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Full Title: Persistent fatigue following SARS-CoV-2 infection is common and
independent of severity of initial infection
Short title: Fatigue following SARS-CoV-2 infection
Liam Townsend1,2*¶, Adam H. Dyer3,4¶, Karen Jones3, Jean Dunne3, Rachel Kiersey3,
Fiona Gaffney3, Laura O’Connor3, Aoife Mooney3, Deirdre Leavy3, Katie Ridge3,
Catherine King3, Fionnuala Cox3, Kate O’Brien5, Joanne Dowds5, Jamie A Sugrue6,
David Hopkins7, Patricia Byrne8, Tara Kingston8, Cliona Ni Cheallaigh1,2, Parthiban
Nadarajan9, Anne Marie McLaughlin9, Nollaig M Bourke4, Colm Bergin1,2, Cliona
O’Farrelly6,10, Ciaran Bannan1,2&, Niall Conlon3,11&
¶ These authors contributed equally to this work
& These authors contributed equally to this work
1. Department of Infectious Diseases, St James’s Hospital, Dublin, Ireland
2. Department of Clinical Medicine, School of Medicine, Trinity Translational
Medicine Institute, Trinity College Dublin, Ireland
3. Department of Immunology, St James’s Hospital, Dublin, Ireland
4. Department of Medical Gerontology, School of Medicine, Trinity Translational
Medicine Institute, Trinity College Dublin, Ireland
5. Department of Physiotherapy, St James’s Hospital, Dublin, Ireland
6. School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute,
Trinity College Dublin, Ireland
7. School of Medicine, Trinity College Dublin, Ireland
8. Department of Psychological Medicine, St James’s Hospital, Dublin, Ireland
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9. Department of Respiratory Medicine, St James’s Hospital, Dublin, Ireland
10. Department of Comparative Immunology, School of Medicine, Trinity College
Dublin, Ireland
11. Department of Immunology, School of Medicine, Trinity College Dublin, Ireland
Corresponding Author:
townsenl@tcd.ie (LT)
ORCID-ID 0000-0002-7089-0665
Contributions
LT contributed to conceptualization, data curation, funding acquisition, methodology,
formal analysis, investigation and writing of the manuscript. AHD contributed to data
curation, formal analysis, and writing of the manuscript. KJ, JD, RK, FG, LOC, AM and
DL contributed to investigation and validation. KR, CK and FC contributed to
validation. KOB, JD, JS, DH and PN contributed to investigation. PB, TK and AMcL
contributed to conceptualisation. CNiC, CBe, COF and NMB contributed to
conceptualisation, supervision and writing. NC and CBa contributed to
conceptualisation, methodology, supervision and writing.
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Abstract
Fatigue is a common symptom in those presenting with symptomatic COVID-19
infection. However, it is unknown if COVID-19 results in persistent fatigue in those
recovered from acute infection. We examined the prevalence of fatigue in individuals
recovered from the acute phase of COVID-19 illness using the Chalder Fatigue Score
(CFQ-11). We further examined potential predictors of fatigue following COVID-19
infection, evaluating indicators of COVID-19 severity, markers of peripheral immune
activation and circulating pro-inflammatory cytokines. Of 128 participants (49.5 15
years; 54% female), more than half reported persistent fatigue (52.3%; 45/128) at 10
weeks (median) after initial COVID-19 symptoms. There was no association between
COVID-19 severity (need for inpatient admission, supplemental oxygen or critical
care) and fatigue following COVID-19. Additionally, there was no association between
routine laboratory markers of inflammation and cell turnover (leukocyte, neutrophil or
lymphocyte counts, neutrophil-to-lymphocyte ratio, lactate dehydrogenase, C-reactive
protein) or pro-inflammatory molecules (IL-6 or sCD25) and fatigue post COVID-19.
Female gender and those with a pre-existing diagnosis of depression/anxiety were
over-represented in those with fatigue. Our findings demonstrate a significant burden
of post-viral fatigue in individuals with previous SARS-CoV-2 infection after the acute
phase of COVID-19 illness. This study highlights the importance of assessing those
recovering from COVID-19 for symptoms of severe fatigue, irrespective of severity of
initial illness, and may identify a group worthy of further study and early intervention.
