Identification of Distinct Long COVID Clinical Phenotypes Through Cluster Analysis of Self-Reported Symptoms

Identification of inflammatory clusters in long-COVID through analysis of plasma biomarker levels

Mechanisms underlying long COVID remain poorly understood. Patterns of immunological responses in individuals with long COVID may provide insight into clinical phenotypes. Here we aimed to identify these immunological patterns and study the inflammatory processes ongoing in individuals with long COVID. We applied an unsupervised hierarchical clustering approach to analyze plasma levels of 42 biomarkers measured in individuals with long COVID. Logistic regression models were used to explore associations between biomarker clusters, clinical variables, and symptom phenotypes. In 101 individuals, we identified three inflammatory clusters: a limited immune activation cluster, an innate immune activation cluster, and a systemic immune activation cluster. Membership in these inflammatory clusters did not correlate with individual symptoms or symptom phenotypes, but was associated with clinical variables including age, BMI, and vaccination status. Differences in serologic responses between clusters were also observed. Our results indicate that clinical variables of individuals with long COVID are associated with their inflammatory profiles and can provide insight into the ongoing immune responses.

Long COVID exhibits clinically distinct phenotypes at 3-6 months post-SARS-CoV-2 infection: results from the P4O2 consortium

BACKGROUND

Four months after SARS-CoV-2 infection, 22%-50% of COVID-19 patients still experience complaints. Long COVID is a heterogeneous disease and finding subtypes could aid in optimising and developing treatment for the individual patient.

METHODS

Data were collected from 95 patients in the P4O2 COVID-19 cohort at 3-6 months after infection. Unsupervised hierarchical clustering was performed on patient characteristics, characteristics from acute SARS-CoV-2 infection, long COVID symptom data, lung function and questionnaires describing the impact and severity of long COVID. To assess robustness, partitioning around medoids was used as alternative clustering.

RESULTS

Three distinct clusters of patients with long COVID were revealed. Cluster 1 (44%) represented predominantly female patients (93%) with pre-existing asthma and suffered from a median of four symptom categories, including fatigue and respiratory and neurological symptoms. They showed a milder SARS-CoV-2 infection. Cluster 2 (38%) consisted of predominantly male patients (83%) with cardiovascular disease (CVD) and suffered from a median of three symptom categories, most commonly respiratory and neurological symptoms. This cluster also showed a significantly lower forced expiratory volume within 1 s and diffusion capacity of the lung for carbon monoxide. Cluster 3 (18%) was predominantly male (88%) with pre-existing CVD and diabetes. This cluster showed the mildest long COVID, and suffered from symptoms in a median of one symptom category.

CONCLUSIONS

Long COVID patients can be clustered into three distinct phenotypes based on their clinical presentation and easily obtainable information. These clusters show distinction in patient characteristics, lung function, long COVID severity and acute SARS-CoV-2 infection severity. This clustering can help in selecting the most beneficial monitoring and/or treatment strategies for patients suffering from long COVID. Follow-up research is needed to reveal the underlying molecular mechanisms implicated in the different phenotypes and determine the efficacy of treatment.

Insights into early recovery from Long COVID-results from the German DigiHero Cohort

65 million people worldwide are estimated to suffer from long-term symptoms after their SARS-CoV-2 infection (Long COVID). However, there is still little information about the early recovery among those who initially developed Long COVID, i.e. had symptoms 4-12 weeks after infection but no symptoms after 12 weeks. We aimed to identify associated factors with this early recovery. We used data from SARS-CoV-2-infected individuals from the DigiHero study. Participants provided information about their SARS-CoV-2 infections and symptoms at the time of infection, 4-12 weeks, and more than 12 weeks post-infection. We performed multivariable logistic regression to identify factors associated with early recovery from Long COVID and principal component analysis (PCA) to identify groups among symptoms. 5098 participants reported symptoms at 4-12 weeks after their SARS-CoV-2 infection, of which 2441 (48%) reported no symptoms after 12 weeks. Men, younger participants, individuals with mild course of acute infection, individuals infected with the Omicron variant, and individuals who did not seek medical care in the 4-12 week period after infection had a higher chance of early recovery. In the PCA, we identified four distinct symptom groups. Our results indicate differential risk of continuing symptoms among individuals who developed Long COVID. The identified risk factors are similar to those for the development of Long COVID, so people with these characteristics are at higher risk not only for developing Long COVID, but also for longer persistence of symptoms. Those who sought medical help were also more likely to have persistent symptoms.

Cluster analysis of long COVID in Japan and association of its trajectory of symptoms and quality of life

BACKGROUND

Multiple prolonged symptoms observed in patients who recovered from COVID-19 are defined as long COVID. Although diverse phenotypic combinations are possible, they remain unclear. This study aimed to perform a cluster analysis of long COVID in Japan and clarify the association between its characteristics and background factors and quality of life (QOL).

METHODS

This multicentre prospective cohort study collected various symptoms and QOL after COVID-19 from January 2020 to February 2021. This study included 935 patients aged ≥18 years with COVID-19 at 26 participating medical facilities. Hierarchical cluster analysis was performed using 24 long COVID symptom at 3 months after diagnosis.

RESULTS

Participants were divided into the following five clusters: numerous symptoms across multiple organs (cluster 1, n=54); no or minor symptoms (cluster 2, n=546); taste and olfactory disorders (cluster 3, n=76); fatigue, psychoneurotic symptoms and dyspnoea (low prevalence of cough and sputum) (cluster 4, n=207) and fatigue and dyspnoea (high prevalence of cough and sputum) (cluster 5, n=52). Cluster 1 included elderly patients with severe symptoms, while cluster 3 included young female with mild symptoms. No significant differences were observed in the comorbidities. Cluster 1 showed the most impaired QOL, followed by clusters 4 and 5; these changes as well as the composition of symptoms were observed over 1 year.

CONCLUSIONS

We identified patients with long COVID with diverse characteristics into five clusters. Future analysis of these different pathologies could result in individualised treatment of long COVID.

TRIAL REGISTRATION NUMBER

The study protocol is registered at UMIN clinical trials registry (UMIN000042299).

Effects of Aerobic Exercise Therapy through Nordic Walking Program in Lactate Concentrations, Fatigue and Quality-of-Life in Patients with Long-COVID Syndrome: A Non-Randomized Parallel Controlled Trial

BACKGROUND

Long-COVID syndrome comprises a variety of signs and symptoms that develop during or after infection with COVID-19 which may affect the physical capabilities. However, there is a lack of studies investigating the effects of Long-COVID syndrome in sport capabilities after suffering from COVID-19 infection. The purpose of the study was to evaluate and compare lactate concentration and quality of life (QoL) in patients with Long-COVID with those who have not developed non-Long-COVID during Nordic walking exercise therapy.

METHODS

Twenty-nine patients (25.5 ± 7.1 years) took part in a non-randomized controlled trial, divided into two groups: a Long-COVID group (n = 16) and a non-Long-COVID control (n = 13). Patients were confirmed as having Long-COVID syndrome if they experienced fatigue or tiredness when performing daily activities and worsening of symptoms after vigorous physical or mental activity. All participants underwent a 12-week Nordic Walking program. Lactate concentration after exercise and distance covered during all sessions were measured. Pre- and Long-Nordic Walking program, the Modified Fatigue Impact Scale (MFIS), the Short Form 36 Health Survey (SF-36), and EURO QoL-5D (EQ-ED) were administered to assess fatigue and quality of life, respectively.

RESULTS

There was a lactate concentration effect between groups (F = 5.604; p = 0.024). However, there was no significant effect as a result of the session (F = 3.521; p = 0.121) with no interaction of group × session (F = 1.345; p = 0.414). The group main effect (F = 23.088; p < 0.001), time effect (F = 6.625; p = 0.026), and group × time (F = 4.632; p = 0.002) interaction on the SF-36 scale were noted. Also, there were a significant group main effect (F = 38.372; p < 0.001), time effect (F = 12.424; p = 0.005), and group × time interaction (F = 4.340; p = 0.014) on EQ-5D. However, there was only a significant group main effect (F = 26.235; p < 0.001) with no effect on time (F = 2.265; p = 0.160) and group × time (F = 1.584; p = 0.234) interaction on the MFIS scale.

CONCLUSIONS

The Long-COVID group showed higher lactate concentration compared with the control group during the 12 weeks of the Nordic Walking program. The Long-COVID group presented a decrease in fatigue with respect to the control group according to the MFIS scale, as well as improvement in quality of life after aerobic exercise therapy.

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.

A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks

Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, clinical, and histopathologic characteristics. There are more than 35 different histologic forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch-matching tools, allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the World Health Organization breast taxonomy (Classification of Tumors fifth ed.) spanning 35 tumor types. We visualized all tumor types using deep features extracted from a state-of-the-art deep-learning model, pretrained on millions of diagnostic histopathology images from the Cancer Genome Atlas repository. Furthermore, we tested the concept of a digital "atlas" as a reference for search and matching with rare test cases. The patch similarity search within the World Health Organization breast taxonomy data reached >88% accuracy when validating through "majority vote" and >91% accuracy when validating using top n tumor types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.

