other groups, including the comparison groups.

This RECOVER study looked at whether people with Human Immunodeficiency Virus (HIV) are more likely to get Long COVID after having COVID-19. HIV is a virus that attacks the body’s immune system. Researchers used electronic health records (EHRs) from 2 nationwide research networks of people ages 21 and older who had COVID-19. They divided them into 2 groups: people with HIV and people without HIV. Researchers used 2 methods to find people in the groups who might have Long COVID. One used a computer system that looked for Long COVID symptoms in medical records. The other looked for official diagnosis codes that doctors enter when they think someone has Long COVID. They found that people with HIV may have a slightly higher risk of being diagnosed with Long COVID when looking for Long COVID symptoms in medical records. However, they found that there was no clear link between having HIV and receiving the official diagnosis code for Long COVID. This suggests that Long COVID may be missed in people with long-term health problems like HIV.

This RECOVER study looked at whether the COVID-19 vaccine could help protect teenagers ages 12–17 from developing Long COVID. Researchers studied 1,231 teenagers enrolled in RECOVER who had confirmed COVID-19. Some were vaccinated before they got COVID-19 (724 teenagers), and some were not (507 teenagers). Researchers made sure the 2 groups were similar in terms of sex, date when they got COVID-19, and when they joined the study to make comparisons fair. They found that teenagers who were vaccinated in the 6 months before getting COVID-19 for the first time were about one-third less likely to get Long COVID. This study is important because it shows that COVID-19 vaccines, which were previously found to prevent getting COVID-19, can also protect against developing Long COVID in young people. 

This RECOVER study tested a new way to identify Long COVID in children. Researchers created a special computer-based tool, called a computable phenotype (CP), that scans electronic health records (EHRs) for specific codes and symptoms linked to Long COVID. To test the CP, researchers scanned records for nearly 340,000 children who had COVID-19. Doctors then checked a smaller group of 651 children’s records to see how well it worked. The study showed that the CP was able to successfully find that a child had Long COVID. But it sometimes marked symptoms that fit in with health problems someone had before getting COVID-19 (pre-existing conditions), so it wasn’t clear if those symptoms were due to COVID-19. When the researchers updated the CP to pay closer attention to pre-existing conditions, it became more accurate at identifying Long COVID from EHRs. This study is important because it provides a faster and more consistent way for researchers to identify Long COVID in large groups of children. This tool can help doctors give better care to children with Long COVID.

This RECOVER study looked at whether pregnant women who had the Omicron type of COVID-19 were more likely to have Long COVID than women who were not pregnant when they had the Omicron type of COVID-19. Researchers studied the symptom surveys and study visits of more than 2,400 RECOVER pregnancy participants, ages 18–45, to see if being pregnant while having COVID-19 impacted the risk of developing Long COVID. They found that about 10.2% of the participants who had COVID-19 while pregnant later got Long COVID, compared with 10.6% of the those who were not pregnant at the time of infection. This suggests that there was no real difference in the chance of getting Long COVID based on whether someone was pregnant or not when they got COVID-19. This study is important because it helps researchers better understand the risk of developing COVID-19 based on whether or not someone is pregnant.

In this RECOVER study, researchers wanted to find out if natural language processing (NLP) could be used to identify Long COVID symptoms in children. NLP is a tool that can help find details in electronic health records (EHRs) beyond what is usually looked at in EHRs, such as diagnosis or billing codes (known as standard EHR data). Researchers used an NLP tool to look for 25 signs of Long COVID in children: 21 symptoms (like pain or extreme tiredness) and 4 types of daily life challenges (such as trouble with school). They compared children who had been diagnosed with Long COVID to those who had COVID-19 but did not develop Long COVID. The NLP tool analyzed more than 48,000 doctors’ notes within the EHRs of more than 10,000 children across 12 hospitals. Researchers found that the NLP tool identified almost all 25 symptoms much more often in the children who had Long COVID. The NLP tool also often identified patients’ symptoms that were not recognized when researchers only looked at standard EHR data. The study shows that using NLP to read EHR notes can help researchers better understand the symptoms and daily challenges that children with Long COVID experience when compared to looking only at codes and medication lists. This supports the idea that NLP should be used when doing scientific studies that need to identify children with Long COVID.