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Introduction
Fatigue is recognised as one of the most common presenting complaints in individuals
infected with SARS-CoV-2, the cause of the current COVID-19 pandemic. In early
reports on the clinical characteristics of those infected, fatigue was listed as a
presenting complaint in 44 - 69.6% (1-3). Further studies were followed by meta-
analyses, with 34 - 46% of those infected presenting with fatigue (4-7). Whilst the
presenting features of SARS-CoV-2 infection have been well-characterised, the
medium and long-term consequences of SARS-CoV-2 infection remain unexplored. In
particular, concern has been raised that SARS-CoV-2 has the potential to trigger a
post-viral fatigue syndrome (8, 9).
Patients acutely infected with SARS-CoV-2 demonstrate decreased lymphocyte
counts, higher leukocyte counts with an elevated neutrophil-to-lymphocyte ratio (NLR)
in addition to decreased percentages of monocytes, eosinophils and basophils. It has
also been reported that both helper and suppressor T cells are decreased in those
with SARS-CoV-2 (10). In severe cases, elevated C-reactive protein (CRP), ferritin, d-
dimers in addition to pro-inflammatory factors such as IL-6 and soluble CD25 (sC25),
and an increase in intermediate (CD16+ CD14+) monocytes have been reported (11,
12). Whether or not the immunological alterations seen in SARS-CoV-2 have any
relationship to the potential development of medium and long-term symptoms
following infection is an area which has not been researched to date. The persistence
of these changes following resolution of initial infection have also not been examined.
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In one of the few reports to assess the long-term consequences of the original severe
acute respiratory syndrome (SARS) epidemic (also caused by a coronavirus), a subset
of patients in Toronto experienced persistent fatigue, diffuse myalgia, weakness and
depression one year after their acute illness and could not return to work (13). In a
similar follow-up study amongst 233 SARS survivors in Hong Kong, over 40% of
respondents reported a chronic fatigue problem 40 months after infection (14). In those
affected by the subsequent Middle-Eastern Respiratory Syndrome Coronavirus
(MERS-CoV) outbreak, prolonged symptoms and fatigue were reported up to 18
months after acute infection (15). Similarly, prominent post-viral fatigue syndromes
have been reported following Epstein-Barr Virus (EBV), Q-Fever and Ross River Virus
(RRV) infections (16-19). Whether or not infection with the novel SARS-CoV-2
coronavirus has the potential to result in post-viral fatigue, both in the medium and
long-term, is currently unknown.
Persistent fatigue lasting 6 months or longer without an alternate explanation is termed
chronic fatigue syndrome (CFS). This may be observed after several viral and bacterial
infections (9). There have also been links between CFS and depression, although it
remains unclear whether one diagnosis precedes the onset of the other (20-22). Whilst
infections are thought to precipitate CFS, the pathophysiology remains controversial.
Studies of post-viral fatigue and CFS often focus on immune system alterations, but
robust data to indicate causation or association is absent. There are a plethora of
studies examining immune dysregulation and activation in CFS; however, none of
these have provided a consistent finding or biologically plausible answer; rather, there
are contrasting findings across studies concerning both immune population changes
and cytokine levels (23-25). The heterogenous findings in immune populations in CFS
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include changes in both lymphoid and myeloid populations (26-29). The disparate
findings of prior CFS studies may be due to the variety of aetiologies that ultimately
lead to CFS. Whether alterations in immune system activity has any relationship to the
potential post-viral fatigue experienced with the novel SARS-CoV-2 is an important
question for future research. Prospectively examining patients following SARS-CoV-2
infection provides a well characterised population with identical index infection,
allowing for more accurate descriptors of both disease state and disease
characteristics.
We sought to establish whether patients recovering from SARS-CoV-2 infection
remained fatigued after their physical recovery, and to investigate whether there was
a relationship between severe fatigue and a variety of clinicopathological parameters.
We also sought to examine persistence of markers of disease beyond clinical
resolution of infection.
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Materials and Methods
Study Setting and Participants
The current study was carried out in the post-COVID-19 review clinic at St James’s
Hospital (SJH), Dublin, Ireland. Participants were recruited from the post-COVID-19
outpatient clinic, which offers an outpatient appointment to all individuals with a
positive SARS-CoV-2 nasopharyngeal swab PCR at our institution. Patients attending
the outpatient clinic were invited to participate in the current study by a research
physician. In order to be considered for inclusion in the current study, participation had
to occur at least 6 weeks after either: (i) date of last acute COVID-19 symptoms (for
outpatients) and (ii) date of discharge for those who were admitted during their acute
COVID-19 illness.