Sleep and memory complaints in long COVID: an insight into clustered psychological phenotypes

This study evaluated clinical features of individuals with long COVID (5-8 months after diagnosis) who reported sleep and memory problems (62 cases) compared to those without (52 controls). Both groups had a similar mean age (41 vs. 39 years). Around 86% of the participants were non-hospitalized at the time of infection, and none of them were vaccinated at that point. Subsequently, both cases and controls received the vaccine; however, the vaccination rates differed significantly between the groups (30.7% vs. 51.0%). Cases and controls had similar rates of symptoms at acute COVID phase. However, cases were more likely to experience coryza, dyspnea, headache, and nausea/vomiting during long COVID. Regarding new-onset symptoms in long COVID, 12.9% of cases had dyspnea, and 14.5% experienced nausea/vomiting, whereas in the control group there were only 1.9% and 0.0%, respectively. Cases also had a significantly higher prevalence of persistent headache (22.6% vs. 7.7%), and dyspnea (12.9% vs. 0.0). In addition, cases also showed an increased rate of mental health complaints: disability in daily activities (45.2% vs. 9.6%; P < 0.001); concentration/sustained attention difficulties (74.2% vs. 9.6%; P < 0.001); anxiety-Generalized Anxiety Disorder 2-item scale (GAD-2) ≥ 3 (66.1% vs. 34.6%; P = 0.0013); and "post-COVID sadness" (82.3% vs. 40.4%; P < 0.001). We observed a significant correlation between sadness and anxiety in cases, which was not observed in controls (P=0.0212; Spearman correlation test). Furthermore, the frequency of concomitant sadness and anxiety was markedly higher in cases compared to controls (59.7% vs. 19.2%) (P < 0.0001; Mann-Whitney test). These findings highlight a noteworthy association between sadness and anxiety specifically in cases. In conclusion, our data identified concurrent psychological phenotypes in individuals experiencing sleep and memory disturbances during long COVID. This strengthens the existing evidence that SARS-CoV-2 causes widespread brain pathology with interconnected phenotypic clusters. This finding highlights the need for comprehensive medical attention to address these complex issues, as well as major investments in testing strategies capable of preventing the development of long COVID sequelae, such as vaccination.

A statewide population-based approach to examining Long COVID symptom prevalence and predictors in Michigan

OBJECTIVE

The current broad definition of Long COVID, and an overreliance on clinical and convenience samples, is leading to a wide array of Long COVID estimates with limited generalizability. Our objective was to examine Long COVID symptoms using a statewide population-based probability sample.

METHODS

Among 8000 sampled adults with polymerase-chain-reaction-confirmed SARS-CoV-2 between June 2020 and July 2021 in the Michigan Disease Surveillance System, 2533 completed our survey (response rate 32.2%). Using modified Poisson regression, we examined sociodemographic, behavioral, and clinical predictors of eight Long COVID symptom clusters, defined as at least one applicable symptom lasting 90 or more days post COVID-19 onset.

RESULTS

Neuropsychiatric Long COVID symptoms, including brain fog, were most prevalent (23.7%), followed by systemic symptoms (17.1%), including fatigue, musculoskeletal (11.4%), pulmonary (10.4%), dermatologic (6.7%), cardiovascular (6.1%), gastrointestinal (5.4%), and ear, nose, and throat symptoms (5.3%). In adjusted analyses, female sex, a pre-existing psychological condition, and intensive care unit admission were strong predictors of most Long COVID symptom clusters. Older age was not associated with a higher prevalence of all symptoms - cardiovascular and dermatologic symptoms were most prevalent among middle-aged adults and age was not associated with neuropsychiatric or gastrointestinal symptoms. Additionally, there were fewer associations between pre-existing conditions and cardiovascular, neuropsychiatric, and dermatologic symptoms compared to other symptom clusters.

CONCLUSIONS

While many predictors of Long COVID symptom clusters were similar, the relationship with age and pre-existing conditions varied across clusters. Cardiovascular, neuropsychiatric, and dermatologic symptoms require further study as potentially distinct from other Long COVID symptoms.

"The burden of post-acute COVID-19 symptoms in a multinational network cohort analysis"

Persistent symptoms following the acute phase of COVID-19 present a major burden to both the affected and the wider community. We conducted a cohort study including over 856,840 first COVID-19 cases, 72,422 re-infections and more than 3.1 million first negative-test controls from primary care electronic health records from Spain and the UK (Sept 2020 to Jan 2022 (UK)/March 2022 (Spain)). We characterised post-acute COVID-19 symptoms and identified key symptoms associated with persistent disease. We estimated incidence rates of persisting symptoms in the general population and among COVID-19 patients over time. Subsequently, we investigated which WHO-listed symptoms were particularly differential by comparing their frequency in COVID-19 cases vs. matched test-negative controls. Lastly, we compared persistent symptoms after first infections vs. reinfections.Our study shows that the proportion of COVID-19 cases affected by persistent post-acute COVID-19 symptoms declined over the study period. Risk for altered smell/taste was consistently higher in patients with COVID-19 vs test-negative controls. Persistent symptoms were more common after reinfection than following a first infection. More research is needed into the definition of long COVID, and the effect of interventions to minimise the risk and impact of persistent symptoms.

Impact of vaccination and variants of concern on long COVID clinical phenotypes

BACKGROUND

Defining patterns of symptoms in long COVID is necessary to advance therapies for this heterogeneous condition. Here we aimed to describe clusters of symptoms in individuals with long COVID and explore the impact of the emergence of variants of concern (VOCs) and vaccination on these clusters.

METHODS

In a prospective, multi centre cohort study, individuals with symptoms persisting > 4 weeks from acute COVID-19 were divided into two groups based on timing of acute infection; pre-Alpha VOC, denoted wild type (WT) group and post-Alpha VOC (incorporating alpha and delta dominant periods) denoted VOC group. We used multiple correspondence analysis (MCA) and hierarchical clustering in the WT and VOC groups to identify symptom clusters. We then used logistic regression to explore factors associated with individual symptoms.

RESULTS

A total of 417 individuals were included in the analysis, 268 in WT and 149 in VOC groups respectively. In both groups MCA identified three similar clusters; a musculoskeletal (MSK) cluster characterised by joint pain and myalgia, a cardiorespiratory cluster and a less symptomatic cluster. Differences in characteristic symptoms were only seen in the cardiorespiratory cluster where a decrease in the frequency of palpitations (10% vs 34% p = 0.008) and an increase in cough (63% vs 17% p < 0.001) in the VOC compared to WT groups was observed. Analysis of the frequency of individual symptoms showed significantly lower frequency of both chest pain (25% vs 39% p = 0.004) and palpitations (12% vs 32% p < 0.001) in the VOC group compared to the WT group. In adjusted analysis being in the VOC group was significantly associated with a lower odds of both chest pain and palpitations, but vaccination was not associated with these symptoms.

CONCLUSION

This study suggests changes in long COVID phenotype in individuals infected later in the pandemic, with less palpitations and chest pain reported. Adjusted analyses suggest that these effects are mediated through introduction of variants rather than an effect from vaccination.

Five cluster classifications of long COVID and their background factors: A cross-sectional study in Japan

PURPOSE

The long-term symptoms of coronavirus disease 2019 (COVID-19), i.e., long COVID, have drawn research attention. Evaluating its subjective symptoms is difficult, and no established pathophysiology or treatment exists. Although there are several reports of long COVID classifications, there are no reports comparing classifications that include patient characteristics, such as autonomic dysfunction and work status. We aimed to classify patients into clusters based on their subjective symptoms during their first outpatient visit and evaluate their background for these clusters.

METHODS

Included patients visited our outpatient clinic between January 18, 2021, and May 30, 2022. They were aged ≥ 15 years and confirmed to have SARS-CoV-2 infection and residual symptoms lasting at least 2 months post-infection. Patients were evaluated using a 3-point scale for 23 symptoms and classified into five clusters (1. fatigue only; 2. fatigue, dyspnea, chest pain, palpitations, and forgetfulness; 3. fatigue, headache, insomnia, anxiety, motivation loss, low mood, and forgetfulness; 4. hair loss; and 5. taste and smell disorders) using CLUSTER. For continuous variables, each cluster was compared using the Kruskal-Wallis test. Multiple comparison tests were performed using the Dunn's test for significant results. For nominal variables, a Chi-square test was performed; for significant results, a residual analysis was conducted with the adjusted residuals.

RESULTS

Compared to patients in other cluster categories, those in cluster categories 2 and 3 had higher proportions of autonomic nervous system disorders and leaves of absence, respectively.

CONCLUSIONS

Long COVID cluster classification provided an overall assessment of COVID-19. Different treatment strategies must be used based on physical and psychiatric symptoms and employment factors.

