Defining long-COVID using self-reported symptoms

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In a recent article published in JAMA Network, researchers performed a prospective observational cohort study to develop symptom-based criteria to identify post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) cases, otherwise known as long COVID.

Study: Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection. Image Credit: MeekoMedia/Shutterstock.comStudy: Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection. Image Credit: MeekoMedia/Shutterstock.com

Background

They proposed that PASC is a new condition arising due to SARS-CoV-2 infection, and unlike in prior reports, researchers did not rely on its predefined clinical symptoms. Researchers consider PASC a condition where relapsing, persistent, or new symptoms are present beyond 30 days of recovery from SARS-CoV-2 infection.

The PASC effects, short- and long-term, are substantial; it affects one's health-related quality of life and financial income while also burdening healthcare infrastructure.

Most published PASC studies have fetched inconsistent estimates of PASC prevalence focused on the symptom frequency of an individual due to the lack of a comparison group and their retrospective design. 

Moreover, heterogeneity in PASC symptoms makes it challenging to define PASC precisely. It manifests as conditions with variable and often overlapping etiologies (e.g., organ injury, gut dysbiosis, immune dysregulation).

Understanding the mechanisms governing PASC is of significant public health importance as it could help devise preventive and therapeutic intervention strategies.

However, it requires data collection from a large prospective cohort study of SARS-CoV-2–infected vs. –uninfected individuals, specifically designed to characterize PASC. Additionally, this study should use appropriate analytical techniques and monitor symptoms that persist after recovery. 

It is equally important to consider that changes in PASC incidence and its manifestations throughout the COVID-19 pandemic varied for several reasons, such as the emergence of new SARS-CoV-2 strains, introduction and subsequent availability of new treatments, and repeat (breakthrough) infections.

In the United States (US), the National Institutes of Health initiated Researching COVID to Enhance Recovery (RECOVER) to understand, prevent, and treat PASC.

About the study

In the present study, researchers analyzed data from the RECOVER adult cohort for diagnosing PASC based on patients' self-reported symptoms. They delineated and described several unique PASC subphenotypes with differential impacts on health and well-being.

The authors expected that selection bias based on PASC would be minimal and estimates more accurate among the subcohort enrolled in 30 days post-acute SARS-CoV-2 infection. Also, RECOVER captured PASC's self-reported symptoms using standard questionnaires developed with the help of patient representatives.

The team recruited all participants from 85 US sites and asked them to make office visits and complete remote surveys.

The participant enrollment is ongoing; however, in this analysis, they considered 13,754 adult participants enrolled before April 10, 2023. These participants belonged to the acute and post-acute cohorts, i.e., enrolled ≤30 days or >30 days to three years since the index date (December 1, 2021), respectively.

Also, the study participants needed to complete a study visit six months or later. SARS-CoV-2 infection before enrollment was the primary exposure of the study, and the main outcome was the presence of 44 symptoms to help researchers develop a PASC definition based on a composite symptom score.

The team reported symptoms overall and for three subcohorts, acute Omicron, post-acute pre-Omicron, and post-acute Omicron. They anticipated that symptom frequency estimates within the acute Omicron subcohort aligned more with the corresponding population frequencies.

For this analysis, they considered symptoms with severity threshold frequency 2.5%. Finally, they reported symptom frequencies by infection status and used weighted logistic regression to compute adjusted odds ratios (aORs).

The team used the least absolute shrinkage and selection operator (LASSO) to differentiate the symptoms of infected and uninfected participants. Next, they assigned a score to each sign based on the estimated coefficients. 

In this way, each participant received a composite symptom score, and the researchers selected an optimal score threshold for PASC using 10-fold cross-validation.

Results

A total of 9,764 participants met the study criteria, of which 8,646 and 1,118 were SARS-CoV-2-infected and uninfected, respectively. Of 44, 37 symptoms had frequency 2.5%, and all had aORs 1.5.

Symptoms like PEM, fatigue, dizziness, brain fog, and gastrointestinal (GI) symptoms showed a>15% absolute difference in frequencies among infected vs. uninfected individuals.

However, the frequencies of these symptoms (with severity thresholds) were comparable in infected participants. Without severity thresholds, the observed corresponding symptom frequencies were higher.

Despite a higher proportion of unvaccinated individuals in the post-acute pre-Omicron subcohort, the distributions of comorbidities and demographics were comparable across all three subcohorts. The post-acute pre-Omicron subcohort also had the highest symptom frequencies.

Strikingly, only 12 symptoms out of 44 contributed to the PASC score; however, the authors noted a correlation between their increasing levels and progressively worsening well-being and functioning, especially among participants infected in the pre-Omicron era.

Also, PASC frequency was higher among those with recurrent infections who got infected first during the Omicron era.

Another observation was that SARS-CoV-2 infection-related long-term symptoms spanned multiple organ systems, likely due to persistent viral reservoirs, autoimmunity, or direct differential organ injury. 

Conclusions

In this study, the researchers developed a novel framework for PASC diagnoses that incorporated contributions of multiple self-reported symptoms. An updated algorithm could also consider a patient's biological features. 

Together, it could enable the development of PASC biomarkers to get a sneak peek into the mechanistic underpinnings of PASC to inform the choice of therapeutic interventions in future PASC clinical trials.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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