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https://doi.org/10.1093/arclin/acac093 Advance Access publication 2 December 2022
Archives of Clinical Neuropsychology 38 (2023) 196–204
Cognitive Complaints Assessment and Neuropsychiatric Disorders After
Mild COVID-19 Infection
Mariana Beiral Hammerle1,*,Deborah Santos Sales1,Patricia Gomes Pinheiro1,
Elisa Gutman Gouvea1,Pedro Ignacio F. M. de Almeida1,Clarissa de Araujo Davico1,
Rayanne S. Souza1,Carina Tellaroli Spedo2,Denise Hack Nicaretta1,Regina Maria Papais Alvarenga1,
Karina Lebeis Pires1,Luiz Claudio Santos Thuler3,Claudia Cristina Ferreira Vasconcelos1
1Departamento de Neurologia, Hospital Universitário Gaffrée e Guinle/HUGG Programa de Pós Graduação em Neurologia da Universidade Federal do
Estado do Rio de Janeiro (UNIRIO), RJ, Brazil
2Departamento de Psicologia, Universidade Federal de São Carlos, SP, Brazil
3Universidade Federal do Estado do Rio de Janeiro e Instituto Nacional de Câncer, RJ, Brazil
*Corresponding author at: Hospital Universitário Gaffrée e Guinle—HUGG, Universidade Federal do Estado do Rio de Janeiro, 775 Mariz e Barros St,
Tijuca, Rio de Janeiro, RJ 22.270-004, Brazil. Tel.: (21) 2264-5317; fax: (21) 2264-5177.
E-mail address: marianabeiral@gmail.com (M. B. Hammerle).
CAAE: 33659620.1.1001.5258
Accepted 26 October 2022
Abstract
Objectives: This study aimed to analyze cognitive impairment associated with long-term coronavirus disease 2019 (COVID-19)
syndrome and its correlation with anxiety, depression, and fatigue in patients infected with severe acute respiratory syndrome
coronavirus.
Methods: This was a cross-sectional study of 127 patients with COVID-19. Tests to screen for neuropsychiatric symptoms
included the Fatigue Severity Scale, Mini-Mental State Exam 2 (MMSE-2), Symbol Digit Modalities Test (SDMT), and Hospital
Anxiety and Depression Scale.
Results: In cognitive tests, SDMT was abnormal in 22%, being more sensitive than MMSE-2 to detect cognitive changes.
Furthermore, although manifestations such as fatigue, depression, and anxiety were frequent in the post-COVID-19 phase,
these 3 conditions, known to contribute to cognitive impairment, were slightly correlated with worse performance on the rapid
screening tests.
Conclusions: In patients with mild COVID-19 and cognitive complaints, SDMT helped to confirm disturbances in the attention
domain and processing speed.
Keywords: COVID-19; Post-COVID-19; Neurocognitive deficits; Neurocognitive screenings
Introduction
AccordingtotheWorld Health Organization, 2022, 376,478,335 cases of coronavirus disease 2019 (COVID-19) have been
recorded, along with 5,666,064 deaths (as of January 31, 2022). There are an increasing number of reports on persistent and
prolonged effects after the acute phase of COVID-19. This syndrome is characterized by persistent symptoms and/or late or long-
term complications beyond 4 weeks from the onset of symptoms (Nalbandian et al., 2021). Some scholars have characterized
the long-term COVID-19 syndrome as a condition where symptoms last for more than 3 months after the onset of the first
symptom of the acute phase (Yong, 2021). The 2003 severe acute respiratory syndrome (SARS) epidemic and the 2012 Middle
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East respiratory syndrome outbreak also recorded persistent symptoms, raising concerns about the clinically important sequelae
of COVID-19 (Nalbandian et al., 2021).
There is strong evidence that severe acute respiratory syndrome coronavirus (SARS-CoV-2) infects the central nervous system
(CNS) via the nasal mucosa or hematogenous spread, reaching endothelial cells and neurons (Miners, Kehoe, & Love, 2020).
Cerebral hypoxemia and hypoperfusion resulting from coagulopathy, with thrombotic occlusion of the cerebral microvascu-
lature, in addition to the inflammatory injury caused by the direct action of the virus on neuronal cells, particularly in regions
of the brain that are highly susceptible to hypoxia, such as the hippocampus, are now recognized as responsible for cerebral
tissue damage caused by COVID-19 (Miners et al., 2020). Such damage to the CNS can have a long-term negative effect on
cognitive function, daily functioning, and quality of life, even several months after the recovery from COVID-19 (Miskowiak
et al., 2021). Reports of acute cognitive complications such as attention deficits and dysexecutive symptoms are also emerging
(Kumar, Veldhuis, & Malhotra, 2021).
Similarly, acute psychiatric manifestations of COVID-19 have been reported in previous studies, including increased stress,
anxiety, and depression. In the long-term, people with psychiatric presentations can also be affected by the outcome of their
illness, stigma or memory, and amnesia associated with the period when they were severely ill and hospitalization (Kumar et al.,
2021). In addition to the CNS damage caused by the virus, a long hospitalization period can contribute to neuropsychiatric
problems. Fatigue, muscle weakness, and sleep difficulties have also been reported (Miskowiak et al., 2021). A retrospective
study of 236,379 patients with COVID-19 found estimated incidences of 0%–67%, 17%–39%, and 1%–10% for dementia,
anxiety disorders, and psychotic disorders, respectively. Moreover, these incidences are higher in patients with more severe
COVID-19 and those admitted to intensive care units (Taquet, Geddes, Husain, Luciano, & Harrison, 2021). However, studies
have found that long-term COVID-19 affects even people with mild-to-moderate COVID-19 that do not require respiratory
support or intensive care, and patients who are no longer positive for SARS-CoV-2 (Yong, 2021).