Fatigue Assessment
Fatigue was assessed using the validated Chalder Fatigue Scale (CFQ-11) (30, 31).
Briefly, participants are asked to answer these questions with particular reference to
the past month in comparison to their pre-COVID-19 baseline, with responses
measured on a Likert scale (0-3). From this a global score can be constructed out of
a total of 33, as well as scores for the sub-scales of physical and psychological fatigue
(32).
Further, the CFQ also allows the differentiation of “cases” vs “non-cases” where scores
0 and 1 (“Better than usual”/”No worse than usual”) are scored a zero and scores 2
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and 3 (“Worse than usual”/”Much worse than usual”) are scored a 1 (bimodal scoring).
The sum of all 11 binary scores is calculated and those with a total score of four or
greater considered to meet the criteria for fatigue. This latter method for “caseness” is
validated and closely resembles other fatigue questionnaires (32-35).
For the current study, we computed: (i) case-status (fatigue vs. non-fatigued) using
the bimodal scoring method and the (ii) total CFQ-11 score (from a maximum of 33).
Blood Sampling & Analysis of Circulating Pro-Inflammatory Cytokines
Blood sampling was incorporated as part of routine phlebotomy occurring on the same
day as study participation/fatigue assessment. This involved measurement of routine
laboratory parameters, including white cell counts (leukocyte, neutrophil and
lymphocyte counts), CRP and lactate dehydrogenase (LDH). IL-6 and sCD25 levels
were measured in serum by ELISA (R&D systems).
Clinical Covariate Assessment
Routine demographic information was collected from participants. Further information
was obtained from patient records and included: dates of COVID-19 symptoms,
inpatient admission, treatment with supplemental oxygen and admission to the critical
care/Intensive Care Unit (ICU). Background medical history was assessed by
obtaining a list of regular medications and a list of medical comorbidities. A history of
depression/anxiety was recorded as a physician-diagnosed history of
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depression/anxiety or regular use of antidepressant medication. Additionally,
participants were assessed for frailty which was operationalised using Rockwood’s
Clinical Frailty Scale (range 0-7) (36). In order to assess subjective recovery from
COVID-19 illness, participants were also asked a binary question regarding their
perception of having returned to full health.
Ethical Approval
Informed consent was obtained from all participants in the current study in accordance
with the Declaration of Helsinki (37). Ethical approval for the current study was
obtained from the Tallaght University Hospital (TUH)/St James’s Hospital (SJH) Joint
Research Ethics Committee (reference REC 2020-04 (01)).
Statistics
All statistical analysis was carried out using STATA v15.0 (Texas, USA) and statistical
significance considered p<0.05. Descriptive statistics are reported as means with
standard deviations (SD) and interquartile ranges (IQR) as appropriate.
We analysed between-group differences in those with severe fatigue in comparison to
those without severe fatigue (catergorised as non-fatigued as per the case definition
of the CFQ-11 above) using t-tests, chi-square tests and wilcoxon rank-sum tests as
appropriate (data were examined for normality using Q-q plots and histograms).
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Logistic regression was used to analyse predictors of severe fatigue. We tested the
association of severe fatigue with time interval between assessment and COVID-19
diagnosis, as well as several important indicators of COVID-19 severity (days since
symptom onset, need for inpatient admission, supplemental oxygen treatment
admission to critical care). These associations were tested individually using both an
unadjusted model (model 1) and a model adjusted for age and sex (model 2).
Subsequently, we analysed the associations between individual laboratory
parameters/serum cytokines and severe fatigue using the same models. Results are
presented as Odds Ratios (OR)/adjusted Odds Ratios (aOR) with corresponding 95%
Confidence Intervals (CIs) and p-values.
Using the same independent variables and model adjustment, we examined the
association between the above predictor variables and total CFQ-11 score in order to
assess relationships not seen using the binary case definition. Linear regression was
used testing each predictor individually (model 1) and again, adjusting for age and sex
(model 2). Further exploratory analysis involved adding interaction terms with both age
and gender, to examine for any potential gender or age-specific effects.