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

More than 100 symptoms have been reported for post-coronavirus disease 2019 (COVID-19) and this study aimed to organize self-reported symptoms by identifying symptom clusters. We used a cross-sectional survey with a convenience sample of 491 adults who reported experiencing prolonged symptoms of COVID. A list of 25 symptoms of post-COVID-19 was used to measure the symptoms, and exploratory factor analysis was undertaken to identify symptom clusters for people with symptoms lasting 5 to 8 weeks and 9 weeks or longer. Six symptom clusters were identified for each of the two groups, and five clusters were similar across both groups: respiratory, general viral, smell/taste, cognitive cardiac, and mental health. The >9-week group reported symptoms primarily from two factors: respiratory-muscular and mental health. Post-COVID-19 symptom clusters differ across timeframes. Symptom clusters were useful in establishing coherent patterns of multiple complex symptoms.

Persistent chest pain following COVID-19 infection - A scoping review

Persistent chest pain (PCP) following acute COVID-19 infection is a commonly reported symptom with an unclear etiology, making its management challenging. This scoping review aims to address the knowledge gap surrounding the characteristics of PCP following COVID-19, its causes, and potential treatments. This is a scoping review of 64 studies, including observational (prospective, retrospective, cross-sectional, case series, and case-control) and one quasi-experimental study, from databases including Embase, PubMed/MEDLINE, Cochrane CENTRAL, Google Scholar, Cochrane Database of Systematic Reviews, and Scopus. Studies on patients with PCP following mild, moderate, and severe COVID-19 infection were included. Studies with patients of any age, with chest pain that persisted following acute COVID-19 disease, irrespective of etiology or duration were included. A total of 35 studies reported PCP symptoms following COVID-19 (0.24%-76.6%) at an average follow-up of 3 months or longer, 12 studies at 1-3 months and 17 studies at less than 1-month follow-up or not specified. PCP was common following mild-severe COVID-19 infection, and etiology was mostly not reported. Fourteen studies proposed potential etiologies including endothelial dysfunction, cardiac ischemia, vasospasm, myocarditis, cardiac arrhythmia, pneumonia, pulmonary embolism, postural tachycardia syndrome, or noted cardiac MRI (cMRI) changes. Evaluation methods included common cardiopulmonary tests, as well as less common tests such as flow-mediated dilatation, cMRI, single-photon emission computed tomography myocardial perfusion imaging, and cardiopulmonary exercise testing. Only one study reported a specific treatment (sulodexide). PCP is a prevalent symptom following COVID-19 infection, with various proposed etiologies. Further research is needed to establish a better understanding of the causes and to develop targeted treatments for PCP following COVID-19.

Distinct temporal trajectories and risk factors for Post-acute sequelae of SARS-CoV-2 infection

Background

The understanding of Post-acute sequelae of SARS-CoV-2 infection (PASC) can be improved by longitudinal assessment of symptoms encompassing the acute illness period. To gain insight into the various disease trajectories of PASC, we assessed symptom evolution and clinical factors associated with the development of PASC over 3 months, starting with the acute illness period.

Methods

We conducted a prospective cohort study to identify parameters associated with PASC. We performed cluster and case control analyses of clinical data, including symptomatology collected over 3 months following infection.

Results

We identified three phenotypic clusters associated with PASC that could be characterized as remittent, persistent, or incident based on the 3-month change in symptom number compared to study entry: remittent (median; min, max: -4; -17, 3), persistent (-2; -14, 7), or incident (4.5; -5, 17) (p = 0.041 remittent vs. persistent, p < 0.001 remittent vs. incident, p < 0.001 persistent vs. incident). Despite younger age and lower hospitalization rates, the incident phenotype had a greater number of symptoms (15; 8, 24) and a higher proportion of participants with PASC (63.2%) than the persistent (6; 2, 9 and 52.2%) or remittent clusters (1; 0, 6 and 18.7%). Systemic corticosteroid administration during acute infection was also associated with PASC at 3 months [OR (95% CI): 2.23 (1.14, 4.36)].

Conclusion

An incident disease phenotype characterized by symptoms that were absent during acute illness and the observed association with high dose steroids during acute illness have potential critical implications for preventing PASC.

Immunological profiling in long COVID: overall low grade inflammation and T-lymphocyte senescence and increased monocyte activation correlating with increasing fatigue severity

Background

Many patients with SARS-CoV-2 infection develop long COVID with fatigue as one of the most disabling symptoms. We performed clinical and immune profiling of fatigued and non-fatigued long COVID patients and age- and sex-matched healthy controls (HCs).

Methods

Long COVID symptoms were assessed using patient-reported outcome measures, including the fatigue assessment scale (FAS, scores ≥22 denote fatigue), and followed up to one year after hospital discharge. We assessed inflammation-related genes in circulating monocytes, serum levels of inflammation-regulating cytokines, and leukocyte and lymphocyte subsets, including major monocyte subsets and senescent T-lymphocytes, at 3-6 months post-discharge.

Results

We included 37 fatigued and 36 non-fatigued long COVID patients and 42 HCs. Fatigued long COVID patients represented a more severe clinical profile than non-fatigued patients, with many concurrent symptoms (median 9 [IQR 5.0-10.0] vs 3 [1.0-5.0] symptoms, p<0.001), and signs of cognitive failure (41%) and depression (>24%). Immune abnormalities that were found in the entire group of long COVID patients were low grade inflammation (increased inflammatory gene expression in monocytes, increased serum pro-inflammatory cytokines) and signs of T-lymphocyte senescence (increased exhausted CD8+ TEMRA-lymphocytes). Immune profiles did not significantly differ between fatigued and non-fatigued long COVID groups. However, the severity of fatigue (total FAS score) significantly correlated with increases of intermediate and non-classical monocytes, upregulated gene levels of CCL2, CCL7, and SERPINB2 in monocytes, increases in serum Galectin-9, and higher CD8+ T-lymphocyte counts.

Conclusion

Long COVID with fatigue is associated with many concurrent and persistent symptoms lasting up to one year after hospitalization. Increased fatigue severity associated with stronger signs of monocyte activation in long COVID patients and potentially point in the direction of monocyte-endothelial interaction. These abnormalities were present against a background of immune abnormalities common to the entire group of long COVID patients.

Classification of post COVID-19 condition symptoms: a longitudinal study in the Belgian population

OBJECTIVES

Since the onset of the COVID-19 pandemic, most research has focused on its acute pathophysiology, yet some people tend to experience persisting symptoms beyond the acute phase of infection, referred to as post COVID-19 condition (PCC). However, evidence on PCC is still scarce. This study aimed to assess the distribution, classification of symptoms and associated factors of PCC in adults.

DESIGN

Longitudinal online cohort study.

SETTING

National study in Belgium.

PARTICIPANTS

Participants were Belgian adults with a recent SARS-CoV-2 infection and were recruited when called up for contact tracing. A total of 3039 participants were included and completed an online questionnaire at the time of their infection and again 3 months later.

OUTCOME MEASURES

The baseline questionnaire assessed the initial health status of the participants and their status during the acute phase of the infection. The follow-up questionnaire assessed their PCC status 3 months after infection. A latent class analysis (LCA) was performed to assess whether there are different classes of individuals with a similar set of self-reported PCC symptoms.

RESULTS

Half of the participants reported PCC 3 months after infection (47%). The most frequent symptoms were fatigue (21%), headache (11%) and memory problems (10%). The LCA highlighted three different classes of PCC symptoms with different risk factors: (1) a combination of loss of smell and taste, (2) a combination of neurological symptoms and (3) other heterogeneous symptoms.

CONCLUSIONS

With the increasing number of people who underwent COVID-19, PCC has become an important but complex public health problem due to the heterogeneity of its symptoms. The classification of symptoms performed in this study can help give insight into different aetiologies of PCC and better plan care according to the symptoms and needs of those affected.

Determinants of the onset and prognosis of the post-COVID-19 condition: a 2-year prospective observational cohort study

Background

At least 5-10% of subjects surviving COVID-19 develop the post-COVID-19 condition (PCC) or "Long COVID". The clinical presentation of PCC is heterogeneous, its pathogenesis is being deciphered, and objective, validated biomarkers are lacking. It is unknown if PCC is a single entity or a heterogeneous syndrome with overlapping pathophysiological basis. The large US RECOVER study identified four clusters of subjects with PCC according to their presenting symptoms. However, the long-term clinical implications of PCC remain unknown.

Methods

We conducted a 2-year prospective cohort study of subjects surviving COVID-19, including individuals fulfilling the WHO PCC definition and subjects with full clinical recovery. We systematically collected post-COVID-19 symptoms using prespecified questionnaires and performed additional diagnostic imaging tests when needed. Factors associated with PCC were identified and modelled using logistic regression. Unsupervised clustering analysis was used to group subjects with PCC according to their presenting symptoms. Factors associated with PCC recovery were modelled using a direct acyclic graph approach.

Findings

The study included 548 individuals, 341 with PCC, followed for a median of 23 months (IQR 16.5-23.5), and 207 subjects fully recovered. In the model with the best fit, subjects who were male and had tertiary studies were less likely to develop PCC, whereas a history of headache, or presence of tachycardia, fatigue, neurocognitive and neurosensitive complaints and dyspnea at COVID-19 diagnosis predicted the development of PCC. The cluster analysis revealed the presence of three symptom clusters with an additive number of symptoms. Only 26 subjects (7.6%) recovered from PCC during follow-up; almost all of them (n = 24) belonged to the less symptomatic cluster A, dominated mainly by fatigue. Recovery from PCC was more likely in subjects who were male, required ICU admission, or had cardiovascular comorbidities, hyporexia and/or smell/taste alterations during acute COVID-19. Subjects presenting with muscle pain, impaired attention, dyspnea, or tachycardia, conversely, were less likely to recover from PCC.