The recognition of the high prevalence of post-COVID-19 neuropsychiatric manifestations inspired this study; the main
objective of which was to analyze cognitive impairment as a sequela and its correlation with depression, anxiety, and fatigue in
patients with long-term COVID-19 syndrome.
Materials and Methods
Patients
A cross-sectional study was performed in a cohort of 127 patients diagnosed with COVID-19 according to the diagnostic
criteria of the Brazilian Ministry of Health (Secretaria de Ciência, & Tecnologia, Inovação e Insumos Estratégicos em Saúde
[SCTIE], 2020). By clinic epidemiological criteria, individuals with flu-like syndrome or SARS with a history of close or home
contact in the 14 days prior to the onset of symptoms, and/or individuals with flu-like syndrome or SARS fulfilling the laboratory
criteria (detection of SARS-CoV-2 by real-time RT-qPCR method and/or by the immunochromatography method for antigen
detection) were considered as confirmed cases of COVID-19.
These patients were regularly followed up at the Neurological Manifestations Post-COVID-19 Outpatient Clinic between
September 2020 and 2021. The eligibility criteria were as follows: confirmed diagnosis of COVID-19, age 18 years or older,
and demonstrated understanding of the administered tests.
This study was conducted in accordance with the Ethical Guidelines of the Declaration of Helsinki. It was approved by the
Ethics Review Board (protocol number CAAE: 33659620.1.1001.5258). All participants provided written informed consent and
were given copies of their signed consent forms. Patients self-identified their ethnicity as White or non-White.
Data were collected on comorbidities most often associated with the risk of severe COVID-19, including arterial hypertension,
diabetes mellitus, asthma, obesity, previous stroke, previous myocardial infarction, previous deep-vein thrombosis, and smoking.
All patients with severe symptoms of respiratory failure or complications were hospitalized. During the clinical interview,
no patient reported cognitive complaints or symptoms of anxiety or depression prior to the occurrence of COVID-19.
Tests administered. All questionnaires had already been validated for the Portuguese language and were therefore administered
in Portuguese (Botega, Bio, Zomignani, Garcia, & Pereira, 1995;Castro et al., 2006;Silva, Spedo, Barreira, & Leoni, 2018;
Spedo, Pereira, Foss, & Barreira, 2018;Valderramas, Feres, & Melo, 2012). The questionnaires were administered in a quiet
environment and any doubts about the questions were clarified.
Fatigue was assessed using the Fatigue Severity Scale (FSS) with a cut-off of 28 for fatigued patients. Cognitive impairment
screening was performed by administering the following tests: Mini-Mental State Exam 2 (MMSE-2) and Symbol Digit
Modalities Test (SDMT). In addition, all patients were asked whether they perceived any cognitive deficits after COVID-19.
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Questionnaires were administered to all patients, with or without cognitive complaints. Anxiety and depression screening was
performed using the Hospital Anxiety and Depression Scale (HADS).
Fatigue Severity Scale. The FSS consists of nine items that assess the intensity and severity of fatigue in relation to certain
activities in the evaluated patients (Rajovic et al., 2021;Valderramas et al., 2012). The items are scored on a Likert scale, where 1
corresponds to “strongly disagree” and 7 corresponds to “strongly agree.” The total number of points may vary from 9 to 63, with
values equal to or <28 indicating fatigue (Alvarenga Filho, Carvalho, Dias, & Alvarenga, 2010;Krupp, Larocca, Muir-Nash, &
Steinberg, 1989).
Mini-Mental State Exam 2. The MMSE-2 was administered in the translated, adapted, and validated versions of Brazilian
Portuguese (Spedo et al., 2018). This test consists of three versions: the brief version (BV), standard version (SV), and expanded
version (EV). The latest version has three parts: Parts 1 and 2 corresponding to BV and SV, respectively, and the third part. The
first two versions correspond to the conventional MMSE.
The maximum total score of the MMSE-2 is 90 points, which is divided as follows: BV totals 16 points, which when added
to 14 points of the SV makes the maximum possible value reach 30 points, which is equivalent to the conventional MMSE total
score, and more than 60 points correspond to the additional third part. In MMSE-2, the general structure of the conventional
MMSE was maintained, some items that were difficult to translate into other languages were modified, and others were replaced
to increase the difficulty of MMSE-2. The test results were calculated using the t-score and normative demographic variables
(age and education). According to the t-score, the patients in the sample were classified as superior average, average, lower
average, borderline, and deficient. The patients whose test scores changed/decreased were classified as borderline or deficient.
Symbol Digit Modalities Test. The SDMT was developed to assess neurological impairments. This test assesses neurocognitive
function measures, such as patient attention, speed of information processing (IPS), and concentration (López-Góngora, Querol,
& Escartín, 2015). The oral and written sections present two different indices of functioning to measure attention, sweeping skills,
and motor skills (Sheridan et al., 2006).
