The association between the number of symptoms and the severity of Post-COVID-Fatigue after SARS-CoV-2 infection treated in an outpatient setting

Post infectious fatigue and circadian rhythm disruption in long-COVID and other infections: a need for further research

Chronic fatigue syndrome (CFS) remains a subject of scientific research specifically with regards to its association with infections, including the more recently described Long COVID condition. Chronic fatigue and sleep disturbances in Long COVID are intricately linked to disruptions in circadian rhythms, driven by distinct molecular and cellular mechanisms triggered by SARS-CoV-2 infection. This can be driven by various mechanisms including dysregulation of key clock genes (CLOCK, BMAL1, PER2), mitochondrial dysfunction impairing oxidative phosphorylation, and cytokine-induced neuroinflammation (e.g., interleukin-6, tumor necrosis factor-alpha). Epigenetic changes, including DNA methylation at clock-related loci, particularly in peripheral tissues, further contribute to systemic circadian dysregulation. This work underscores the multifaceted molecular and systemic disruptions to circadian regulation in relation to fatigue and sleep disturbances identified as post-infectious sequelae, focusing on the Long COVID condition.

Self-Reported Post-COVID Symptoms at 18 Months After Infection Among Adults in Southern Brazil: A Cross-Sectional Study

BACKGROUND/OBJECTIVES

Currently, there is a limited understanding of the long-term consequences following acute COVID-19, referred to as long COVID. This cross-sectional study aims to analyze the prevalence of persistent signs and symptoms of long COVID, 18 months after primary SARS-CoV-2 infection in adults in southern Brazil.

METHODS

Using two national databases (the digital registry of SARS-CoV-2 positive cases), 370 individuals living in the state of Paraná (Brazil) were recruited. Data were collected through telephone interviews conducted in 2021 and 2022.

RESULTS

The overall prevalence of long COVID was 66.2% among study participants. During the acute phase of infection, the most common symptom clusters included neurological symptoms (87.0%; n = 318), followed by respiratory (82.0%; n = 301), musculoskeletal (66.0%; n = 241), digestive (50.0%; n = 184), psychological (38.0%; n = 138), and endocrine symptoms (28.0%; n = 104). In the 18 month follow-up, the main persistent symptoms were memory loss (42.7%), fatigue (32.2%), anxiety (23.5%), dyspnea (19.7%), and hair loss (19.7%). The proportion of participants with long COVID was statistically higher in females (73.9%), those with a family income below two minimum wages (94.7%), those who do not practice physical activity (83.3%), those who report poor sleep quality (93.3%), those who use long-term medication (85.9%), those who needed health care in the previous six months (87.3%), those who required professional and/or family care (79.3%), those who were in the ICU (79.0%), and those who used ventilatory support (77.5%).

CONCLUSIONS

Long COVID is a complex condition that requires long-term monitoring and investment in health services due to its high prevalence and the health consequences in the population.

Predicting higher risk factors for COVID-19 short-term reinfection in patients with rheumatic diseases: a modeling study based on XGBoost algorithm

BACKGROUND

Corona virus disease 2019 (COVID-19) reinfection, particularly short-term reinfection, poses challenges to the management of rheumatic diseases and may increase adverse clinical outcomes. This study aims to develop machine learning models to predict and identify the risk of short-term COVID-19 reinfection in patients with rheumatic diseases.

METHODS

We developed four prediction models using explainable machine learning to assess the risk of short-term COVID-19 reinfection in 543 patients with rheumatic diseases. Psychological health was evaluated using the Functional Assessment of Chronic Illness Therapy Fatigue (FACIT-F) scale, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder 7-item (GAD-7) questionnaire, and the Pittsburgh Sleep Quality Index (PSQI) scale. Health status and disease activity were assessed using the EuroQol-5 Dimension-3 Level (EQ-5D-3L) descriptive system and the Visual Analogue Score (VAS) scale. The model performance was assessed by Area Under the Receiver Operating Characteristic Curve (AUC), Area Under the Precision-Recall Curve (AUPRC), and the geometric mean of sensitivity and specificity (G-mean). SHapley Additive exPlanations (SHAP) analysis was used to interpret the contribution of each predictor to the model outcomes.

RESULTS

The eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance with an AUC of 0.91 (95% CI 0.87-0.95). Significant factors of short-term reinfection included glucocorticoid taper (OR = 2.61, 95% CI 1.38-4.92), conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) taper (OR = 2.97, 95% CI 1.90-4.64), the number of symptoms (OR = 1.24, 95% CI 1.08-1.42), and GAD-7 scores (OR = 1.07, 95% CI 1.02-1.13). FACIT-F scores were associated with a lower likelihood of short-term reinfection (OR = 0.95, 95% CI 0.93-0.96). Besides, we found that the GAD-7 score was one of the most important predictors.

CONCLUSION

We developed explainable machine learning models to predict the risk of short-term COVID-19 reinfection in patients with rheumatic diseases. SHAP analysis highlighted the importance of clinical and psychological factors. Factors included anxiety, fatigue, depression, poor sleep quality, high disease activity during initial infection, and the use of glucocorticoid taper were significant predictors. These findings underscore the need for targeted preventive measures in this patient population.

[Fatigue in the general population: results of the "German Health Update 2023" study]

BACKGROUND

Fatigue is an unspecific symptom complex characterized by tiredness, lack of energy, and lack of concentration and is of considerable public health relevance, due to its links with incapacity for work, risk of accidents, and increased need for healthcare.