Interpretation

Preexisting medical and socioeconomic factors, as well as acute COVID-19 symptoms, are associated with the development of and recovery from the PCC. Recovery is extremely rare during the first 2 years, posing a major challenge to healthcare systems.

Funding

Fundació Lluita contra les Infeccions.

Advancing the Management of Long COVID by Integrating into Health Informatics Domain: Current and Future Perspectives

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. To effectively monitor the health of those affected, maintaining up-to-date health records is essential, and digital health informatics apps for surveillance play a pivotal role. In this review, we overview the existing literature on identifying and characterizing long COVID manifestations through hierarchical classification based on Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) initiative in artificial intelligence (AI) to identify long COVID. Through knowledge exploration, we present a concept map of clinical pathways for long COVID, which offers insights into the data required and explores innovative frameworks for health informatics apps for tackling the long-term effects of COVID-19. This study achieves two main objectives by comprehensively reviewing long COVID identification and characterization techniques, making it the first paper to explore incorporating long COVID as a variable risk factor within a digital health informatics application. By achieving these objectives, it provides valuable insights on long COVID's challenges and impact on public health.

Long Covid symptoms and diagnosis in primary care: A cohort study using structured and unstructured data in The Health Improvement Network primary care database

BACKGROUND

Long Covid is a widely recognised consequence of COVID-19 infection, but little is known about the burden of symptoms that patients present with in primary care, as these are typically recorded only in free text clinical notes.

AIMS

To compare symptoms in patients with and without a history of COVID-19, and investigate symptoms associated with a Long Covid diagnosis.

METHODS

We used primary care electronic health record data until the end of December 2020 from The Health Improvement Network (THIN), a Cegedim database. We included adults registered with participating practices in England, Scotland or Wales. We extracted information about 89 symptoms and 'Long Covid' diagnoses from free text using natural language processing. We calculated hazard ratios (adjusted for age, sex, baseline medical conditions and prior symptoms) for each symptom from 12 weeks after the COVID-19 diagnosis.

RESULTS

We compared 11,015 patients with confirmed COVID-19 and 18,098 unexposed controls. Only 20% of symptom records were coded, with 80% in free text. A wide range of symptoms were associated with COVID-19 at least 12 weeks post-infection, with strongest associations for fatigue (adjusted hazard ratio (aHR) 3.46, 95% confidence interval (CI) 2.87, 4.17), shortness of breath (aHR 2.89, 95% CI 2.48, 3.36), palpitations (aHR 2.59, 95% CI 1.86, 3.60), and phlegm (aHR 2.43, 95% CI 1.65, 3.59). However, a limited subset of symptoms were recorded within 7 days prior to a Long Covid diagnosis in more than 20% of cases: shortness of breath, chest pain, pain, fatigue, cough, and anxiety / depression.

CONCLUSIONS

Numerous symptoms are reported to primary care at least 12 weeks after COVID-19 infection, but only a subset are commonly associated with a GP diagnosis of Long Covid.

Ethno-demographic disparities in humoral responses to the COVID-19 vaccine among healthcare workers

The COVID-19 pandemic had a profound impact on global health, but rapid vaccine administration resulted in a significant decline in morbidity and mortality rates worldwide. In this study, we sought to explore the temporal changes in the humoral immune response against SARS-CoV-2 healthcare workers (HCWs) in Augusta, GA, USA, and investigate any potential associations with ethno-demographic features. Specifically, we aimed to compare the naturally infected individuals with naïve individuals to understand the immune response dynamics after SARS-CoV-2 vaccination. A total of 290 HCWs were included and assessed prospectively in this study. COVID status was determined using a saliva-based COVID assay. Neutralizing antibody (NAb) levels were quantified using a chemiluminescent immunoassay system, and IgG levels were measured using an enzyme-linked immunosorbent assay method. We examined the changes in antibody levels among participants using different statistical tests including logistic regression and multiple correspondence analysis. Our findings revealed a significant decline in NAb and IgG levels at 8-12 months postvaccination. Furthermore, a multivariable analysis indicated that this decline was more pronounced in White HCWs (odds ratio [OR] = 2.1, 95% confidence interval [CI] = 1.07-4.08, p = 0.02) and IgG (OR = 2.07, 95% CI = 1.04-4.11, p = 0.03) among the whole cohort. Booster doses significantly increased IgG and NAb levels, while a decline in antibody levels was observed in participants without booster doses at 12 months postvaccination. Our results highlight the importance of understanding the dynamics of immune response and the potential influence of demographic factors on waning immunity to SARS-CoV-2. In addition, our findings emphasize the value of booster doses to ensure durable immunity.

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

OBJECTIVES

The objective of this study was to characterize post-COVID condition symptoms and symptom clusters, their duration, and prevalence.

METHODS

We conducted a systematic review and random-effects meta-analysis of studies reporting post-COVID-19 symptoms and clusters, respectively. We searched MEDLINE (via PubMed), Scopus, Web of Science, Science Direct, Google Scholar, EBSCOhost, EMBASE, PsycINFO, Cochrane Library, and Mednar for literature reporting on the post-COVID condition up to August 2022.

RESULTS

In the 76 included studies, we found that although most symptoms were reported less frequently 7-12 months after infection compared to earlier, over 20% of patients reported at least one post-COVID condition-compatible symptom. In the seven studies reporting post-COVID symptom clusters, neurological clustering was consistently identified, followed by cardiorespiratory and systemic/inflammatory.

CONCLUSION

Post-COVID symptom clustering provides direction for research into the etiology, diagnosis, and management of post-COVID conditions. Studies reporting post-COVID symptom clusters remain rare due to the focus on individual symptom reporting. Studies on post-COVID symptom clusters should replace individual symptom reporting to accelerate our understanding of this emerging public health issue.

Clinical phenotypes and quality of life to define post-COVID-19 syndrome: a cluster analysis of the multinational, prospective ORCHESTRA cohort

Background

Lack of specific definitions of clinical characteristics, disease severity, and risk and preventive factors of post-COVID-19 syndrome (PCS) severely impacts research and discovery of new preventive and therapeutics drugs.

Methods

This prospective multicenter cohort study was conducted from February 2020 to June 2022 in 5 countries, enrolling SARS-CoV-2 out- and in-patients followed at 3-, 6-, and 12-month from diagnosis, with assessment of clinical and biochemical features, antibody (Ab) response, Variant of Concern (VoC), and physical and mental quality of life (QoL). Outcome of interest was identification of risk and protective factors of PCS by clinical phenotype, setting, severity of disease, treatment, and vaccination status. We used SF-36 questionnaire to assess evolution in QoL index during follow-up and unsupervised machine learning algorithms (principal component analysis, PCA) to explore symptom clusters. Severity of PCS was defined by clinical phenotype and QoL. We also used generalized linear models to analyse the impact of PCS on QoL and associated risk and preventive factors. CT registration number: NCT05097677.

Findings

Among 1796 patients enrolled, 1030 (57%) suffered from at least one symptom at 12-month. PCA identified 4 clinical phenotypes: chronic fatigue-like syndrome (CFs: fatigue, headache and memory loss, 757 patients, 42%), respiratory syndrome (REs: cough and dyspnoea, 502, 23%); chronic pain syndrome (CPs: arthralgia and myalgia, 399, 22%); and neurosensorial syndrome (NSs: alteration in taste and smell, 197, 11%). Determinants of clinical phenotypes were different (all comparisons p < 0.05): being female increased risk of CPs, NSs, and CFs; chronic pulmonary diseases of REs; neurological symptoms at SARS-CoV-2 diagnosis of REs, NSs, and CFs; oxygen therapy of CFs and REs; and gastrointestinal symptoms at SARS-CoV-2 diagnosis of CFs. Early treatment of SARS-CoV-2 infection with monoclonal Ab (all clinical phenotypes), corticosteroids therapy for mild/severe cases (NSs), and SARS-CoV-2 vaccination (CPs) were less likely to be associated to PCS (all comparisons p < 0.05). Highest reduction in QoL was detected in REs and CPs (43.57 and 43.86 vs 57.32 in PCS-negative controls, p < 0.001). Female sex (p < 0.001), gastrointestinal symptoms (p = 0.034) and renal complications (p = 0.002) during the acute infection were likely to increase risk of severe PCS (QoL <50). Vaccination and early treatment with monoclonal Ab reduced the risk of severe PCS (p = 0.01 and p = 0.03, respectively).

Interpretation

Our study provides new evidence suggesting that PCS can be classified by clinical phenotypes with different impact on QoL, underlying possible different pathogenic mechanisms. We identified factors associated to each clinical phenotype and to severe PCS. These results might help in designing pathogenesis studies and in selecting high-risk patients for inclusion in therapeutic and management clinical trials.