In the first stage of SDMT, patients observe nine different symbols equivalent to numbers one through nine and write the
correct number under the equivalent symbol. Soon after the patients complete the blank space below each symbol according to
the corresponding number, a blank copy of the SDMT is provided to each patient, and they must indicate the correct number for
each symbol. Each step should be completed within 90 s. The final score is calculated as the sum of the number of correct answers
for each stage. Standardized instructions for SDMT models in the Brazilian context are provided in a technical and interpretive
manual, and test performance is categorized into six levels (superior, superior average, average, lower average, borderline, and
deficient) (Silva et al., 2018). Patients with borderline or deficient performance were considered to have undergone a decrease
in the SDMT score.
The MMSE-2 and SDMT scores were interpreted according to each patient’s age, education level, and t-score.
Hospital Anxiety and Depression Scale. The HADS is a self-assessment scale developed by Zigmond and Snaith (1983)and
can assess two meaningful subscales: depression and anxiety symptoms. It consists of 14 questions divided into seven questions
for each subscale. Potential scores are based on a 4-point Likert scale ranging from 1 (never) to 3 (always). Scores below 7
indicate improbable anxiety/depression, scores ranging from 8 to 11 indicate the probability of anxiety/depression, and scores
between 12 and 21 indicate possible anxiety/depression. Higher scores suggest a higher intensity of clinical symptoms. The
HADS has previously been validated in the Brazilian population (Botega et al., 1995;Castro et al., 2006). Scores in the probable
and possible ranges were considered positive for depression/anxiety.
Statistical analysis. Descriptive sample data were obtained from a sociodemographic questionnaire.
Data analysis was performed according to the data distribution. Normally distributed variables were plotted as means and
standard deviations, and abnormally distributed variables were plotted as medians and interquartile ranges (defined as the 25th
and 75th percentiles).
For calculations between continuous variables, given that most of them had a non-normal distribution, medians and
nonparametric tests were used for comparison between two groups.
Spearman’s correlation test was used to analyze the correlation between the patients’ MMSE-2 and SDMT scores. According
to Spearman’s coefficient values, correlations were classified as very strong for values≥0.9 positive or negative, strong if
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Tab l e 1 . Clinical and demographic characteristics of the patients with COVID-19 (n=127)
Characteristics n(%) or median (IQR)
White skin color/ethnicity (n,%) 75 (59.1)
Healthcare professional (n,%) 38 (29.9)
Arterial hypertension (n,%) 28 (22.0)
Diabetes mellitus (n,%) 17 (13.4)
History of stroke (n,%) 3 (2.4)
History of myocardial infarction (n,%) 3 (2.4)
Asthma (n,%) 9 (7.1)
Smoking (n,%) 2 (1.6)
Deep vein thrombosis (n,%) 4 (3.1)
aObesity (n,%) 33 (26.0)
Hospitalization (n,%) 9 (7.1)
Regular physical activity (n,%) 54 (42.6)
Cognitive deficit complaint (n,%) 68 (53.5)
Notes:IQR=interquartile range; n=number of patients.
aBody mass index (BMI) was used to define obese and nonobese patients, with obese patients having a BMI≥30.
0.7 ≥0.9 positive or negative, moderate if 0.5 ≥0.7 positive or negative, weak if 0.3 ≥0.5 positive or negative, and very weak
if 0 ≥0.3 positive or negative (negligible correlation) (Evans, 1996).
The chi-squared test was used to compare the frequency of changes in the MMSE-2 SV, MMSE-2 BV, and SDMT between
patients with and without cognitive complaints. The same test was used to analyze the presence of fatigue, depression, and
anxiety between patients with and without cognitive complaints.
Odds ratios (ORs) with their respective confidence intervals were calculated to verify the effect size. Variables with P<0.15
in the univariate analysis were included in the multiple regression model.
The level of significance was set at p<0.05. Statistical analyses were performed using the IBM SPSS Statistics for Windows
(version 22.0; IBM Corp., Armonk, NY, USA).
Results
Tests were performed at a median of 7 months after COVID-19 infection, with the test administration time varying between
1 and 18 months.
Out of the 127 participants, 97 (76.4%) were women. The median age of the patients was 42 years, and none of the patients
were older than 50 years. Nearly 60.0% of the patients self-declared themselves as White. The patients had a median schooling
duration of 17 years. Only 7.1% of the sample required hospitalization because of COVID-19 and more than half complained
of cognitive impairment after infection. Arterial hypertension (28.0%), diabetes mellitus (17.0%), and asthma (9.0%) were the
most common comorbidities. The clinical and demographic characteristics of the study participants are shown in Tab le 1.
In depression screening, more than one-third of the patients met the criteria for depression (p<0.001) and more than half
met the criteria for anxiety (p=0.012) (Table 2).
In the cognitive tests, SDMT was abnormal in 22% of the patients, and MMSE-2 BV was abnormal in 16.5% of the patients,
whereas none of the patients had abnormal scores on the MMSE-2 EV and SV steps.