Results
Participant Characteristics
223 patients were offered an outpatient appointment, of which 128 (57%) attended for
assessment. These were consecutively enrolled (mean age: 49.5 15 years; 52.3%
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female) (See Table 1). Just over half (71/128; 55.5%) were admitted to hospital for
treatment of COVID-19, with the remainder managed as outpatients (57/128; 44.5%).
Just over half (66/128; 51.6%) were healthcare workers. This is reflective of the overall
demographics of Irish data and our institution, where 50% of positive SARS-CoV-2
cases involved healthcare workers (38). Baseline characteristics are detailed in Table
1.
The median interval between study assessment and discharge from hospital or a
timepoint 14 days following diagnosis if managed as an outpatient was 72 days
(IQR: 62-87). Two-fifths (54/128; 42.9%) reported feeling back to their full health,
whilst the majority did not. Prior to COVID-19 illness, the majority (82%;105/128) had
been employed, of whom 33 (31%) had not returned to work at time of study
participation.
Prevalence of post-COVID Fatigue
Fatigue was assessed using the CFQ-11 in all participants and the mean ( SD) score
was 15.8 5.9 across the study population. The mean physical fatigue score ( SD)
was 11.38 4.22, while the mean psychological fatigue score ( SD) was 4.72 1.99.
Based on the CFQ-11 case definition, 52.3% (67/128) met the criteria for fatigue, with
the mean ( SD) CFQ-11 score in this group being 20 4.4. On univariate analysis of
differences in those with and without fatigue, there was a greater number of females
in addition to a greater number of participants with a history of anxiety/depression or
anti-depressant use in the severe fatigue group (χ2 = 9.95, p = 0.002, χ2 = 5.18, p =
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0.02 respectively), but no differences in other characteristics (Table 1). There was no
association with being a healthcare worker and meeting the case definition for fatigue.
COVID-19 Disease Characteristics and Fatigue
Overall, there was no association, either using unadjusted models, or models adjusted
for age and sex, between COVID-19 disease related characteristics (days since
symptom onset, need for inpatient admission/supplemental oxygen/critical care,
length of hospital stay) and either fatigue “caseness” (using logistic regression) or total
CFQ-11 score (using linear regression) (See Table 2).
Laboratory Results and Post-COVID-19 Fatigue
The relationship between the values of six routine laboratory measures of
inflammation and cell turnover (leukocyte, neutrophil and lymphocyte counts, NLR,
LDH, CRP) had no relationship either to severe fatigue case-status (logistic
regression) or total CFQ-11 score (linear regression) under either unadjusted models
or those with adjustment for age and sex. Full results are reported in Table 2. There
was similarly no association between the serum levels of IL-6 or soluble CD25 and
either fatigue case-status or total CFQ-11 score. Of note, 112 participants (87.5%) had
CRP levels within normal range (0 – 5 mg/L), 85/99 (85.6%) with IL-6 measured had
levels within the normal range (0-7.62 pg/mL) and 93/99 (93.9%) with soluble CD25
had levels within the normal range (0-2510 pg/mL).
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Discussion
The current study represents, to our knowledge, the first report in the literature
examining the prevalence of fatigue following SARS-CoV-2 infection. There is a
significant burden of fatigue at median follow up of ten weeks, with half of the patient
cohort reporting severe fatigue. This has several profound implications. Firstly, 50%
of the participants do not feel back to full health, despite being medically deemed
recovered from their primary illness. Secondly, the impact of this fatigue on daily
function is already evident, with almost one third (31%) having not returned to
employment. This is of particular concern, given that it is recommended that post-viral
infection return to work should take place after four weeks to prevent deconditioning
(39). The high proportion of healthcare workers infected by COVID-19, not just in our
cohort but internationally, means that this will have a significant impact on healthcare
systems (38, 40, 41).
The rates of post-COVID fatigue appear much higher than those previously reported
following EBV, Q fever or RRV infection at a similar interval (19). However, post-SARS
fatigue has been reported in 40% of individuals one year after initial infection, with 1
in 4 meeting CFS diagnostic criteria at that timepoint (14). The levels of both physical
and psychological fatigue seen post-COVID are higher than those of the general
population, but do not reach the levels of those seen in chronic fatigue syndrome (42-
44). Rates of fatigue seen in our cohort are roughly equivalent to those reported in
chronic disease states (45, 46). Given that this cohort have no enduring evidence of
active infection, the rate of fatigue is noteworthy. This is particularly important in
relation to the 52% of the cohort that meet the diagnostic criteria for fatigue, as their
CFQ-11 scores approach those seen in CFS cohorts (47, 48).
