According to a study developed by researchers at Weill Cornell Medicine, the post-COVID syndrome known as long COVID has four main subtypes defined by different clusters of symptoms. The research was the largest of its kind to examine the COVID long.
The researchers, representing physicians and computer scientists, used a machine learning algorithm to locate symptom patterns in the medical records of nearly 35,000 U.S. patients who tested positive for SARS-CoV-2 infection and later developed persistent symptoms of like COVID.
The results of the study were published in the scientific journal nature medicine.
Long Covid: here’s what is known about the 4 subtypes
Funded by the National Institutes of Health’s Researching COVID to Enhance Recovery (RECOVER) initiative, this research is part of a $9.8 million annual grant focusing on electronic health record cohort studies, led by principal investigator Dr. Rainu Kaushal, senior associate dean for clinical research and chair of the Department of Population Health Sciences at Weill Cornell Medicine.
“RECOVER aims to quickly clarify what is happening in the long run of COVID,” said Kaushal, co-senior author of the study. “Observing how cases cluster can have a profound impact on patient prognosis and care.”
Of the four main models detected, one had heart and kidney problems and included a relatively large proportion of patients infected in the early months of the pandemic in the United States. Another pattern included breathing problems, anxiety, sleep disturbances, and other symptoms including headaches and chest pain; nearly two-thirds of patients with this pattern were female.
“These findings should inform ongoing research into potential mechanisms of long-term COVID and potential treatments for it,” said Dr. Fei Wang, associate professor of population health sciences, who led the study.
Viral infections sometimes leave patients with a variety of persistent, often nonspecific symptoms. For SARS-CoV-2, these post-infection syndromes are popularly known as “long COVID” and more formally as “SARS-CoV-2 post-acute infection” (PASC). They seem to be very common; estimates of the number of Americans who have had long-term COVID range as high as 40% of the US adult population.
“Understanding the epidemiology of long COVID enables clinicians to help patients understand their symptoms and prognosis and facilitates multispecialty treatment for patients,” said Kaushal, who is also the Nanette Laitman Distinguished Professor of Population Health Sciences and chief physician of population health sciences at NewYork-Presbyterian/Weill Cornell Medical Center. “Electronic health records offer a window into this condition, allowing us to better characterize long-term COVID symptoms, informing other types of research, including breakthrough discoveries and clinical trials.”
The medical records analyzed for the study came from two large data sets amassed by the National Patient-Centered Clinical Research Network (PCORnet), which includes eight consortia of healthcare institutions from across the country. One dataset, from the INSIGHT clinical research network, led by Kaushal, included data from patients based in New York, while the other came from the OneFlorida+ network, which includes patients from Florida, Georgia and Alabama. In all, the analysis covered the medical records of 34,605 different patients from March 2020 to November 2021, up to but not including the first wave of omicrons.
By initially analyzing the New York patient dataset, the machine learning algorithm detected four major symptom patterns. The former, which accounted for approximately 34% of patients, was dominated by cardiac, renal and circulatory symptoms.
Patients in this group, compared with those in other groups, were on average older (mean age 65), were more likely to be male (49%), had a relatively high rate of hospitalization for COVID (61%) and they had a relatively number of existing conditions. This group also had the highest proportion (37%) of patients infected with SARS-CoV-2 during the first major US wave from March to June 2020.
The second symptom picture, comparable in frequency (33% of patients) to the first, was dominated by respiratory and sleep problems, anxiety, headaches and chest pains. Patients with this pattern were mostly female (63%), with a mean age of 51 and a much lower rate (31%) of hospitalizations for COVID. Nearly two-thirds of patients in this group tested positive for SARS-CoV-2 during subsequent waves, from November 2020 to November 2021. Pre-existing conditions in this group centered around respiratory problems such as chronic obstructive pulmonary disease and asthma.
The other two symptom patterns were dominated, respectively, by musculoskeletal and nervous system symptoms including arthritis (23% of patients) and a combination of digestive and respiratory symptoms (10%). Only in the first symptom model was the sex ratio approximately 1:1; in the other three, female patients constituted a significant majority (over 60%).
“This sex difference in long COVID risk is consistent with previous research, but so far, very few studies have even attempted to uncover the underlying mechanisms,” Wang said.
To validate their findings, the researchers applied their algorithm to the dataset covering patients from the three southern states and found very similar results. The analysis also supported the overall validity of long COVID by showing that, for patients who tested negative for SARS-CoV-2, symptoms appearing in the same time frame 30 to 180 days after testing did not have such clear patterns.