When the test results were analyzed according to the complaints of cognitive deficits, it was noticed that impairment in
step BV of the MMSE-2 was more frequent in the group of patients with cognitive complaints, but this was not statistically
significant (p=0.072). In addition, there was significantly more impairment in the SDMT test (p=0.01) as well as more fatigue,
depression, and anxiety (p=0.023; p<0.01; p=0.027, respectively) in this group (Table 3). The presence of comorbidities and
changed MMSE-2 EV and SV versions was not associated with cognitive complaints. Despite the significantly small difference
between the frequencies of changed SDMT in the groups with and without cognitive complaints, the ORs with the respective
confidence intervals were calculated to verify the size of the effect and indicated a greater chance of this test changing in the
group with cognitive complaints. This chance remained after analysis using a multiple regression model, as did depression,
which maintained its significance in univariate and multivariate analyses. Fatigue (p=0.122; OR =2.46), anxiety (p=0.69;
OR =2.22), and altered MMSE-2 BV version (p=0.132; OR =2.50) scores were not significant after multivariate analysis
(Tabl e 4).
Upon analyzing possible correlations between the versions of the MMSE-2 and SDMT, we found a positive correlation
between tests, ranging from weak to strong (Tabl e 5). In contrast, there were significant negative correlations (ranging from very
weak to weak) of the MMSE-2 and SDMT with depression (r=−0.288, p=0.013 and r=−0.397, p<0.001, respectively) and
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200 M. B. Hammerle et al. /Archives of Clinical Neuropsychology 38 (2023); 196–204
Tab l e 2 . Cognitive performance, depression/anxiety, and fatigue according to cognitive complaints
Cognitive tests Tot al (n=127) With cognitive complaints
(n=68)
Without cognitive complaints
(n=59)
p-value
MMSE-2 EV
Changed, n(%) 1 (0.8) 1 (1.5) 0
Not changed, n(%) 126 (99.2) 67 (98.5) 59 (100)
Raw score median (min, max) 59 (33, 76) 55.5 63 <0.001
t-score median (min, max) 81 (31, 98) 77.5 85 <0.001
MMSE-2 SV
Changed, n(%) 0 0 0
Not changed, n(%) 127 (100) 68 (100) 59 (100)
Raw score median (min, max) 28 (21, 30) 28 29 <0.001
tscore median (min, max) 77 (47, 86) 77 82 <0.001
MMSE-2 BV
Changed, n(%) 21 (16.5) 15 (22.1) 6 (10.2)
Not changed, n(%) 106 (83.5) 53 (77.9) 53 (89.8)
Raw score median (min, max) 15 (12, 62) 15 16 <0.001
tscore median (min, max) 47 (12, 62) 45 51 0.015
SDMT
Changed, n(%) 28 (22.0) 21 (30.9) 7 (11.9)
Not changed, n(%) 99 (78.0) 47 (69.1) 52 (88.1)
Raw score median (min, max) 43 (8, 75) 38.5 49 <0.001
tscore median (min, max) 41 (21, 6) 39 45 <0.001
Neuropsychiatric symptomatology
Depression, n(%) 51 (40.2) 39 (57.4) 12 (20.3)
Raw score median (min, max) 6 (0, 18) 8 4 <0.001
Anxiety, n(%) 65 (51.2) 41 (60.3) 24 (40.7)
Raw score median (min, max) 8 (0, 19) 9 7 0.012
Fatigue, n(%) 90 (71.0) 54 (79.4) 36 (61.0)
Raw score median (min, max) 41 (8, 64) 46 35 0.001
Notes:n=number; SDMT =Symbol Digit Modalities Test; MMSE-2 EV =Mini-Mental State Exam 2, Expanded version; SV=standard version; BV =brief
version.
Tab l e 3 . Comparison of the frequency of changes in cognitive tests (MMSE and SDMT) and presence of fatigue, anxiety, and depression between patients with
and without cognitive complaints
Tes ts With cognitive complaints (n=68) Without cognitive complaints (n=59) p-value
MMSE-2 EV changes (n;%) 1 (1.5) 0(0) 1.0
MMSE-2 SVachanges (n;%) 0 0 0
MMSE-2 BV changes (n;%) 15 (22.1) 6 (10.2) 0.072
SDMT changes (n;%) 21 (30.9) 7 (11.9) 0.01
Fatigue present (n;%) 54 (79.4) 36 (61.0) 0.023
Depression present (n;%) 39 (57.4) 12 (20.3) <0.01
Anxiety present (n;%) 41 (60.3) 24 (40.7) 0.027
Comorbidities (n,%) 36 (52.9) 27 (45.8) 0.420
Notes:n=number of patients; SDMT =Symbol Digit Modalities Test; MMSE-2 EV =Mini-Mental State Exam 2 Expanded version; SV =standard version;
BV =brief version.
aNo patient presented with altered MMSE-2 SV.
anxiety (r=−0.175, p=6.37, and r=−0.198, p=0.34, respectively), demonstrating that the higher the mental status scores,
the lower the rates of depression and anxiety in the sample studied. There was a negative correlation between SDMT and fatigue
(r=−0.212, P=0.22), and between MMSE-2 and fatigue (r=−0.149, P=1.22) (Tab le 5).
However, the presence of clinical conditions such as fatigue, depression, and anxiety correlated weakly with poor performance
in the cognitive tests (Table 6).