Funding

The study received funding from the Horizon 2020 ORCHESTRA project, grant 101016167; from the Netherlands Organisation for Health Research and Development (ZonMw), grant 10430012010023; from Inserm, REACTing (REsearch & ACtion emergING infectious diseases) consortium and the French Ministry of Health, grant PHRC 20-0424.

Considerations in Children and Adolescents Related to Coronavirus Disease 2019 (COVID-19)

Pediatric post-acute sequelae of SARS-CoV-2 (PASC) or "long COVID" are a complex multisystemic disease that affects children's physical, social, and mental health. PASC has a variable presentation, time course, and severity and can affect children even with mild or asymptomatic acute COVID-19 symptoms. Screening for PASC in children with a history of SARS-CoV-2 infection is important for early detection and intervention. A multifaceted treatment approach and utilization of multidisciplinary care, if available, are beneficial in managing the complexities of PASC. Lifestyle interventions, physical rehabilitation, and mental health management are important treatment approaches to improve pediatric PASC patients' quality of life.

Identification of post-COVID-19 condition phenotypes, and differences in health-related quality of life and healthcare use: a cluster analysis

The aim of this cross-sectional study was to identify post-COVID-19 condition (PCC) phenotypes and to investigate the health-related quality of life (HRQoL) and healthcare use per phenotype. We administered a questionnaire to a cohort of PCC patients that included items on socio-demographics, medical characteristics, health symptoms, healthcare use, and the EQ-5D-5L. A principal component analysis (PCA) of PCC symptoms was performed to identify symptom patterns. K-means clustering was used to identify phenotypes. In total, 8630 participants completed the survey. The median number of symptoms was 18, with the top 3 being fatigue, concentration problems, and decreased physical condition. Eight symptom patterns and three phenotypes were identified. Phenotype 1 comprised participants with a lower-than-average number of symptoms, phenotype 2 with an average number of symptoms, and phenotype 3 with a higher-than-average number of symptoms. Compared to participants in phenotypes 1 and 2, those in phenotype 3 consulted significantly more healthcare providers (median 4, 6, and 7, respectively, p < 0.001) and had a significantly worse HRQoL (p < 0.001). In conclusion, number of symptoms rather than type of symptom was the driver in the identification of PCC phenotypes. Experiencing a higher number of symptoms is associated with a lower HRQoL and more healthcare use.

Long COVID Clinical Phenotypes up to 6 Months After Infection Identified by Latent Class Analysis of Self-Reported Symptoms

Background

The prevalence, incidence, and interrelationships of persistent symptoms after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection vary. There are limited data on specific phenotypes of persistent symptoms. Using latent class analysis (LCA) modeling, we sought to identify whether specific phenotypes of COVID-19 were present 3 months and 6 months post-infection.

Methods

This was a multicenter study of symptomatic adults tested for SARS-CoV-2 with prospectively collected data on general symptoms and fatigue-related symptoms up to 6 months postdiagnosis. Using LCA, we identified symptomatically homogenous groups among COVID-positive and COVID-negative participants at each time period for both general and fatigue-related symptoms.

Results

Among 5963 baseline participants (4504 COVID-positive and 1459 COVID-negative), 4056 had 3-month and 2856 had 6-month data at the time of analysis. We identified 4 distinct phenotypes of post-COVID conditions (PCCs) at 3 and 6 months for both general and fatigue-related symptoms; minimal-symptom groups represented 70% of participants at 3 and 6 months. When compared with the COVID-negative cohort, COVID-positive participants had higher occurrence of loss of taste/smell and cognition problems. There was substantial class-switching over time; those in 1 symptom class at 3 months were equally likely to remain or enter a new phenotype at 6 months.

Conclusions

We identified distinct classes of PCC phenotypes for general and fatigue-related symptoms. Most participants had minimal or no symptoms at 3 and 6 months of follow-up. Significant proportions of participants changed symptom groups over time, suggesting that symptoms present during the acute illness may differ from prolonged symptoms and that PCCs may have a more dynamic nature than previously recognized. Clinical Trials Registration. NCT04610515.

Characterization of neuroinflammation pattern in anti-LGI1 encephalitis based on TSPO PET and symptom clustering analysis

PURPOSE

TSPO PET with radioligand [18F]DPA-714 is an emerging molecular imaging technique that reflects cerebral inflammation and microglial activation, and it has been recently used in central nervous system diseases. In this study, we aimed to investigate the neuroinflammation pattern of anti-leucine-rich glioma-inactivated 1 (LGI1) protein autoimmune encephalitis (AIE) and to evaluate its possible correlation with clinical phenotypes.

METHODS

Twenty patients with anti-LGI1 encephalitis from the autoimmune encephalitis cohort in Huashan Hospital and ten with other AIE and non-inflammatory diseases that underwent TSPO PET imaging were included in the current study. Increased regional [18F]DPA-714 retention in anti-LGI1 encephalitis was detected on a voxel basis using statistic parametric mapping analysis. Multiple correspondence analysis and hierarchical clustering were conducted for discriminate subgroups in anti-LGI1 encephalitis. Standardized uptake value ratios normalized to the cerebellum (SUVRc) were calculated for semiquantitative analysis of TSPO PET features between different LGI1-AIE subgroups.

RESULTS

Increased regional retention of [18F]DPA-714 was identified in the bilateral hippocampus, caudate nucleus, and frontal cortex in LGI1-AIE patients. Two subgroups of LGI1-AIE patients were distinguished based on the top seven common symptoms. Patients in cluster 1 had a high frequency of facio-brachial dystonic seizures than those in cluster 2 (p = 0.004), whereas patients in cluster 2 had a higher frequency of general tonic-clonic (GTC) seizures than those in cluster 1 (p < 0.001). Supplementary motor area and superior frontal gyrus showed higher [18F]DPA-714 retention in cluster 2 patients compared with those in cluster 1 (p = 0.024; p = 0.04, respectively).

CONCLUSIONS

Anti-LGI1 encephalitis had a distinctive molecular imaging pattern presented by TSPO PET scan. LGI1-AIE patients with higher retention of [18F]DPA-714 in the frontal cortex were more prone to present with GTC seizures. Further studies are required for verifying its value in clinical application.

Long COVID: An approach to clinical assessment and management in primary care

Long COVID is an emerging public health threat, following swiftly behind the surges of acute infection over the course of the COVID-19 pandemic. It is estimated that there are already approximately 100 million people suffering from Long COVID globally, 0.5 million of whom are South African, and for whom our incomplete understanding of the condition has forestalled appropriate diagnosis and clinical care. There are several leading postulates for the complex, multi-mechanistic pathogenesis of Long COVID. Patients with Long COVID may present with a diversity of clinical phenotypes, often with significant overlap, which may exhibit temporal heterogeneity and evolution. Post-acute care follow-up, targeted screening, diagnosis, a broad initial assessment and more directed subsequent assessments are necessary at the primary care level. Symptomatic treatment, self-management and rehabilitation are the mainstays of clinical care for Long COVID. However, evidence-based pharmacological interventions for the prevention and treatment of Long COVID are beginning to emerge. This article presents a rational approach for assessing and managing patients with Long COVID in the primary care setting.

Use of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study

OBJECTIVES

We sought to 1) identify long COVID phenotypes based on patient reported outcome measures (PROMs) and 2) determine whether the phenotypes were associated with quality of life (QoL) and/or lung function.

METHODS

This was a longitudinal cohort study of hospitalized and non-hospitalized patients from March 2020 to January 2022 that was conducted across 4 Post-COVID Recovery Clinics in British Columbia, Canada. Latent class analysis was used to identify long COVID phenotypes using baseline PROMs (fatigue, dyspnea, cough, anxiety, depression, and post-traumatic stress disorder). We then explored the association between the phenotypes and QoL (using the EuroQoL 5 dimensions visual analogue scale [EQ5D VAS]) and lung function (using the diffusing capacity of the lung for carbon monoxide [DLCO]).

RESULTS

There were 1,344 patients enrolled in the study (mean age 51 ±15 years; 780 [58%] were females; 769 (57%) were of a non-White race). Three distinct long COVID phenotypes were identified: Class 1) fatigue and dyspnea, Class 2) anxiety and depression, and Class 3) fatigue, dyspnea, anxiety, and depression. Class 3 had a significantly lower EQ5D VAS at 3 (50±19) and 6 months (54 ± 22) compared to Classes 1 and 2 (p<0.001). The EQ5D VAS significantly improved between 3 and 6 months for Class 1 (median difference of 6.0 [95% CI, 4.0 to 8.0]) and Class 3 (median difference of 5.0 [95% CI, 0 to 8.5]). There were no differences in DLCO between the classes.

CONCLUSIONS

There were 3 distinct long COVID phenotypes with different outcomes in QoL between 3 and 6 months after symptom onset. These phenotypes suggest that long COVID is a heterogeneous condition with distinct subpopulations who may have different outcomes and warrant tailored therapeutic approaches.

