Deep Dive into the Long Haul: Analysis of Symptom Clusters and Risk Factors for Post-Acute Sequelae of COVID-19 to Inform Clinical Care

Abstract

Long COVID is a chronic condition characterized by symptoms such as fatigue, dyspnea, and cognitive impairment that persist or relapse months after an acute infection with the SARS-CoV-2 virus. Many distinct symptoms have been attributed to Long COVID; however, little is known about the potential clustering of these symptoms and risk factors that may predispose patients to certain clusters. In this study, an electronic survey was sent to patients in the UC San Diego Health (UCSDH) system who tested positive for COVID-19, querying if patients were experiencing symptoms consistent with Long COVID. Based on survey results, along with patient demographics reported in the electronic health record (EHR), linear and logistic regression models were used to examine putative risk factors, and exploratory factor analysis was performed to determine symptom clusters. Among 999 survey respondents, increased odds of Long COVID (n = 421; 42%) and greater Long COVID symptom burden were associated with female sex (OR = 1.73, 99% CI: 1.16–2.58; β = 0.48, 0.22–0.75), COVID-19 hospitalization (OR = 4.51, 2.50–8.43; β = 0.48, 0.17–0.78), and poorer pre-COVID self-rated health (OR = 0.75, 0.57–0.97; β = −0.19, −0.32–−0.07). Over one-fifth of Long COVID patients screened positive for depression and/or anxiety, the latter of which was associated with younger age (OR = 0.96, 0.94–0.99). Factor analysis of 16 self-reported symptoms suggested five symptom clusters—gastrointestinal (GI), musculoskeletal (MSK), neurocognitive (NC), airway (AW), and cardiopulmonary (CP), with older age (β = 0.21, 0.11–0.30) and mixed race (β = 0.27, 0.04–0.51) being associated with greater MSK symptom burden. Greater NC symptom burden was associated with increased odds of depression (OR = 5.86, 2.71–13.8) and anxiety (OR = 2.83, 1.36–6.14). These results can inform clinicians in identifying patients at increased risk for Long COVID-related medical issues, particularly neurocognitive symptoms and symptom clusters, as well as informing health systems to manage operational expectations on a population-health level.

Oxidative Damage and Post-COVID Syndrome: A Cross-Sectional Study in a Cohort of Italian Workers

“…Developing the knowledge on host factors that predict a long-hauler status, as well as potential association with symptom clusters, will be pivotal to understanding why the sequelae of acute COVID-19 infection vary widely from patient to patient, from complete recovery to severe persistent symptoms affecting multiple organs and mental health, and for the consequent development of evidence-based management guidelines [ 7 ]. Little is known about the mid- and long-term consequences of COVID-19 in non-hospitalized individuals, although emerging data suggest that a significant proportion of mild or moderate cases, even in younger adults, have persistent symptoms associated with previous SARS-CoV-2 infection [ 8 ].…”

A Comparison of the 1918 and 2019 Pandemics in the United States

“…In addition, there were significant shortages of personal protective equipment to protect frontline healthcare workers, inadequate supplies to care for COVID-19 patients, and rationing of ventilators 24,25 . The result was that many more US citizens died of COVID-19 than needed to die, 26 and others continue to have persistent symptoms after recovering from the initial infection 26–30 …”

“…24,25 The result was that many more US citizens died of COVID-19 than needed to die, 26 and others continue to have persistent symptoms after recovering from the initial infection. [26][27][28][29][30]…”

Symptom Clusters in Adults with Post-COVID-19: A Cross-Sectional Survey

“…Empirical approaches have produced evidence for multiple symptom clusters for post-COVID-19, and the research has not converged on a standard set of symptom clusters; this is not surprising considering the science of symptom clusters for post-COVID-19 is relatively new. In the existing research, the timing of data collection varied from 28 to 617 days after infection, the source of data varied from medical records to self-report surveys, and the list of symptoms surveyed varied from 16 to 30 (Goldhaber et al, 2022; Kenny et al, 2022; Peter et al, 2022; Pinto et al, 2022). Some studies included only people previously hospitalized for COVID (Busatto et al, 2022), some included a combination of previously hospitalized and never hospitalized (Goldhaber et al, 2022), and others included only people with no history of hospitalization for COVID (Subramanian et al, 2022).…”

“…In the existing research, the timing of data collection varied from 28 to 617 days after infection, the source of data varied from medical records to self-report surveys, and the list of symptoms surveyed varied from 16 to 30 (Goldhaber et al, 2022; Kenny et al, 2022; Peter et al, 2022; Pinto et al, 2022). Some studies included only people previously hospitalized for COVID (Busatto et al, 2022), some included a combination of previously hospitalized and never hospitalized (Goldhaber et al, 2022), and others included only people with no history of hospitalization for COVID (Subramanian et al, 2022). In addition, the pandemic is dynamic and symptom patterns change as the virus mutates and as vaccines are more readily available.…”