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The findings concerning correlates of SARS-CoV-2-related fatigue are also notable.
The absence of association with severity of initial infection has major implications on
both the potential number of patients that may be affected and the burden this will
place on healthcare services. Previous studies on SARS have generally focused on
function and fatigue in post-ICU patients (49). Our findings would suggest that all
patients diagnosed with SARS-CoV-2 will require screening for fatigue. Our results
also show a distinct female preponderance in the development of fatigue. This is in
keeping with previous CFS findings (50). We also noted significant association with
pre-existing diagnosis of depression and use of anti-depressant medications and
subsequent development of severe fatigue. While depression and CFS have
previously been associated, there has been some debate as to the temporal
relationship (19, 22). Longitudinal studies will be needed to assess subsequent
development of depression in the aftermath of post-COVID fatigue, as well as
assessing the trajectory and persistence of fatigue.
The absence of a specific immune signature associated with persistent fatigue is a
striking positive finding. As alluded to previously, CFS has been associated with a
large number of differing changes in the inflammatory markers and immune cell
populations. However, no consistent change has been reported across multiple
studies (25). This, in combination with our results, leads us to speculate that the
pathological changes associated with CFS and post-COVID fatigue are more subtle.
CFS may be the end point of a variety of distinct pathways, or may be the consequence
of pathological changes that are no longer systemically detectable. Despite a lack of
distinct immunological findings, it is accepted that CFS can occur in the absence of
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demonstrable disease (51, 52). The lack of distinct immune signature, coupled with
the association with depression, lends credence to the multifactorial aetiology of CFS
(53). It also supports the use of non-pharmacological interventions for fatigue
management, and provides no basis for the use of immunomodulation in treating post-
COVID fatigue.
Our study concerned findings around COVID-19-related fatigue in the medium-term.
In line with published data on viral dynamics, infectivity, and duration of infection, all
our participants had recovered from their acute COVID-19 illness (54-56). The median
period between symptom onset and fatigue assessment was ten weeks, with no
participant being recruited earlier than six weeks after their last COVID-19 symptoms
or hospital discharge. Studies on CFS and post-viral fatigue have commonly assessed
individuals at least 6 months after their viral illness. Post-SARS fatigue was described
in 22 patients between 1 and 3 years post disease resolution; these patients were
chosen due to their symptoms and may therefore not be representative of the overall
cohort (13). We feel that the short interval reported here is relevant due to the burden
of fatigue seen and that COVID-19 patients were seen irrespective of post-disease
symptoms, minimising the risk of selection bias. We also believe the effect fatigue has
on self-perceived health and return to work is profound and worthy of reporting,
especially in light of the number of patients that will be affected by this and the potential
impact on individuals, employers and governments.
Management of fatigue states requires multi-disciplinary input, and will not be
appropriately addressed if follow up is by treating medical physicians alone. A suite of
interventions, including graded exercise and cognitive behavioural therapy, are
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needed to manage CFS and may be relevant to post infectious fatigue (57-59).
Furthermore, successful return to work will require ongoing input from occupational
health departments and employers (60).
Our single centre study in a predominantly Caucasian Irish population has several
limitations worthy of discussion. Our study is cross-sectional in nature and only
assessed participants at a single timepoint. As previously mentioned, we are also
reporting at a medium time point. As such, we would recommend that longitudinal
studies are designed to assess patients at multiple time points and to examine the
changes in immune markers and immune cell populations over time. It will also be
illustrative to describe the persistence of fatigue at six months and beyond. It is
important to note that there is no consensus on the nature of fatigue and its evaluation.
However, the use of the widely applied Chalder Fatigue Scale is appropriate in this
context. Further studies in large cohorts will be required to tease out fatigue subgroups
and the potential complex factors at play. We also suggest that it is now time to
consider the management of this post-COVID syndrome and advocate early analysis
of multidisciplinary fatigue management strategies.
