Characterization of post-COVID syndromes by symptom cluster and time period up to 12 months post-infection: A systematic review and meta-analysis

Who develops long COVID? Longitudinal pre-pandemic predictors of long COVID and symptom clusters in a representative Dutch population

OBJECTIVES

Prior studies show that long COVID has a heterogeneous presentation. Whether specific risk factors are related to subclusters of long COVID remains unknown. This study aimed to determine pre-pandemic predictors of long COVID and symptom clustering.

METHODS

A total of 3,022 participants of a panel representative of the Dutch population completed an online survey about long COVID symptoms. Data was merged into 2018/2019 panel data covering sociodemographic, medical, and psychosocial predictors. A total of 415 participants were classified as having long COVID. K-means clustering was used to identify patient clusters. Multivariate and lasso regression was used to identify relevant predictors compared to a COVID-19 positive control group.

RESULTS

Predictors of long-term COVID included older age, Western ethnicity, BMI, chronic disease, COVID-19 reinfections, severity, and symptoms, lower self-esteem, and higher positive affect (AUC = 0.79, 95%CI 0.73-0.86). Four clusters were identified: a low and a high symptom severity cluster, a smell-taste and respiratory symptoms cluster, and a neuro-cognitive, psychosocial, and inflammatory symptom cluster. Predictors for the different clusters included regular health complaints, healthcare use, fear of COVID-19, anxiety, depressive symptoms, and neuroticism.

CONCLUSIONS

A combination of sociodemographic, medical, and psychosocial factors predicted long COVID. Heterogenous symptom clusters suggest that there are different phenotypes of long COVID-19 presentation.

Presence of SARS-CoV-2 RNA in COVID-19 survivors with post-COVID symptoms: a systematic review of the literature

INTRODUCTION

Viral persistence is one of the main hypotheses explaining the presence of post-COVID symptoms. This systematic review investigated the presence of SARS-CoV-2 RNA in plasma, stool, urine, and nasal/oral swab samples in individuals with post-COVID symptomatology.

CONTENT

MEDLINE, CINAHL, PubMed, EMBASE, Web of Science databases, as well as medRxiv/bioRxiv preprint servers were searched up to November 25th, 2023. Articles investigating the persistence of SARS-CoV-2 RNA in plasma, stool, urine or nasal/oral swab samples in patients with post-COVID symptoms were included. Methodological quality was assessed using the Newcastle-Ottawa Scale or Cochrane's Risk of Bias (Rob) tool.

SUMMARY

From 322 studies identified, six studies met all inclusion criteria. The sample included 678 COVID-19 survivors (52 % female, aged from 29 to 66 years). The methodological quality was moderate in 88 % of the studies (n=5/6). Three papers investigated the presence of SARS-CoV-2 RNA in plasma, three studies in nasal/oral swabs, two studies in stool samples, one in urine and one in saliva. The follow-up was shorter than two months (<60 days after) in 66 % of the studies (n=4/6). The prevalence of SARS-CoV-2 RNA ranged from 5 to 59 % in patients with post-COVID symptoms the first two months after infection, depending on the sample tested, however, SARS-CoV-2 RNA was also identified in COVID-19 survivors without post-COVID symptoms (one study).

OUTLOOK

Available evidence can suggest the presence of persistent SARS-CoV-2 RNA in post-COVID patients in the short term, although the biases within the studies do not permit us to make firm assumptions. The association between post-COVID symptoms and SARS-CoV-2 RNA in the samples tested is also conflicting. The lack of comparative group without post-COVID symptoms limits the generalizability of viral persistence in post-COVID-19 condition.

Prognosis of patients with post-Covid-19 condition: Prospective cohort cluster analysis at one year

OBJECTIVE

We aimed to identify clinically relevant clusters among patients with post-Covid-19 condition (PCC) and assess prognosis overall and within clusters.

METHODS

Prospective cohort study of patients with PCC attending a rehabilitation clinic. We monitored patient reported outcome measures (PROMs): EuroHIS quality of life and symptoms. Unsupervised hierarchical cluster analyses were performed to identify clusters of patients with different quantity of symptoms, and symptoms presenting together. Preliminary findings on symptom prevalence and quality of life at 12 months are reported.

RESULTS

Among 409 patients, 70.4% were women, with an average baseline of 20.3 (SD 6.8) symptoms. Three clusters emerged based on symptom quantity, labelled by the average number of symptoms at baseline: Cluster-11 (17% of all patients), Cluster-17 (35%), and Cluster-25 (48%). Multinomial logistic regression showed female sex, multiple comorbidities predicting more symptoms. Four symptom-based clusters were defined: fatigue and cognitive complaints; pain, trouble sleeping, palpitations and other symptoms; gastrointestinal symptoms; and emotion-related symptoms. Linear regression models showed that female sex, multiple comorbidities, anxiety, use of antidepressants, BMI and smoking were among the determinants of symptom clusters. In 12-month follow-up, symptom count decreased, and quality of life improved across all clusters, with 9% having good quality of life at baseline and 33% at 12 months.