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M. B. Hammerle et al. /Archives of Clinical Neuropsychology 38 (2023); 196–204 201
Tab l e 4 . Independent factors associated with the probability of cognitive complaints post-COVID-19 according to univariate and multivariate analysis
Crude OR (univariate) 95% CI p-value Adjusted OR (multivariate) 95% CI p-value
MMSE-2 EV 0.53 0.45–0.63 1.0 — — —
MMSE-2 SV Incalculable — — — — —
MMSE-2 BV 2.50 0.90–6.94 0.072 2.36 0.77–7.24 0.132
SDMT 3.32 1.29–8.51 0.010 3.75 1.37–10.3 0.010
Fatigue 2.46 1.12–5.41 0.023 1.99 0.83–4.77 0.122
Depression 5.27 2.38–11.67 <0.001 5.63 2.48–12.81 <0.001
Anxiety 2.22 1.09–4.51 0.027 1.19 0.50–2.82 0.69
Comorbidity 1.33 0.66–2.68 0.420 — — —
Tab l e 5 . Correlation between the performance on tests (SDMT vs. MMSE domains) versus fatigue, depression, and anxiety
Correlations r(rhö) p-value Bonferroni corrected
SDMT versus MMSE-2 EV (number of hits) 0.712 <0.001 <0.001
SDMT versus MMSE-2 SV (number of hits) 0.488 <0.001 <0.001
SDMT versus MMSE-2 BV (number of hits) 0.348 <0.001 <0.001
SDMT versus recovery (number of hits) 0.365 <0.001 <0.001
SDMT versus attention and calculation (number of hits) 0.402 <0.001 <0.001
SDMT versus history memory (number of hits) 0.443 <0.001 <0.001
SDMT versus processing speed (number of hits) 0.732 <0.001 <0.001
SDMT versus Fatigue -0.212 0.017 0.22
MMSE-2 EV versus fatigue -0.149 0.094 1.22
SDMT versus depression -0.397 <0.001 <0.001
MMSE-2 EV versus depression -0.288 0.001 0.013
SDMT versus anxiety -0.198 0.026 0.34
MMSE-2 EV versus anxiety -0.175 0.049 6.37
Notes: SDMT =Symbol Digit Modalities Test; MMSE-2 EV =Mini-Mental State Exam 2 Expanded version.
Tab l e 6 . Heat map of Spearman’s correlation coefficients and level of statistical significance
Cognitive test SDMT
r(rhö) (p-value)
MMSE
r(rhö) (p-value)
Domains of MMSE test
MMSE EV (number of hits) 0.712 (<0.001) Not applicable
Recovery (number of hits) 0.365 (<0.001) Not applicable
Attention and calculation (number of hits) 0.402 (<0.001) Not applicable
History memory (number of hits) 0.443 (<0.001) Not applicable
Processing speed (number of hits) 0.732 (<0.001) Not applicable
Clinical conditions
Fatigue −0.212 (0.22) −0.149 (1.22)
Depression −0.397 (<0.001) −0.288 (0.013)
Anxiety −0.198 (0.34) −0.175 (6.37)
Notes: Green signifies a strong correlation, lilac signifies a weak correlation, orange signifies a very weak correlation, and gray indicates a negligible correlation.
Abbreviations: SDMT =Symbol Digit Modalities Test; MMSE-2 EV =Mini-Mental State Exam 2 Expanded version.
Discussion
In our sample, only 7.1% of the patients were hospitalized owing to COVID-19, suggesting that most patients presented
with a mild form of the disease. Despite this, more than half of the participants complained of cognitive impairment, which is
supported by the results of previous studies, indicating that cognitive impairment after COVID-19 is not necessarily related to
hospitalization or disease severity (Miskowiak et al., 2021;Taquet et al., 2021;Woo et al., 2020;Yong, 2021).
Cognitive impairment can be associated with anxiety and depression (Miskowiak et al., 2021). In the sample analyzed here,
53% of the patients reported subjective complaints of cognitive deficits. Despite the high frequencies of fatigue, depression, and
anxiety detected by the specific questionnaires, in the correlation analysis, a very weak or even negligible negative correlation
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was detected between these neuropsychiatric conditions and the performance in the SDMT and MMSE tests. This finding may
indicate that the presence of psycho-emotional conditions had little influence on cognitive difficulties in patients.
This result may have been influenced by the profile of the sample analyzed, which mainly included young patients with a high
level of education and mild COVID-19 infection. Furthermore, the HADS separates patients into those with possible (scores
between 12 and 21), probable (scores ranging from 8 to 11), and improbable (scores below 7) depression or anxiety. In this study,
probable and possible results were considered positive for anxiety and depression. Therefore, the test can be interpreted as more
sensitive rather than specific, classifying those patients as anxiety and depression positive who would otherwise be classified as
healthy in more extensive tests.
However, fatigue, depression, and anxiety are post-COVID-19 realities. Several studies (Giurgi-Oncu, 2021;Taquet et al.,
2021) identified these conditions with frequencies >50%. A study published in the Lancet that included 236,379 patients
diagnosed with COVID-19 found the prevalence of anxiety in up to 51% of patients with COVID-19 who were not hospitalized
(Taquet et al., 2021). This finding is similar to that of the present study, in which an equal prevalence was found in the sample
of patients who were not hospitalized. Another study, also using the HADS to assess anxiety and depression in patients after
COVID-19, found that 27.84% and 40.5% of outpatients had depression and anxiety, respectively (Giurgi-Oncu, 2021). These
results suggest that depression and anxiety are, respectively, nearly three times and one and a half times more common in
patients with cognitive complaints. A systematic review of neuropsychological and psychiatric sequelae of COVID-19 described
memory complaints in up to 34% of the patients. The prevalence of depressive symptoms ranged from 10% to 68%, whereas the
anxiety rate ranged from 5% to 55%. Two to 3 months after hospital discharge, 40%–69% of the patients complained of fatigue
(Vanderlind et al., 2021). In our study sample, ∼70% presented fatigue according to the FSS.