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Conclusions
We present the first report, to our knowledge, of post-viral fatigue in those recovered
from the acute phase of COVID-19 illness. In a similar fashion to previous coronavirus
pandemics, COVID-19 appears to result in symptoms of severe fatigue that outlast the
initial acute illness. Over half of individuals in the current study demonstrated
symptoms consistent with severe fatigue a median of 10 weeks after their initial illness,
while almost one-third of those previously employed had not returned to work. Most
interestingly, fatigue was not associated with initial disease severity, and there were
no detectable differences in pro-inflammatory cytokines or immune cell populations.
Pre-existing diagnosis of depression is associated with severe post-COVID fatigue.
This study highlights the burden of fatigue, the impact on return to work and the
importance of following all patients diagnosed with COVID, not merely those who
required hospitalisation. There are enormous numbers of patients recovering from
SARS-CoV-2 infection worldwide. A lengthy post-infection fatigue burden will impair
quality of life and will have significant impact on individuals, employers and healthcare
systems. These important early observations highlight an emerging issue. These
findings should be used to inform management strategies for convalescent patients,
and allow intervention to occur in a timely manner.
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Characteristic Overall (N = 128) Non-Fatigued
(N = 61)
Fatigued
(N = 67)
Statistic
Age (mean ± SD) 49.5 ± 15 49.7 ± 16 49.3 ± 14.3 t = 0.16, p = 0.44
Gender, female (N, %) 69 (53.9%) 24 (39.3%) 45 (67.2%)
2
= 9.95, p = 0.002
Body Mass Index, kg/m
2,
(mean ± SD)
28.7 ± 5.3 28.6 ± 4.9 28.8 ± 5.8 t = -0.09, p = 0.54
Clinical Frailty Scale
(median, IQR)
2 (1-2) 2 (1-2) 1 (1-2) z = -0.15, p = 0.88
Total Number of Comorbidities
(median, IQR)
1 (0-2) 1 (0-3) 1 (0-2) z = -1.40, p = 0.16
Total Number of Medications (median, IQR) 1 (0-4) 1 (0-4) 0 (0-4) z = -1.35, p = 0.18
History of Anxiety/Depression 10 (7.8%) 1 (1.6%) 9 (13.4%)
2
= 5.18, p = 0.02
Total CFQ-11 Score (mean ± SD) [Liekert Scoring] 15.8 ± 5.9 11.2 ± 3.2 20.0 ± 4.4 t = -12.8, p<0.001
Physical Fatigue (mean ± SD) [CFQ-11 items 1-7] 11.38 ± 4.22 7.72 ± 1.87 14.54± 2.94 z = -9.52, p<0.001
Psychological Fatigue (mean ± SD) [CFQ-11 items 8-11] 4.72 ± 1.99 3.79 ± 0.97 5.52 ± 2.29 z = -5.91, p<0.001
Total CFQ-11 Score (mean ± SD) [Bimodal Scoring] 4.2 ± 3.5 1 ± 1.2 7 ± 2.2 t = -18.6, p<0.001
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Table 1. Baseline Characteristics of Study Participants by Fatigue Case Status (“Caseness”). SD: Standard Deviation, N:
Number; IQR: Interquartile Range. Data are presented as means with standard deviations or medians with interquartile ranges as
appropriate. Proportions are expressed both as numbers and percentages. Statistical analysis was carried out using t-tests,
Wilcoxon rank sum tests and chi-square tests as appropriate in order to compare differences in those without fatigue and those
non-fatigued/with non-severe fatigue as per the CFQ-11 “caseness” definition for severe fatigue.