METHODS

The analyses are based on data from 9766 adults of the telephone survey "Gesundheit in Deutschland aktuell (GEDA)" 2023. Fatigue was recorded using the Fatigue Assessment Scale (FAS), a validated instrument with 10 questions for self-assessment of fatigue. The scale was dichotomized into yes (at least mild to moderate fatigue) versus no (no fatigue). Population-weighted prevalences of fatigue and associated sociodemographic and health-related factors were calculated in descriptive analyses and multivariable Poisson regression.

RESULTS

The overall prevalence of fatigue in adults in Germany is 29.7% (95% CI 28.1-31.2), is highest in 18- to 29-year-olds (39.6% (95% CI 35.0-44.4)), and decreases in the age groups up to 65-79 years (20.6% (95% CI 18.2-23.3)). It is higher again in the very old age group (33.2% (95% CI 28.9-37.7)). Women have a higher risk of fatigue than men (aRR 1.19 (95% CI 1.08-1.32)). Fatigue is significantly associated with age, lower education, chronic illness, depression, and long COVID, regardless of covariates.

DISCUSSION

GEDA 2023 is one of the few population-based studies to have collected data on fatigue. The results allow estimates to be made for Germany on the frequency of fatigue and the significance of physical, psychological, and social influencing factors. They can be used as a reference or as a basis for trends over time as part of continuous health monitoring in Germany.

Symptom Clusters in Acute SARS-CoV-2 Infection and Long COVID Fatigue in Male and Female Outpatients

(1) Background: After an acute SARS-CoV-2 infection, patients are at risk of developing Long COVID, with fatigue as a frequent and serious health problem. Objectives: To identify symptom clusters in acute SARS-CoV-2 infections and investigate their associations with the development of Long COVID fatigue, and to examine sex-specific differences. (2) Methods: The analysis included a total of 450 COVID-19 outpatients, of whom 54.4% were female. The median ages of the men and women were 51 years (IQR 36.0; 60.0) and 48 years (IQR 33.0; 57.0), respectively. Data collection took place between November 2020 and May 2021, with a median time between acute SARS-CoV-2 infection and examination in the study center of 240 days (IQR 133; 326). The Fatigue Assessment Scale (FAS) was used to identify fatigue and its severity. A multiple correspondence analysis was used to group forty-two COVID-19 symptoms into seven symptom clusters. Logistic and log-linear regressions were used to investigate associations between acute symptom clusters and Long COVID fatigue as dichotomous and continuous outcome, respectively. (3) Results: Fatigue occurred more frequently in women than in men (45% vs. 25%) and the median FAS score, indicating severity of fatigue, was higher in women than in men. The comparison between men and women revealed notable differences in four out of seven clusters. The strongest associations between symptom clusters in infection and Long COVID fatigue were identified for the cluster "cognitive and mental symptoms". In the log-linear regression model, each additional symptom in this cluster was associated with an increase of the FAS score by 5.13% (95% CI: [0.04; 0.07]; p < 0.001). The results of the logistic regression models supported this finding. Each additional symptom in this symptom cluster increased the odds of fatigue by 42% (95% CI: [1.23; 1.66]; p < 0.001). (4) Conclusions: In our study in COVID-19 outpatients, a strong association was observed between the number of symptoms in the cluster "cognitive and mental symptoms" during acute SARS-CoV-2 infection and the risk of developing fatigue months later. The consequent use of preventive and therapeutic strategies is necessary to decrease the burden of fatigue in the context of Long COVID.

Can inflammatory plasma proteins predict Long COVID or Fatigue severity after SARS-CoV-2 infection?

OBJECTIVE

To investigate whether specific immune response plasma proteins can predict an elevated risk of developing Long COVID symptoms or fatigue severity after SARS-CoV-2 infection.

METHODS

This study was based on 257 outpatients with test-confirmed SARS-CoV-2 infection between February 2020 and January 2021. At least 12 weeks after the acute infection, 92 plasma proteins were measured using the Olink Target 96 immune response panel (median time between acute infection and venous blood sampling was 38.8 [IQR: 24.0-48.0] weeks). The presence of Long COVID symptoms and fatigue severity was assessed 115.8 [92.5-118.6] weeks after the acute infection by a follow-up postal survey. Long COVID (yes/no) was defined as having one or more of the following symptoms: fatigue, shortness of breath, concentration or memory problems. The severity of fatigue was assessed using the Fatigue Assessment Scale (FAS). In multivariable-adjusted logistic and linear regression models the associations between each plasma protein (exposure) and Long COVID (yes/no) or severity of fatigue were investigated.

RESULTS

Nine plasma proteins were significantly associated with Long COVID before, but not after adjusting for multiple testing (FDR-adjustment): DFFA, TRIM5, TRIM21, HEXIM1, SRPK2, PRDX5, PIK3AP1, IFNLR1 and HCLS1. Moreover, a total of 10 proteins were significantly associated with severity of fatigue before FDR-adjustment: SRPK2, ITGA6, CLEC4G, HEXIM1, PPP1R9B, PLXNA4, PRDX5, DAPP1, STC1 and HCLS1. Only SRPK2 and ITGA6 remained significantly associated after FDR-adjustment.

CONCLUSIONS

This study demonstrates that certain immune response plasma proteins might play an important role in the pathophysiology of Long COVID and severity of fatigue after SARS-CoV-2 infection.

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.