In the cognitive assessment tests, the SDMT was altered in 22% of the samples. SDMT is conceptualized as a measure of IPS,
and its cognitive processes are multifarious. Studies with multiple sclerosis samples demonstrate that along with IPS, SDMT taps
into other processes of memory and rapid automated naming (RAN) (Patel, Walker, & Feinstein, 2017;Sandry et al., 2021). We
hypothesized that the high sensitivity of SDMT in COVID-19 is likely due to its ability to capture substantially heterogeneous
cognitive profiles owing to its nonspecific unitary function.
Another hypothesis is that the reduction in SDMT scores is due to the pattern of cognitive impairment associated with COVID-
19, as found in previous studies, such as attention deficit, impairment in executive function (Kumar et al., 2021), learning, and
language (Vanderlind et al., 2021;Yang, Zhao, Liu, Wu, & Li, 2021).
The conventional MMSE is one of the most widely and easily used screening tests in general practice to detect cognitive
impairment, but the MMSE-2 EV was chosen to be administered because the sample studied had a high level of education;
therefore, besides each step assessing a different cognitive domain, the third step of the MMSE-2-EV has a higher degree of
difficulty (Spedo et al., 2018). The BV step assesses recording, temporal and spatial orientation, and recovery of memory and is
useful for rapid clinical assessment and for screening larger populations; the SV step assesses attention, calculation, language,
and visuospatial function. Together, these two versions have adequate sensitivity and specificity for the detection of cognitive
decline in patients with dementia. The EV step is recommended for a more detailed follow-up of cases because it assesses story
memory and processing speed. In the MMSE-2, assessments of story memory and processing speed were added to increase the
clinical utility of the conventional MMSE by extending the ceiling effect and increasing the sensitivity and specificity of this
version to detect cognitive impairment in patients with Alzheimer’s disease and with subcortical dementia (Baek, Kim, Park, &
Kim, 2016). Story memory evaluates explicit verbal learning and verbal free recall, and the processing speed test (symbol-digit-
coding test) measures psychomotor ability and incidental learning primarily associated with the executive function of the frontal
lobe (Sheridan et al., 2006). In the MMSE-2, there are two alternative forms, the blue and red forms, to reduce the learning effect
that may occur upon repeated use (Baek et al., 2016).
In our findings, the EV and SV of the MMSE-2 were normal in the entire sample and the BV of the MMSE-2 was altered
by 16.5%. The BV of MMSE-2 has adequate sensitivity and specificity for detecting cognitive decline in large samples and is
highly correlated with verbal memory and frontal lobe function (Baek et al., 2016). Similarly, SDMT is highly correlated with
memory and RAN (Sandry et al., 2021).
SDMT reflects the functionality of the frontotemporal attention network and occipital cortex as well as the cuneus, precuneus,
and cerebellum (Silva et al., 2018). Among the MMSE-2 versions, BV consists of record assessment, time orientation, location
orientation, and recall (Baek et al., 2016). The nature of the versions is a possible explanation for our results, as MMSE-2-BV
and SDMT show deficits in verbal memory and attention, present in patients with post-COVID-19 cognitive complaints.
Although the SV of the MMSE-2 added attention and language assessment, and the EV of the MMSE-2 added a history
memory test, they did not change in patients with post-COVID-19 cognitive complaints. In addition to the sample with a
high average schooling of 17 years, these tests may not have been able to detect cognitive impairments in a sample possibly
with greater cognitive reserve, because they are short screening tests. This indicates a possible lower sensitivity in relation to
the SDMT.
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M. B. Hammerle et al. /Archives of Clinical Neuropsychology 38 (2023); 196–204 203
The interval between the resolution of SARS-CoV-2 infection and the search for cognitive sequelae and neuropsychiatric
symptoms was 1–18 months, which can be considered wide and prone to interference. However, the presence of comorbidities
was not statistically associated with complaints of cognitive impairment, and these variables were controlled for in the regression
model. In addition, data collection on cognitive impairment after contracting the infection was retrospective, and post-COVID
syndrome occurs on average from 4 weeks after the onset of symptoms; therefore, a longer data collection period offered chances
for detection of these manifestations. On the other hand, memory bias may have occurred, underestimating the frequency of these
complaints in patients interviewed after a longer period.
When test performance was compared with the presence of objective complaints of cognitive deficits, a higher frequency of
altered SDMT was observed, followed by the BV step of the MMSE-2. In a categorized assessment of the MMSE, it presented
a stronger correlation with SDMT in the domains of attention and calculation, history memory, and processing speed.
In the present study, fatigue was more frequent in patients with cognitive complaints, and a higher degree of fatigue
was correlated with worse performance on the SDMT and MMSE, but with a statistically weak correlation. This result was
corroborated by Ortelli et al. (2021), who reported executive impairments in patients with fatigue, suggesting that dysexecutive
syndrome and deficits in cognitive control are related to cognitive fatigue (Ortelli et al., 2021).