Researchers are currently pursuing research along several lines, including establishing long patterns of COVID symptoms so they can be identified easily from electronic health records, identifying risk factors for different symptom patterns, and identifying of existing treatments that can be repurposed to help long COVID patients.
Many studies have described a variety of long-term effects of COVID-19, with symptoms including fatigue and malaise, breathing difficulties and cognitive problems, often referred to as post COVID-19 or long COVID condition. A recent analysis on the Journal of Internal Medicine identified several characteristics associated with an increased likelihood of receiving a post COVID-19 condition diagnosis.
In the study of 204,805 people who tested positive for SARS-CoV-2 in Stockholm, Sweden from March 2020 to July 2021, the proportion who received a post-COVID-19 diagnosis was 1% among people not hospitalized for their COVID-19 infection, 6% among hospitalized and 32% among people treated in intensive care units (ICU). The most common new-onset symptoms among people with a post-COVID-19 diagnosis were fatigue (29%) among outpatients and breathing difficulties among both hospitalized (25%) and treated patients intensive (41%).
Female gender, prior mental health disorders, and asthma were associated with post-COVID-19 status among outpatients and hospitalized individuals. Among people with post COVID-19 condition, outpatient care use was substantially elevated up to one year after acute infection.
“Our understanding of health effects beyond acute SARS-CoV-2 infection is continuously improving. In this study, we observed a marked difference in the occurrence of post-COVID-19 diagnosis in different severities of acute infection,” said the corresponding author. Pontus Hedberg, MD and postdoctoral researcher at Karolinska Institutet. “Furthermore, the high use of outpatient primary and specialist care indicates poor recovery for people suffering from conditions post COVID-19, highlighting the urgent need to better understand this condition and its potential resolution over time.”
Patients who have recovered from COVID-19 frequently report post-acute sequelae of SARS-CoV-2 (PASC) symptoms such as fatigue, dyspnea, and anosmia. Previous studies describing PASC have focused on hospitalized adult patients or patients with mild COVID-19 treated on an outpatient basis up to 9 months after infection.
Cohorts of patients with PASC included small proportions of individuals from minority groups. This is the first study examining the association between race, social vulnerability and insurance status with the development of long-term Covid, according to the researchers.
The scientists analyzed data from 1038 participants (aged 60 years; interquartile range [IQR], from 37 to 83 years; 42% Latino, 30% White) in the University of California at Los Angeles (UCLA) Outpatient COVID Monitoring Program. Patients completed follow-up investigations 30, 60, or 90 days after hospital discharge or outpatient diagnosis. Eighty percent of the patients were followed up after their illness.
Long-term Covid was found in 29.8% of patients at least 60 days after acute illness (30.8% of hospital-treated patients, 26.5% of high-risk outpatients). At 30 days, the most commonly reported symptoms were fatigue (73.2%), shortness of breath (63.6%), fever and chills (51.5%), and body aches (50.6%). At 60 days, fatigue (31.4%), shortness of breath (13.9%) and loss of taste or smell (9.8%).
Fatigue was the most common symptom among both hospitalized and outpatients. About 15% of hospitalized patients experienced shortness of breath, and about 16% of outpatients experienced loss of taste or smell.
Patients in outpatient care were more likely to be younger, white, female, and commercially insured. Hospitalized patients were more likely to report long-term Covid symptoms if they were female. Patients with a history of organ transplantation were less likely to develop the condition.
Hospitalization for COVID-19 (OR, 1.49 95% CI 1.04-2.14), diabetes (odds ratio [OR], 1.39; 95% CI, 1.02–1.88) and higher body mass index (OR, 1.02; 95% CI, 1.0002–1.04), have been linked to the development of PASC. Patients with Medicaid (OR, 0.49; 95% CI, 0.31-0.77) or organ transplant history (OR, 0.44; 95% CI, 0.26-9.76) had less likely to develop PASC.
The researchers said the lack of association between age or race with long Covid development may be influenced by access to the same healthcare system with standardized follow-up, importance of risk factors for contracting COVID-19 versus recovering from COVID -19 or variance in symptoms and expectations across demographic groups and the ability of PASC tools to realize those differences. The variation in symptoms between hospitalized and outpatients is likely due to differences in clinical phenotypes, according to the researchers.
Study limitations included potential self-report bias, baseline bias, survival bias, assessment of a limited number of PASC symptoms, no control group of patients with persistent symptoms following non-COVID-related hospital admissions, and limited knowledge of conditions pre-existing.
“Understanding the effects of long-term COVID will allow for more effective education among patients and healthcare professionals and enable appropriate use of healthcare resources in PASC assessment and treatment,” concluded the researchers.
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