CONCLUSION

Four patient clusters based on symptoms were identified in the PCC cohort. Prognosis was favorable across all clusters, with symptom reduction and improved quality of life observed. Female sex, comorbidities, BMI, and mental-health related variables predicted higher symptom burden, suggesting multifactorial origins of PCC.

Long COVID is not a uniform syndrome: Evidence from person-level symptom clusters using latent class analysis

BACKGROUND

The current study aims to enhance insight into the heterogeneity of long COVID by identifying symptom clusters and associated socio-demographic and health determinants.

METHODS

A total of 458 participants (Mage 36.0 ± 11.9; 46.5% male) with persistent symptoms after COVID-19 completed an online self-report questionnaire including a 114-item symptom list. First, a k-means clustering analysis was performed to investigate overall clustering patterns and identify symptoms that provided meaningful distinctions between clusters. Next, a step-three latent class analysis (LCA) was performed based on these distinctive symptoms to analyze person-centered clusters. Finally, multinominal logistic models were used to identify determinants associated with the symptom clusters.

RESULTS

From a 5-cluster solution obtained from k-means clustering, 30 distinctive symptoms were selected. Using LCA, six symptom classes were identified: moderate (20.7%) and high (20.7%) inflammatory symptoms, moderate malaise-neurocognitive symptoms (18.3%), high malaise-neurocognitive-psychosocial symptoms (17.0%), low-overall symptoms (13.3%) and high overall symptoms (9.8%). Sex, age, employment, COVID-19 suspicion, COVID-19 severity, number of acute COVID-19 symptoms, long COVID symptom duration, long COVID diagnosis, and impact of long COVID were associated with the different symptom clusters.

CONCLUSIONS

The current study's findings characterize the heterogeneity in long COVID symptoms and underscore the importance of identifying determinants of different symptom clusters.

Persistence of post-COVID symptoms in the general population two years after SARS-CoV-2 infection: A systematic review and meta-analysis

OBJECTIVE

This meta-analysis investigated the prevalence of post-COVID symptoms two-years after SARS-CoV-2 infection.

METHODS

Electronic literature searches on PubMed, MEDLINE, CINAHL, EMBASE, Web of Science databases, and on medRxiv/bioRxiv preprint servers were conducted up to October 1, 2023. Studies reporting data on post-COVID symptoms at two-years after infection were included. Methodological quality was assessed using the Newcastle-Ottawa Scale. Random-effects models were used for meta-analytical pooled prevalence of each symptom.

RESULTS

From 742 studies identified, twelve met inclusion criteria. The sample included 7912 COVID-19 survivors (50.7% female; age: 59.5, SD: 16.3). Post-COVID symptoms were assessed at a follow-up of 722.9 (SD: 51.5) days after. The overall methodological quality of studies was moderate (mean: 6/10, SD: 1.2 points). The most prevalent post-COVID symptoms two-years after SARS-CoV-2 infection were fatigue (28.0%, 95%CI 12.0-47.0), cognitive impairments (27.6%, 95%CI 12.6-45.8), and pain (8.4%, 95%CI 4.9-12.8). Psychological disturbances such as anxiety (13.4%, 95%CI 6.3-22.5) and depressive (18.0%, 95%CI 4.8-36.7) levels as well as sleep problems (20.9%, 95%CI 5.25-43.25) were also prevalent. Pooled data showed high heterogeneity (I2 ≥ 75%).

CONCLUSION

This meta-analysis shows the presence of post-COVID symptoms in 30% of patients two-years after COVID-19. Fatigue, cognitive disorders, and pain were the most prevalent post-COVID symptoms. Psychological disturbances as well as sleep problems were still present two-years after COVID-19.

Post-covid-19 conditions in adults: systematic review and meta-analysis of health outcomes in controlled studies

Objective

To assess the impact of post-covid-19 conditions among adults.

Design

Systematic review and meta-analysis of health outcomes in controlled studies.

Data sources

Two sources were searched from database inception to 20 October 2022: Cochrane covid-19 study register (comprising Cochrane Central Register of Controlled Trials, Medline, Embase, clinicalTrials.gov, World Health Organization's International Clinical Trials Registry Platform, medRxiv) and WHO's covid-19 research database.

