A study conducted by experts at Weill Cornell Medicine identified four distinct subgroups of the post-COVID condition, also known as extended COVID, based on several clusters of symptoms.
The study, which looked at lengthy COVID, was the largest of its kind and was published on December 1 in Nature Medicine. In order to identify symptom patterns in the medical records of roughly 35,000 U.S. patients who tested positive for SARS-CoV-2 infection and later experienced lasting long-COVID-type symptoms, the researchers—clinicians and informaticists—used a machine-learning algorithm.
This study is a component of a $9.8 million, one-year grant focused on electronic health record cohort studies that is funded by the National Institutes of Health's Researching COVID to Enhance Recovery (RECOVER) initiative. Its principal investigator is Dr. Rainu Kaushal, senior associate dean for clinical research and chair of the Department of Population Health Sciences at Weill Cornell Medicine.
Dr. Kaushal, a co-senior author on the study, stated that the goal of RECOVER is to quickly explain what is occurring in protracted COVID. "Considering how instances cluster can have a significant impact on patient prognosis and management."
One of the four key trends found was a disproportionately high number of individuals who were infected during the first few months of the epidemic in the United States and included cardiac and renal issues. Nearly two-thirds of the individuals with another pattern had respiratory issues, anxiety, sleep issues, as well as other symptoms like headache and chest pain.
The study's principal investigator, Dr. Fei Wang, an associate professor of population health sciences, noted that the findings "should enrich continuing research on the likely mechanisms of extended COVID, and novel treatments for it."
Patients who have viral infections can experience a wide range of lasting, frequently vague symptoms. These post-infection syndromes for SARS-CoV-2 are more formally described as "post-acute SARS-CoV-2 infection," but are more commonly referred to as "long COVID" (PASC). Since estimates of the proportion of Americans with lengthy COVID range up to 40% of the adult population in the US, they appear to be fairly widespread.
Dr. Kaushal, who is also the Nanette Laitman Distinguished Professor of Population Health Sciences at Weill Cornell Medicine and the physician-in-chief of population health sciences at NewYork-Presbyterian/Weill Cornell Medical Center, said that understanding the epidemiology of long COVID enables clinicians to assist patients in understanding their symptoms and prognoses and facilitates multispecialty treatment for patients."Electronic health records provide a window into this disorder, enabling us to better describe long COVID symptoms, informing various forms of research including fundamental understanding and treatment trials," says the study.
The National Patient-Centered Clinical Research Network (PCORnet), which consists of eight consortiums of healthcare facilities from throughout the nation, generated two sizable databases from which the study's health information were drawn. One dataset came from the OneFlorida+ network, which includes patients from Florida, Georgia, and Alabama, and the other came from the INSIGHT Clinical Research Network, which Dr. Kaushal directs. The investigation comprised 34,605 distinct patients' health records in total from March 2020 to November 2021, up to but excluding the first Omicron wave.
The machine learning system initially examined the New York patient dataset and identified four main symptom patterns. The first category, which included around 34% of patients, was dominated by symptoms connected to the heart, kidneys, and circulation. Patients in this group had a median age of 65, were more likely to be male (49 percent), had a relatively high rate of COVID hospitalization (61 percent), and had comparatively more pre-existing conditions than patients in other categories. Additionally, this group had the largest percentage (37%) of patients who contracted SARS-CoV-2 during the first significant U.S. wave from March to June 2020.
The second symptom pattern was dominated by respiratory and sleep issues, anxiety, headaches, and chest pains, and it occurred in 33% of patients as frequently as the first pattern. The majority of patients with this pattern were female (63 percent), had a median age of 51 years, and had a substantially lower prevalence of COVID hospitalization (31 percent). During following waves, from November 2020 to November 2021, about two-thirds of the patients in this cohort tested positive for SARS-CoV-2. Asthma and chronic obstructive lung disease were the primary pre-existing illnesses in this cluster.
Musculoskeletal and nervous system symptoms, including arthritis (23 percent of patients), and a mix of digestive and respiratory symptoms dominated the other two symptom categories, respectively (10 percent).
Only the first symptom pattern had an approximately 1:1 sex ratio; in the other three, female patients predominated significantly (more than 60 percent).
Although previous studies have found that there is a sex difference in long-COVID risk, very few studies have even attempted to identify the underlying mechanisms, according to Dr. Wang.
The researchers used their algorithm to analyze the dataset of patients from the three southern states in order to confirm their findings, and they discovered results that were remarkably comparable. The data also demonstrated that symptoms occurring in the same 30-to-180-day time window after the test for patients who tested negative for SARS-CoV-2 did not follow such distinct patterns, supporting the general validity of extended COVID.
In order to easily identify long COVID symptom patterns from electronic health records, the researchers are currently defining long COVID symptom patterns. They are also identifying risk factors for various symptom patterns and existing treatments that can be repurposed to benefit long COVID patients.
To promote scientific innovation and offer professional advice, many Weill Cornell Medicine doctors and researchers engage with outside groups. To ensure transparency, the institution makes these disclosures available to the public. See the Drs. Kaushal and Wang's profiles for more details.
The American Rescue Plan Act of 2021 provides funding for the $1.15 billion National Institutes of Health Researching COVID to Enhance Recovery (NIH RECOVER) Initiative, which aims to better understand how COVID-19 infections are treated and who is most at risk for developing post-acute sequelae of SARS-CoV-2 (PASC). Additionally, researchers are collaborating with patients, doctors, and communities across the United States to develop methods for preventing and managing COVID's long-term impacts, including Long COVID.