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Non
-
Severe
Fatigue
(N = 61)
Severe
Fatigue
(N = 67)
Severe
Fatigue
(Logistic)
CFQ
-
11
(Linear)
Model 1
Model 2
Model 1
Model
2
Covid-19
Characteristics OR
(95% CI) p aOR
(95% CI) p β
(95% CI) p β
(95% CI) p
Days since
Symptom Onset 71
(66-85) 73
(56-88) 1.00
(0.98, 1.02) 0.87 1.00
(0.98, 1.01) 0.48 -0.02
(-0.07, 0.03) 0.48 -0.04
(-0.08, 0.01) 0.14
Required Admission 36
(59.0%) 35
(52.4%) 0.76
(0.38, 1.53) 0.44 1.04
(0.44, 2.43) 0.93 -0.42
(-2.48, 1.66) 0.57 0.96
(-1.29, 3.20) 0.89
Length of Stay, Days 9.5
(6-19) 8
(6-17) 1.00
(0.95, 1.04) 0.85 1.01
(0.95, 1.07) 0.78 -0.02
(-0.17, 0.14) 0.85 0.03
(-0.12, 0.18) 0.68
Required Supplemental
O
2
25
(41%) 22
(32.8%) 0.73
(0.34, 1.60) 0.44 0.98
(0.18, 2.39) 0.96 -0.99
(-3.37, 1.40) 0.41 0.21
(-2.20, 2.63) 0.86
Required ICU 10
(16.4%) 8
(13.1%) 0.55
(0.19, 1.52) 0.24 0.82
(0.26, 2.56) 0.73 -2.90
(-6.09, 0.30) 0.08 -1.25
(-4.42, 1.92) 0.44
Laboratory Values
Leukocytes (10
9
cells/L)
6.0
(5.3-7.2) 6.3
(5.4-7.4) 1.05
(0.87, 1.28) 0.59 1.05
(0.86, 1.29) 0.62 0.02
(-0.55, 0.58) 0.96 -0.02
(-0.55, 0.52) 0.96
Neutrophils (10
9
cells/L)
3.2
(2.6-4.4) 3.2
(2.8-4.3) 1.09
(0.86, 1.39) 0.49 1.08
(0.84, 1.40) 0.53 -0.01
(-0.71, 0.70) 0.99 -0.03
(-1.39, 1.33) 0.97
Lymphocytes (10
9
cells/L) 2.0
(1.6-2.3) 2.0
(1.6-2.5) 1.00
(0.62, 1.61) 0.99 0.91
(0.55, 1.50) 0.72 0.29
(-1.15, 1.73) 0.69 0.30
(-1.56, 2.16) 0.75
Neutrophil:Lymphocyte
Ratio 1.7
(1.2-2.3) 1.6
(1.3-2.3) 1.14
(0.89, 1.46) 0.30 1.23
(0.95, 1.60) 0.12 -0.04
(-0.68, 0.61) 0.81 0.18
(-0.44, 0.81) 0.56
LDH (U/L) 185
(168-208) 178
(165-195) 1.00
(0.99, 1.01) 0.69 1.00
(0.99, 1.01) 0.50 0.01
(-0.02, 0.04) 0.34 0.01
(-0.02, 0.04) 0.52
CRP (pg/mL) 1.19
(0-2.52) 1.68
(0-3.74) 1.12
(0.99, 1.27) 0.06 1.12
(0.99, 1.28) 0.07 0.17
(-0.11, 0.44) 0.23 0.12
(-0.12, 0.39) 0.31
IL-6 (pg/mL) 0
(0-4.32) 0
(0-3.52) 0.90
(0.78, 1.03) 0.13 0.90
(0.77, 1.06) 0.21 -0.18
(-0.54, 0.18) 0.33 -0.13
(-0.50, 0.25) 0.50
CD25 (pg/mL) 1118
(883-1634) 1137
(802-1606) 1.00
(1.00, 1.00) 0.76 1.00
(1.00, 1.00) 0.18 -0.00
(-0.00, 0.00) 0.73 0.00
(-0.00, 0.00) 0.41
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Table 2. Association of COVID-19, Laboratory Values and Circulating Pro-Inflammatory Cytokines with Fatigue Case
Status (Fatigue vs Non-Fatigued/Non-Severe Fatigue) and Total Fatigue Score (CFQ-11). CFQ-11: Chalder Fatigue Score;
OR: Odds Ratio; ICU: Intensive Care Unit; LDH: Lactate Dehydrogenase; U/L: Units/Litre. Summary statistics are provided as
medians with interquartile ranges or numbers with percentages as appropriate. Results of logistic regression are reported as Odds
Ratio (OR) and adjusted Odds Ratio (aOR) with appropriate 95% confidence intervals alongside corresponding p-values. Results of
linear regressions are presented as Beta-coefficients β with appropriate 95% confidence intervals and p-values. Associations were
tested unadjusted in the first instance (Model 1) with adjustment for Age and Gender (Model 2).
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