Limitations
This study had several limitations. The sample was predominantly women and was highly educated. There are no results from
a longitudinal follow-up of these patients to predict whether cognitive deficits, fatigue, depression, or anxiety tend to disappear
over time or become chronic. In addition, the patients did not undergo screening tests for cognitive impairment, anxiety, and
depression prior to COVID-19. Considering the pandemic scenario of social isolation, the risk of hospitalization, death, and
unemployment, it is possible that these patients had some degree of depression or anxiety before contracting COVID-19. Thus,
the comparison between the presence of these symptoms before and after COVID-19 is difficult to control and causes a bias.
Finally, laboratory screening for other possible causes of cognitive impairment was lacking. A more detailed longitudinal
study is needed to resolve these issues.
Conclusions
Even in patients with a mild presentation of COVID-19, complaints of cognitive impairment were frequent. Screening tests,
such as SDMT, helped to confirm disturbances in the attention domain and processing speed, especially in patients with cognitive
impairment complaints after the infection. Although fatigue, depression, and anxiety were frequent in the post-COVID-19 phase,
especially in patients with complaints of cognitive deficits, these three conditions that are known to contribute to cognitive
impairment showed little correlation with poor performance on rapid screening tests.
Funding
No funding was received for this study.
Conflict of Interest
None declared.
References
Alvarenga Filho, H., Carvalho, S., Dias, R., & Alvarenga, R. (2010). Principais testes utilizados na avaliação de fadiga na esclerose múltipla. Revisão sistemática.
Revista brasileira de Neurologia,46(2), 37–43.
Baek, M. J., Kim, K., Park, Y. H., & Kim, S. (2016). The validity and reliability of the Mini-Mental State Examination-2 for detecting mild cognitive impairment
and Alzheimer’s disease in a Korean population. PLoS One,11(9), e0163792. https://doi.org/10.1371/journal.pone.0163792.
Botega, N. J., Bio, M. R., Zomignani, M. A., Garcia, J. R. C., & Pereira, W. A. B. (1995). Transtornos do humor em enfermaria de clínica médica e validação
de escala de medida (HAD) de ansiedade e depressão. Revista de Saúde Pública,29(5), 355–363. https://doi.org/10.1590/S0034-89101995000500004.
Castro, M., Quarantini, L. C., Batista-Neves, S., Kraychete, D., da Cunha Daltro, C. H., & Scippa, Â. M. (2006). Validade da escala hos-
pitalar de ansiedade e depressão em pacientes com dor crônica. Revista Brasileira de Anestesiologia,56(5), 470–477 Available from:.
https://doi.org/10.1590/S0034-70942006000500005 (Accessed April 19, 2022).
Evans, J. D. (1996). Straightforward statistics for the behavioral science. Pacific Grove, CA: Brooks/Cole Publishing.
Giurgi-Oncu, C., Tudoran, C., Pop, G. N., Bredicean, C., Pescariu, S. A., Giurgiuca, A., et al. (2021). Cardiovascular abnormalities and mental health difficulties
result in a reduced quality of life in the post-acute COVID-19 syndrome. Brain Sciences,11(11), 1456. https://doi.org/10.3390/brainsci11111456.
Downloaded from https://academic.oup.com/acn/article/38/2/196/6865028 by guest on 19 July 2023
204 M. B. Hammerle et al. /Archives of Clinical Neuropsychology 38 (2023); 196–204
Krupp, L. B., Larocca, N. G., Muir-Nash, J., & Steinberg, A. D. (1989). The Fatigue Severity Scale: Application to patients with multiple sclerosis andsystemic
lupus erythematosus. Archives of Neurology,46(10), 1121–1123. https://doi.org/10.1001/archneur.1989.00520460115022.
Kumar, S., Veldhuis, A., & Malhotra, T. (2021). Neuropsychiatric and cognitive sequelae of COVID-19. Frontiers in Psychology,12, 577529.
https://doi.org/10.3389/fpsyg.2021.577529.
López-Góngora, M., Querol, L., & Escartín, A. (2015). A one-year follow-up study of the Symbol Digit Modalities Test (SDMT) and the Paced Auditory
Serial Addition Test (PASAT) in relapsing-remitting multiple sclerosis: An appraisal of comparative longitudinal sensitivity. BMC Neurology,15(1), 40.
https://doi.org/10.1186/s12883-015-0296-2.
Miners, S., Kehoe, P. G., & Love, S. (2020). Cognitive impact of COVID-19: Looking beyond the short term. Alzheimer’s Research & Therapy,12(1), 170.
https://doi.org/10.1186/s13195-020-00744-w.
Ministério da Saúde do Brasil. Secretaria de Vigilância em Saúde. Guia de Vigilância Epidemiológica: Emergência de Saúde Pública de Importância Nacional
pela Doença pelo Coronavírus 2019—Vigilância de Síndromes Respiratórias Agudas COVID-19, 58 p. available in: https://www.conasems.org.br/wp-co
ntent/uploads/2020/08/af_gvs_coronavirus_6ago20_ajustes-finais-2.pdf (August 5, 2020).
Miskowiak, K. W., Johnsen, S., Sattler, S. M., Nielsen, S., Kunalan, K., Rungby, J., et al. (2021). Cognitive impairments four months
after COVID-19 hospital discharge: Pattern, severity and association with illness variables. European Neuropsychopharmacology,46, 39–48.
https://doi.org/10.1016/j.euroneuro.2021.03.019.