The pathophysiology of postacute sequelae of COVID-19 (PASC): Possible role for persistent inflammation

As the SARS-CoV-2-induced pandemic wanes, a substantial number of patients with acute Corona Virus-induced disease (COVID-19 continue to have symptoms for a prolonged time after initial infection. These patients are said to have postacute sequelae of COVID (PASC) or "long COVID". The underlying pathophysiology of this syndrome is poorly understood and likely quite heterogeneous. The role of persistent, possibly deviant inflammation as a major factor in comorbidity is suspected.

Objective

To review data that address the relative importance of inflammation in the pathophysiology spectrum of PASC and to address how this would impact diagnosis and approach to therapy in patients identified as having such inflammatory abnormalities.

Methods

A review of public databases, including PubMed, MeSH, NLM catalog, and clinical trial databases such as clinicaltrials.gov.

Results

The literature supports a prominent role for various forms and types of inflammation in the pathophysiologic spectrum of PASC. Such inflammation can be persistent ant CoV-2-specific responses, new onset autoimmune responses, or a loss of normal immunoregulation resulting in widespread, sustained inflammatory pathologies that can affect both broad constitutional symptoms (such as fatigue, neurocognitive dysfunction, and anxiety/depression) and organ-specific dysfunction and/or failure.

Conclusions

PASC is a significant clinical entity with similarities to and differences from other postviral syndromes. Significant research efforts are ongoing to better understand specific aberrant inflammatory pathways present in individual patients for the purpose of developing and implementing effective therapies and ultimately prophylaxis strategies to prevent the progression of COVID-19 as well as likely future viral illnesses and pandemics.

Post-Acute COVID-19 Joint Pain and New Onset of Rheumatic Musculoskeletal Diseases: A Systematic Review

As the number of reports of post-acute COVID-19 musculoskeletal manifestations is rapidly rising, it is important to summarize the current available literature in order to shed light on this new and not fully understood phenomenon. Therefore, we conducted a systematic review to provide an updated picture of post-acute COVID-19 musculoskeletal manifestations of potential rheumatological interest, with a particular focus on joint pain, new onset of rheumatic musculoskeletal diseases and presence of autoantibodies related to inflammatory arthritis such as rheumatoid factor and anti-citrullinated protein antibodies. We included 54 original papers in our systematic review. The prevalence of arthralgia was found to range from 2% to 65% within a time frame varying from 4 weeks to 12 months after acute SARS-CoV-2 infection. Inflammatory arthritis was also reported with various clinical phenotypes such as symmetrical polyarthritis with RA-like pattern similar to other prototypical viral arthritis, polymyalgia-like symptoms, or acute monoarthritis and oligoarthritis of large joints resembling reactive arthritis. Moreover, high figures of post-COVID-19 patients fulfilling the classification criteria for fibromyalgia were found, ranging from 31% to 40%. Finally, the available literature about prevalence of rheumatoid factor and anti-citrullinated protein antibodies was largely inconsistent. In conclusion, manifestations of rheumatological interest such as joint pain, new-onset inflammatory arthritis and fibromyalgia are frequently reported after COVID-19, highlighting the potential role of SARS-CoV-2 as a trigger for the development of autoimmune conditions and rheumatic musculoskeletal diseases.

Clustering Analysis Identified Three Long COVID Phenotypes and Their Association with General Health Status and Working Ability

BACKGROUND/AIM

This study aimed to distinguish different phenotypes of long COVID through the post-COVID syndrome (PCS) score based on long-term persistent symptoms following COVID-19 and evaluate whether these symptoms affect general health and work ability. In addition, the study identified predictors for severe long COVID.

METHOD

This cluster analysis included cross-sectional data from three cohorts of patients after COVID-19: non-hospitalized (n = 401), hospitalized (n = 98) and those enrolled at the post-COVID outpatient's clinic (n = 85). All the subjects responded to the survey on persistent long-term symptoms and sociodemographic and clinical factors. K-Means cluster analysis and ordinal logistic regression were used to create PCS scores that were used to distinguish patients' phenotypes.

RESULTS

506 patients with complete data on persistent symptoms were divided into three distinct phenotypes: none/mild (59%), moderate (22%) and severe (19%). The patients with severe phenotype, with the predominating symptoms were fatigue, cognitive impairment and depression, had the most reduced general health status and work ability. Smoking, snuff, body mass index (BMI), diabetes, chronic pain and symptom severity at COVID-19 onset were factors predicting severe phenotype.

CONCLUSION

This study suggested three phenotypes of long COVID, where the most severe was associated with the highest impact on general health status and working ability. This knowledge on long COVID phenotypes could be used by clinicians to support their medical decisions regarding prioritizing and more detailed follow-up of some patient groups.

Long COVID in people living with HIV

PURPOSE OF REVIEW

It is now recognized that SARS-CoV-2 infection can have a long-term impact on health. This review summarizes the current state of knowledge regarding Long COVID in people living with HIV (PLWH).

RECENT FINDINGS

PLWH may be at elevated risk of experiencing Long COVID. Although the mechanisms contributing to Long COVID are incompletely understood, there are several demographic and clinical factors that might make PLWH vulnerable to developing Long COVID.

SUMMARY

PLWH should be aware that new or worsening symptoms following SARS-CoV-2 infection might represent Long COVID. HIV providers should be aware of this clinical entity and be mindful that their patients recovering from SARS-CoV-2 infection may be at higher risk.

Long COVID: Clinical characteristics, proposed pathogenesis and potential therapeutic targets

The emergence of persistent ill-health in the aftermath of SARS-CoV-2 infection has presented significant challenges to patients, healthcare workers and researchers. Termed long COVID, or post-acute sequelae of COVID-19 (PASC), the symptoms of this condition are highly variable and span multiple body systems. The underlying pathophysiology remains poorly understood, with no therapeutic agents proven to be effective. This narrative review describes predominant clinical features and phenotypes of long COVID alongside the data supporting potential pathogenesis of these phenotypes including ongoing immune dysregulation, viral persistence, endotheliopathy, gastrointestinal microbiome disturbance, autoimmunity, and dysautonomia. Finally, we describe current potential therapies under investigation, as well as future potential therapeutic options based on the proposed pathogenesis research.

Long-term impact on cardiopulmonary function and quality of life among patients recovered from SARS-CoV-2 infection in a 6-month follow-up period in Lima, Peru: FUNCTION cohort study protocol

INTRODUCTION

The sequelae of COVID-19 have been described as a multisystemic condition, with a great impact on the cardiovascular and pulmonary systems with abnormalities in pulmonary function tests, such as lower diffusing capacity of the lung for carbon monoxide (DLco) levels and pathological patterns in spirometry; persistence of radiological lesions; cardiac involvement such as myocarditis and pericarditis; and an increase in mental disorders such as anxiety and depression. Several factors, such as infection severity during the acute phase as well as vaccination status, have shown some variable effects on these post-COVID-19 conditions, mainly at a clinical level such as symptoms persistence. Longitudinal assessments and reversibility of changes across the spectrum of disease severity are required to understand the long-term impact of COVID-19.

METHODS AND ANALYSIS

A prospective cohort study aims to assess the impact of SARS-CoV-2 infection on cardiopulmonary function and quality of life after the acute phase of the disease over a 6-month follow-up period. Sample size was calculated to recruit 200 participants with confirmatory COVID-19 tests who will be subsequently classified according to infection severity. Four follow-up visits at baseline, month 1, month 3 and month 6 after discharge from the acute phase of the infection will be scheduled as well as procedures such as spirometry, DLco test, 6-minute walk test, chest CT scan, echocardiogram, ECG, N-terminal pro-B-type natriuretic peptide measurement and RAND-36 scale. Primary outcomes are defined as abnormal pulmonary function test considered as DLco <80%, abnormal cardiovascular function considered as left ventricular ejection fraction <50% and abnormal quality of life considered as a <40 score for each sphere in the RAND-36-Item Short Form Health Survey.

ETHICS AND DISSEMINATION

The study protocol was approved by the Institutional Ethics Committee of the Universidad Peruana Cayetano Heredia (SIDISI 203725) and the Ethics Committee of the Hospital Cayetano Heredia (042-2021). Protocol details were uploaded in ClinicalTrials.gov. Findings will be disseminated through peer-reviewed journals, scientific conferences and open-access social media platforms.

TRIAL REGISTRATION NUMBER

NCT05386485.

Long COVID: a review and proposed visualization of the complexity of long COVID

Post-Acute Sequelae of Severe Acute Respiratory Syndrome Coronavirus - 2 (SARS-CoV-2) infection, or Long COVID, is a prevailing second pandemic with nearly 100 million affected individuals globally and counting. We propose a visual description of the complexity of Long COVID and its pathogenesis that can be used by researchers, clinicians, and public health officials to guide the global effort toward an improved understanding of Long COVID and the eventual mechanism-based provision of care to afflicted patients. The proposed visualization or framework for Long COVID should be an evidence-based, dynamic, modular, and systems-level approach to the condition. Furthermore, with further research such a framework could establish the strength of the relationships between pre-existing conditions (or risk factors), biological mechanisms, and resulting clinical phenotypes and outcomes of Long COVID. Notwithstanding the significant contribution that disparities in access to care and social determinants of health have on outcomes and disease course of long COVID, our model focuses primarily on biological mechanisms. Accordingly, the proposed visualization sets out to guide scientific, clinical, and public health efforts to better understand and abrogate the health burden imposed by long COVID.