Eligibility criteria

Cohort studies recruiting more than 100 participants with a control group and a follow-up of at least 12 weeks were included. Adults who were documented to have SARS-CoV-2 infection based on clinical, imaging, or laboratory criteria were included.

Data extraction and synthesis

Two independent reviewers extracted data. The main outcomes included quality of life, functionality in daily activities, use of resources, recovery rates (cluster of symptoms), and the incidence of new medical diagnoses. Data were pooled using a random effects model. The risk of bias was assessed with the Joanna Briggs Institute critical appraisal tool for cohort studies.

Results

We included 63 controlled cohort studies, encompassing more than 96 million participants. Based on five studies, we found a reduction in overall quality of life between individuals with confirmed SARS-CoV-2 infection versus controls at six to 24 months follow-up, although heterogeneity was very high (mean difference in EQ-5D scale -5.28 (95% confidence interval -7.88 to 2.68; I2=93.81%). Evidence from ten studies, which could not be pooled in a meta-analysis, indicated that an increased rate of functional impairment associated with SARS-CoV-2 infection. Use of care increased compared with controls at six to 24 months follow-up at intensive care units (risk ratio 2.00 (95% confidence interval 0.69 to 5.80), five studies, I2=91.96%) and in outpatient care (1.12 (1.01 to 1.24), seven studies, I2=99.51%). Regarding persistent symptoms, individuals with documented SARS-CoV-2 infection had an increased risk of having two or more persistent symptoms at follow-up, especially those related to neurological clusters (ie, risk ratio 1.51 (95% confidence interval 1.17 to 1.93), I2=98.91%). Evidence also showed an increased incidence of a wide variety of metabolic, cardiovascular, neurological, respiratory, haematological and other incident diagnoses.

Conclusion

Evidence suggests functional impairment after SARS-CoV-2 infection, in addition to a higher use of resources and a higher incidence of widely varying medical diagnoses. These results should be interpreted with caution, considering the high heterogeneity across studies and study limitations related to outcome measurement and attrition of participants.

Systematic review registration

Open Science Framework, osf.io/drm39.

Long COVID or Post-COVID-19 Condition: Past, Present and Future Research Directions

The presence of symptoms after an acute SARS-CoV-2 infection (long-COVID) has become a worldwide healthcare emergency but remains underestimated and undertreated due to a lack of recognition of the condition and knowledge of the underlying mechanisms. In fact, the prevalence of post-COVID symptoms ranges from 50% during the first months after the infection up to 20% two-years after. This perspective review aimed to map the existing literature on post-COVID symptoms and to identify gaps in the literature to guide the global effort toward an improved understanding of long-COVID and suggest future research directions. There is a plethora of symptomatology that can be due to COVID-19; however, today, there is no clear classification and definition of this condition, termed long-COVID or post-COVID-19 condition. The heterogeneity in the symptomatology has led to the presence of groups/clusters of patients, which could exhibit different risk factors and different mechanisms. Viral persistence, long-lasting inflammation, immune dysregulation, autoimmune reactions, reactivation of latent infections, endothelial dysfunction and alteration in gut microbiota have been proposed as potential mechanisms explaining the complexity of long-COVID. In such an equation, viral biology (e.g., re-infections, SARS-CoV-2 variants), host biology (e.g., genetics, epigenetics) and external factors (e.g., vaccination) should be also considered. These various factors will be discussed in the current perspective review and future directions suggested.

Is there a role for growth hormone replacement in adults to control acute and post-acute COVID-19?

The SARS-CoV-2 pandemic created a multitude of medical crossroads requiring real time adaptations of best practice covering preventative and interventional aspects of care. Among the many discoveries borne from efforts to address the myriad clinical presentations across multiple organ systems was a common impact on tissues with cells that express the ACE-2 receptor. The vast majority of acute infections began and often ended in the respiratory tract, but more recent evaluations have confirmed significant extrapulmonary manifestations including symptom clusters that extend beyond the acute phase of infection collectively referred to as "post-acute sequelae SARS-CoV-2 infection" (PASC) or more commonly as "long (-haul) COVID". Both acute SARS-CoV-2 infection and PASC are associated with gut microbiome dysbiosis and alterations in the gut-brain and HPA-axis in a subset of the infected. Mounting evidence suggests these extrapulmonary manifestations may ultimately lead to reduced growth hormone (GH) secretion as demonstrated following stimulation tests. Disrupted GH secretion could cause or exacerbate long lasting neuropsychological symptoms as seen in other similar manifesting conditions. Ongoing clinical research has shown promising improvement in PASC patients with fatigue and cognition complaints can be achieved via GH replacement therapy. GH stimulation testing should be considered in PASC workups and future research should delve deeper into the mechanistic effects of GH on acute COVID and PASC.