Nalbandian, A., Sehgal, K., Gupta, A., Madhavan, M. V., McGroder, C., Stevens, J. S., et al. (2021). Post-acute COVID-19 syndrome. Nature Medicine,27(4),
601–615. https://doi.org/10.1038/s41591-021-01283-z.
Ortelli, P., Ferrazzoli, D., Sebastianelli, L., Engl, M., Romanello, R., Nardone, R., et al. (2021). Neuropsychological and neurophysiological correlates of fatigue
in post-acute patients with neurological manifestations of COVID-19: Insights into a challenging symptom. Journal of the Neurological Sciences,420,
117271. https://doi.org/10.1016/j.jns.2020.117271.
Patel, V. P., Walker, L. A. S., & Feinstein, A. (2017). Deconstructing the Symbol Digit Modalities Test in multiple sclerosis: The role of memory. Multiple
Sclerosis and Related Disorders,17, 184–189. https://doi.org/10.1016/j.msard.2017.08.006.
Rajovic, T., Todorovic, N., Vracevic, M., Rajovic, N., Pavlovic, A., Pavlovic, V., et al. (2021). From burden to depressive symptoms in infor-
mal caregivers during the COVID-19 pandemic: A path analysis. International Journal of Environmental Research and Public Health,18(18).
https://doi.org/10.3390/ijerph18189577.
Sandry, J., Simonet, D. V., Brandstadter, R., Krieger, S., Katz Sand, I., Graney, R. A., et al. (2021). The Symbol Digit Modalities Test (SDMT) is sensitive but
non-specific in MS: Lexical access speed, memory, and information processing speed independently contribute to SDMT performance. Multiple Sclerosis
and Related Disorders,51, 102950. https://doi.org/10.1016/j.msard.2021.102950.
Secretaria de Ciência & Tecnologia, Inovação e Insumos Estratégicos em Saúde—SCTIE (2020). MINISTÉRIO DA SAÚDE. DIRETRIZES PARA DIAGNÓS-
TICO E TRATAMENTO DA COVID-19. Version 2 April 08, 2020(Accessed in April, 17, 2021).
Sheridan, L. K., Fitzgerald, H. E., Adams, K. M., Nigg, J. T., Martel, M. M., Puttler, L. I., et al. (2006). Normative Symbol Digit Modalities Test performance
in a community-based sample. Archives of Clinical Neuropsychology,21(1), 23–28. https://doi.org/10.1016/j.acn.2005.07.003.
Silva, P. H. R., Spedo, C. T., Barreira, A. A., & Leoni, R. F. (2018). Symbol Digit Modalities Test adaptation for magnetic resonance imaging environment: A
systematic review and meta-analysis. Multiple Sclerosis and Related Disorders,20, 136–143. https://doi.org/10.1016/j.msard.2018.01.014.
Spedo, C. T., Pereira, D. A., Foss, M. P., & Barreira, A. A. (2018). Mini Exame do Estado Mental. In Adaptação Brasileira do Mini-Mental State Examination
(2nd ed. ). Editora Hogrefe, SP, Brazil.
Taquet, M., Geddes, J. R., Husain, M., Luciano, S., & Harrison, P. J. (2021). 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19:
A retrospective cohort study using electronic health records. Lancet Psychiatry,8(5), 416–427. https://doi.org/10.1016/S2215-0366(21)00084-5.
Valderra\textit{a}mas, S., Feres, A. C., & Melo, A. (2012). Reliability and validity study of a Brazilian-Portuguese version of the Fatigue Severity Scale in
Parkinson’s disease patients. Arquivos de Neuro-Psiquiatria,70(7), 497–500. https://doi.org/10.1590/S0004-282X2012000700005.
Vanderlind, W. M., Rabinovitz, B. B., Miao, I. Y., Oberlin, L. E., Bueno-Castellano, C., Fridman, C., et al. (2021). A systematic review
of neuropsychological and psychiatric sequalae of COVID-19: Implications for treatment. Current Opinion in Psychiatry,34(4), 420–433.
https://doi.org/10.1097/YCO.0000000000000713.
Woo, M. S., Malsy, J., Pöttgen, J., Seddiq Zai, S., Ufer, F., Hadjilaou, A., et al. (2020). Frequent neurocognitive deficits after recovery from mild COVID-19.
Brain Communications,2(2). https://doi.org/10.1093/braincomms/fcaa205.
World Health Organization. (2022). Diagnostic testing for SARS-CoV-2: Interim guidance. COVID-19: Laboratory and diagnosis.https://www.who.int/publi
cations/i/item/diagnostic-testing- for-sars-cov-2 (Accessed April 2022).
Yang, F., Zhao, H., Liu, H., Wu, X., & Li, Y. (2021). Manifestations and mechanisms of central nervous system damage caused by SARS-CoV-2. Brain Research
Bulletin,177, 155–163. https://doi.org/10.1016/j.brainresbull.2021.09.015.
Yong, S. J. (2021). Long COVID or post-COVID-19 syndrome: Putative pathophysiology, risk factors, and treatments. Infectious Diseases,53(10), 737–754.
https://doi.org/10.1080/23744235.2021.1924397.
Zigmond, A. S., & Snaith, R. P. (1983). The Hospital Anxiety and Depression Scale. Acta Psychiatrica Scandinavica,67(6), 361–370.
https://doi.org/10.1111/j.1600-0447.1983.tb09716.x.
Downloaded from https://academic.oup.com/acn/article/38/2/196/6865028 by guest on 19 July 2023