Cellular and molecular biomarkers of long COVID: a scoping review

BACKGROUND

Long-COVID (LC) encompasses diverse symptoms lasting months after the initial SARS-CoV-2 infection. Symptoms can be debilitating and affect the quality of life of individuals with LC and their families. Although the symptoms of LC are well described, the aetiology of LC remains unclear, and consequently, patients may be underdiagnosed. Identification of LC specific biomarkers is therefore paramount for the diagnosis and clinical management of the syndrome. This scoping review describes the molecular and cellular biomarkers that have been identified to date with potential use for diagnosis or prediction of LC.

METHODS

This review was conducted using the Joanna Briggs Institute (JBI) Methodology for Scoping Reviews. A search was executed in the MEDLINE and EMBASE databases, as well as in the grey literature for original studies, published until October 5th, 2022, reporting biomarkers identified in participants with LC symptoms (from all ages, ethnicities, and sex), with a previous infection of SARS-CoV-2. Non-English studies, cross-sectional studies, studies without a control group, and pre-prints were excluded. Two reviewers independently evaluated the studies, extracted population data and associated biomarkers.

FINDINGS

23 cohort studies were identified, involving 2163 LC patients [median age 51.8 years, predominantly female sex (61.10%), white (75%), and non-vaccinated (99%)]. A total of 239 candidate biomarkers were identified, consisting mainly of immune cells, immunoglobulins, cytokines, and other plasma proteins. 19 of the 239 candidate biomarkers identified were evaluated by the authors, by means of receiver operating characteristic (ROC) curves.

INTERPRETATION

Diverse cellular and molecular biomarkers for LC have been proposed. Validation of candidate biomarkers in independent samples should be prioritized. Modest reported performance (particularly in larger studies) suggests LC may encompass many distinct aetiologies, which should be explored e.g., by stratifying by symptom clusters and/or sex.

FUNDING

Dr. Tebbutt has received funding from the Canadian Institutes of Health Research (177747) to conduct this work. The funding source was not involved in this scoping review, or in the decision to submit this manuscript for publication.

Predictors of impaired functioning among long COVID patients

BACKGROUND

There is limited information on what acute factors predict more long-term symptoms from COVID-19.

OBJECTIVE

Our objective was to conduct an exploratory factor analysis of self-reported symptoms at two time points of Long COVID-19.

METHODS

Data from patients with Long COVID-19 were collected at the initial two weeks of contracting SARS CoV-2 and the most recent two weeks, with a mean duration of 21.7 weeks between the two-time points. At time point 2, participants also complete the Coronavirus Impact Scale (CIS), measuring how the COVID-19 pandemic affected various dimensions of their lives (e.g., routine, access to medical care, social/family support, etc.).

RESULTS

At time 1, a three-factor model emerged consisting of Cognitive Dysfunction, Autonomic Dysfunction and Gastrointestinal Dysfunction. The analysis of time 2 resulted in a three-factor model consisting of cognitive dysfunction, autonomic dysfunction, and post-exertional malaise. Using factor scores from time 1, the Autonomic Dysfunction and the Gastrointestinal Dysfunction factor scores significantly predicted the CIS summary score at time two. In addition, the same two factor scores at time 1 predicted the occurrence of myalgic encephalomyelitis/chronic fatigue syndrome at time 2.

CONCLUSION

As Cognitive and Autonomic Dysfunction emerged as factors for both time points, suggesting health care workers might want to pay particular attention to these factors that might be related to later symptoms and difficulties with returning to pre-illness family life and work functioning.

Post COVID-19 condition after Wildtype, Delta, and Omicron SARS-CoV-2 infection and prior vaccination: Pooled analysis of two population-based cohorts

BACKGROUND

Post COVID-19 condition (PCC) is an important complication of SARS-CoV-2 infection, affecting millions worldwide. This study aimed to evaluate the prevalence and severity of post COVID-19 condition (PCC) with novel SARS-CoV-2 variants and after prior vaccination.

METHODS

We used pooled data from 1350 SARS-CoV-2-infected individuals from two representative population-based cohorts in Switzerland, diagnosed between Aug 5, 2020, and Feb 25, 2022. We descriptively analysed the prevalence and severity of PCC, defined as the presence and frequency of PCC-related symptoms six months after infection, among vaccinated and non-vaccinated individuals infected with Wildtype, Delta, and Omicron SARS-CoV-2. We used multivariable logistic regression models to assess the association and estimate the risk reduction of PCC after infection with newer variants and prior vaccination. We further assessed associations with the severity of PCC using multinomial logistic regression. To identify groups of individuals with similar symptom patterns and evaluate differences in the presentation of PCC across variants, we performed exploratory hierarchical cluster analyses.

RESULTS

We found strong evidence that vaccinated individuals infected with Omicron had reduced odds of developing PCC compared to non-vaccinated Wildtype-infected individuals (odds ratio 0.42, 95% confidence interval 0.24-0.68). The odds among non-vaccinated individuals were similar after infection with Delta or Omicron compared to Wildtype SARS-CoV-2. We found no differences in PCC prevalence with respect to the number of received vaccine doses or timing of last vaccination. The prevalence of PCC-related symptoms among vaccinated, Omicron-infected individuals was lower across severity levels. In cluster analyses, we identified four clusters of diverse systemic, neurocognitive, cardiorespiratory, and musculoskeletal symptoms, with similar patterns across variants.

CONCLUSION

The risk of PCC appears to be lowered with infection by the Omicron variant and after prior vaccination. This evidence is crucial to guide future public health measures and vaccination strategies.

A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms

BACKGROUND

Accurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflammatory profiles.

METHODS

1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative symptom scores (FLU-PRO Plus) were included in this analysis. We employed machine-learning approaches to identify symptom clusters and compared risk of hospitalization, long-term symptoms, as well as peak CRP and IL-6 concentrations.

RESULTS

We identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 ("Nasal cluster") is highly correlated with reporting runny/stuffy nose and sneezing, cluster 2 ("Sensory cluster") is highly correlated with loss of smell or taste, and cluster 3 ("Respiratory/Systemic cluster") is highly correlated with the respiratory (cough, trouble breathing, among others) and systemic (body aches, chills, among others) domain symptoms. Participants in the Respiratory/Systemic cluster were twice as likely as those in the Nasal cluster to have been hospitalized, and 1.5 times as likely to report that they had not returned-to-activities, which remained significant after controlling for confounding covariates (P < 0.01). Respiratory/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset (P = 0.03). We observed higher peak CRP and IL-6 in the Respiratory/Systemic cluster (P < 0.01).

CONCLUSIONS

We identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.

Clinical and temporal characterization of COVID-19 subgroups using patient vector embeddings of electronic health records

OBJECTIVE

To identify and characterize clinical subgroups of hospitalized Coronavirus Disease 2019 (COVID-19) patients.

MATERIALS AND METHODS

Electronic health records of hospitalized COVID-19 patients at NewYork-Presbyterian/Columbia University Irving Medical Center were temporally sequenced and transformed into patient vector representations using Paragraph Vector models. K-means clustering was performed to identify subgroups.

RESULTS

A diverse cohort of 11 313 patients with COVID-19 and hospitalizations between March 2, 2020 and December 1, 2021 were identified; median [IQR] age: 61.2 [40.3-74.3]; 51.5% female. Twenty subgroups of hospitalized COVID-19 patients, labeled by increasing severity, were characterized by their demographics, conditions, outcomes, and severity (mild-moderate/severe/critical). Subgroup temporal patterns were characterized by the durations in each subgroup, transitions between subgroups, and the complete paths throughout the course of hospitalization.

DISCUSSION

Several subgroups had mild-moderate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections but were hospitalized for underlying conditions (pregnancy, cardiovascular disease [CVD], etc.). Subgroup 7 included solid organ transplant recipients who mostly developed mild-moderate or severe disease. Subgroup 9 had a history of type-2 diabetes, kidney and CVD, and suffered the highest rates of heart failure (45.2%) and end-stage renal disease (80.6%). Subgroup 13 was the oldest (median: 82.7 years) and had mixed severity but high mortality (33.3%). Subgroup 17 had critical disease and the highest mortality (64.6%), with age (median: 68.1 years) being the only notable risk factor. Subgroups 18-20 had critical disease with high complication rates and long hospitalizations (median: 40+ days). All subgroups are detailed in the full text. A chord diagram depicts the most common transitions, and paths with the highest prevalence, longest hospitalizations, lowest and highest mortalities are presented. Understanding these subgroups and their pathways may aid clinicians in their decisions for better management and earlier intervention for patients.

Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes

The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes

BACKGROUND

Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.

METHODS

We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.

FINDINGS

We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.

INTERPRETATION

Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

FUNDING

NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Long COVID Classification: Findings from a Clustering Analysis in the Predi-COVID Cohort Study

The increasing number of people living with Long COVID requires the development of more personalized care; currently, limited treatment options and rehabilitation programs adapted to the variety of Long COVID presentations are available. Our objective was to design an easy-to-use Long COVID classification to help stratify people with Long COVID. Individual characteristics and a detailed set of 62 self-reported persisting symptoms together with quality of life indexes 12 months after initial COVID-19 infection were collected in a cohort of SARS-CoV-2 infected people in Luxembourg. A hierarchical ascendant classification (HAC) was used to identify clusters of people. We identified three patterns of Long COVID symptoms with a gradient in disease severity. Cluster-Mild encompassed almost 50% of the study population and was composed of participants with less severe initial infection, fewer comorbidities, and fewer persisting symptoms (mean = 2.9). Cluster-Moderate was characterized by a mean of 11 persisting symptoms and poor sleep and respiratory quality of life. Compared to the other clusters, Cluster-Severe was characterized by a higher proportion of women and smokers with a higher number of Long COVID symptoms, in particular vascular, urinary, and skin symptoms. Our study evidenced that Long COVID can be stratified into three subcategories in terms of severity. If replicated in other populations, this simple classification will help clinicians improve the care of people with Long COVID.

Association of COVID-19 mortality with serum selenium, zinc and copper: Six observational studies across Europe

Introduction

Certain trace elements are essential for life and affect immune system function, and their intake varies by region and population. Alterations in serum Se, Zn and Cu have been associated with COVID-19 mortality risk. We tested the hypothesis that a disease-specific decline occurs and correlates with mortality risk in different countries in Europe.

Methods

Serum samples from 551 COVID-19 patients (including 87 non-survivors) who had participated in observational studies in Europe (Belgium, France, Germany, Ireland, Italy, and Poland) were analyzed for trace elements by total reflection X-ray fluorescence. A subset (n=2069) of the European EPIC study served as reference. Analyses were performed blinded to clinical data in one analytical laboratory.

Results

Median levels of Se and Zn were lower than in EPIC, except for Zn in Italy. Non-survivors consistently had lower Se and Zn concentrations than survivors and displayed an elevated Cu/Zn ratio. Restricted cubic spline regression models revealed an inverse nonlinear association between Se or Zn and death, and a positive association between Cu/Zn ratio and death. With respect to patient age and sex, Se showed the highest predictive value for death (AUC=0.816), compared with Zn (0.782) or Cu (0.769).

Discussion

The data support the potential relevance of a decrease in serum Se and Zn for survival in COVID-19 across Europe. The observational study design cannot account for residual confounding and reverse causation, but supports the need for intervention trials in COVID-19 patients with severe Se and Zn deficiency to test the potential benefit of correcting their deficits for survival and convalescence.

Longitudinal Cluster Analysis of Hemodialysis Patients with COVID-19 in the Pre-Vaccination Era

Coronavirus disease 2019 (COVID-19) in hemodialysis patients (HD) is characterized by heterogeneity of clinical presentation and outcomes. To stratify patients, we collected clinical and laboratory data in two cohorts of HD patients at COVID-19 diagnosis and during the following 4 weeks. Baseline and longitudinal values were used to build a linear mixed effect model (LME) and define different clusters. The development of the LME model in the derivation cohort of 17 HD patients (66.7 ± 12.3 years, eight males) allowed the characterization of two clusters (cl1 and cl2). Patients in cl1 presented a prevalence of females, higher lymphocyte count, and lower levels of lactate dehydrogenase, C-reactive protein, and CD8 + T memory stem cells as a possible result of a milder inflammation. Then, this model was tested in an independent validation cohort of 30 HD patients (73.3 ± 16.3 years, 16 males) assigned to cl1 or cl2 (16 and 14 patients, respectively). The cluster comparison confirmed that cl1 presented a milder form of COVID-19 associated with reduced disease activity, hospitalization, mortality rate, and oxygen requirement. Clustering analysis on longitudinal data allowed patient stratification and identification of the patients at high risk of complications. This strategy could be suitable in different clinical settings.

Effect of COVID-19 vaccines for the treatment of people with post-COVID-19 condition: a rapid review

Background: Vaccination for coronavirus disease 2019 (COVID-19) has demonstrated reduced risk of hospitalisation and death against more recent variants of COVID-19. Some studies suggested improvements in patients with post-COVID-19 condition (PCC) following vaccination. We systematically explored available evidence on the effect of COVID-19 vaccines for the treatment of people with PCC. Methods: We conducted a rapid review of the literature following systematic approaches. We searched Medline (OVID), EMBASE (Elsevier), ClinicalTrials.gov, and the International Clinical Trials Registry Platform (ICTRP) for randomised trials, non-randomised trials, controlled before-after studies, and interrupted time-series studies of the effect of COVID-19 vaccines for treating people with PCC. Two independent review authors screened citations. Two review authors  extracted data independently. We had planned to assess the risk of bias and use the GRADE approach (Grading of Recommendations, Assessment, Development, and Evaluation) to assess the certainty of evidence if there were completed studies. Results: We identified two ongoing randomised controlled trials. Both trials examine the effectiveness of therapeutic vaccines on PCC. The anticipated completion date of the CIMAvax-EGFA trial is January 2023, and the completion date of the COVID-19 mRNA vaccine trial is not stated. Conclusions: There is currently an absence of high‐quality evidence evaluating the effectiveness of COVID-19 vaccines for treating people with post-COVID-19 condition. The absence of published studies and only two ongoing trials highlight the need for additional studies on the effectiveness of vaccines for PCC. We recommend that researchers consider PCC as per the definition provided by the World Health Organization and use the available core outcome set for PCC in deciding which outcomes to measure and report in the trials. PROPSERO registration: CRD42022330821 (20/06/2022)

Clusters of Pregnant Women with Severe Acute Respiratory Syndrome Due to COVID-19: An Unsupervised Learning Approach

COVID-19 has been widely explored in relation to its symptoms, outcomes, and risk profiles for the severe form of the disease. Our aim was to identify clusters of pregnant and postpartum women with severe acute respiratory syndrome (SARS) due to COVID-19 by analyzing data available in the Influenza Epidemiological Surveillance Information System of Brazil (SIVEP-Gripe) between March 2020 and August 2021. The study's population comprised 16,409 women aged between 10 and 49 years old. Multiple correspondence analyses were performed to summarize information from 28 variables related to symptoms, comorbidities, and hospital characteristics into a set of continuous principal components (PCs). The population was segmented into three clusters based on an agglomerative hierarchical cluster analysis applied to the first 10 PCs. Cluster 1 had a higher frequency of younger women without comorbidities and with flu-like symptoms; cluster 2 was represented by women who reported mainly ageusia and anosmia; cluster 3 grouped older women with the highest frequencies of comorbidities and poor outcomes. The defined clusters revealed different levels of disease severity, which can contribute to the initial risk assessment of the patient, assisting the referral of these women to health services with an appropriate level of complexity.

Predictors of chronic COVID-19 symptoms in a community-based cohort of adults

Background

COVID-19 can cause some individuals to experience chronic symptoms. Rates and predictors of chronic COVID-19 symptoms are not fully elucidated.

Objective

To examine occurrence and patterns of post-acute sequelae of SARS-CoV2 infection (PASC) symptomatology and their relationship with demographics, acute COVID-19 symptoms and anti-SARS-CoV-2 IgG antibody responses.

Methods

A multi-stage observational study was performed of adults (≥18 years) from 5 US states. Participants completed two rounds of electronic surveys (May-July 2020; April-May 2021) and underwent testing to anti-SARS-CoV-2 nucleocapsid protein IgG antibody testing. Latent Class Analysis was used to identify clusters of chronic COVID-19 symptoms.

Results

Overall, 390 adults (median [25%ile, 75%ile] age: 42 [31, 54] years) with positive SARS-CoV-2 antibodies completed the follow-up survey; 92 (24.7%) had ≥1 chronic COVID-19 symptom, with 11-month median duration of persistent symptoms (range: 1–12 months). The most common chronic COVID-19 symptoms were fatigue (11.3%), change in smell (9.5%) or taste (5.6%), muscle or joint aches (5.4%) and weakness (4.6%). There were significantly higher proportions of ≥1 persistent COVID-19 symptom (31.5% vs. 18.6%; Chi-square, P = 0.004), and particularly fatigue (15.8% vs. 7.3%, P = 0.008) and headaches (5.4% vs. 1.0%, P = 0.011) in females compared to males. Chronic COVID-19 symptoms were also increased in individuals with ≥6 acute COVID-19 symptoms, Latent class analysis revealed 4 classes of symptoms. Latent class-1 (change of smell and taste) was associated with lower anti-SARS-CoV-2 antibody levels; class-2 and 3 (multiple chronic symptoms) were associated with higher anti-SARS-CoV-2 antibody levels and more severe acute COVID-19 infection.

Limitations

Ambulatory cohort with less severe acute disease.

Conclusion

Individuals with SARS-CoV-2 infection commonly experience chronic symptoms, most commonly fatigue, changes in smell or taste and muscle/joint aches. Female sex, severity of acute COVID-19 infection, and higher anti-SARS-CoV-2 IgG levels were associated with the highest risk of having chronic COVID-19 symptoms.