INTRODUCTION
In the last 20 years, much knowledge has been sought regarding the quality of life
(QoL) of patients with chronic diseases. In the case of chronic inflammatory demyelinating
polyradiculopathy (CIPD), some studies have been carried out with the objective of
evaluating how the daily lives of these patients have been affected.[1]
[2]
[3] However, little is known about the effect of this peripheral condition of the central
nervous system (CNS), resulting in cognitive deficits.
Previously, other groups observed cognitive impairment in patients with CIDP.[4]
[5] However, the lack of standardized cognitive tests restrains the comparison of studies
with this goal.
The present study intends to evaluate patients with CIDP from a referral center, to
better understand how the QoL of these patients is affected and whether there are
deficits in higher mental processes, such as working memory, executive function, processing
speed, and sustained attention, among others.
METHODS
Study design and participants
This is an analytical, cross-sectional, observational, case-control study approved
by the research ethics committee of the hospital, in agreement with the declaration
of Helsinki, regarding research on humans. All CIDP patients were invited to attend
the neuromuscular diseases outpatient clinic of Hospital Universitário Antonio Pedro
at Universidade Federal Fluminense on a predetermined day. Only those who signed the
informed consent form were subjected to the research protocol. In this study, 21 patients
were excluded from the total of 28 who attended, due to the associated comorbidities
that could generate a confounding factor in the result ([Table 1]). The diagnosis of CIDP as defined or probable was made according to the European
Federation of Neurological Societies and the Peripheral Nerve Society guidelines.[6] The control group (CG) was composed by the companions of patients who did not have
neurological diseases or complaints.
Table 1
Reasons for exclusion (number of patients)
|
Type 2 diabetes mellitus (5)
|
Psychiatric illness (1)
|
|
Hereditary neuropathy with liability to pressure palsy (HNPP) (1)
|
Epilepsy (1)
|
|
Guillain-Barré syndrome (1)
|
Preestablished dementia (1)
|
|
Multifocal motor neuropathy (1)
|
Hypothyroidism (1)
|
|
Positive serological test for human T-cell lymphotropic virus (1)
|
Withdrawal during the research (1)
|
|
Complex regional pain syndrome (1)
|
Age > 65 years (3)
|
|
Charcot-Marie- tooth disease (1)
|
Instruments
The patients were interviewed and examined to gather information about the disease.
Thereafter, participants completed a neuropsychological test battery consisting of
mini-mental state examination (MMSE), digit symbol substitution test associated with
the symbol copy test (DSST), and Montreal cognitive assessment (MoCA) to evaluate
cognitive abilities. The Beck depressive inventory-I (BDI-I), the 36-item short-form
health survey (SF-36), and the fatigue severity scale (FSS) were applied to assess
QoL, emotional aspects, and to know if there is some degree of fatigue in CIDP patients.
To further corroborate our findings when comparing patient performance with the respective
reference means in neuropsychological tests, we performed an additional analysis,
in which we compared patient performance directly with the performance of healthy
controls (CG).
Statistical analysis
The collected data were tabulated and later submitted to statistical analysis. The
statistical program used was R version 3.6.1. The categorical variables were described
through frequency and percentages (sex). For continuous variables (age, test scores),
the description was made by means and standard deviation for normal data or median
and percentiles (p. 25–75) for data with nonparametric distribution.
The Shapiro-Wilk test was used to determine if the data had a parametric distribution.
The Fisher exact test was used to analyze the frequency of failures in each domain
of the MMSE and MoCA.
When comparing the average time in DSST operations associated with the symbol copy
test, a general linear model (two-way analysis of variance [ANOVA] 2 × 3) with Tukey
posthoc was used for equal samples.
To correlate the count on the fatigue score (FSS) with depression measured by the
BDI and with age in the group of patients, we used the Pearson correlation test for
continuous data of parametric distribution and the Spearman correlation test for nonparametric
distribution.
To compare the scores of the MMSE, MoCA, depression (BDI), and quality of life (SF-36)
between the groups, the Student t-test was performed in case of parametric distribution or Wilcoxon-Mann-Whitney in
non-parametric distribution.
Significant p-values lower than 0.05 and a 95% confidence interval were considered.
The analysis of the test results was made respecting the scores predetermined by each
test. We did not reduce the standard deviation to consider cognitive deficit as performed
by some studies, as our CG was a mirror of the patients group, paired by a similar
number of individuals, sex, age, and scholarity, in the expectation of not generating
possible biases.
RESULTS
Seven patients and seven controls were included in the study. The mean age of the
patients was 43 years old vs. 48 years old in the CG. Most of the patients affected
by CIDP were male (71.4%).
When comparing the results of the MMSE between the two groups, there was no statistical
difference regarding the total score or its individualized domains separately ([Table 2]).
Table 2
Comparison of mini-mental state examination results
|
Patient
|
Control
|
|
|
Domain
|
Median
|
Median
|
p
|
|
Guidance
|
10
|
10
|
0.545
|
|
Immediate memory
|
3
|
3
|
0.645
|
|
Heads up
|
5
|
4
|
0.126
|
|
Evocation memory
|
2
|
3
|
0.223
|
|
Language
|
8
|
8
|
1
|
|
Visuospatial capability
|
1
|
1
|
0.645
|
|
Domain
|
Average
|
Average
|
p
|
|
total score
|
25.28
|
25.28
|
0.686
|
Regarding the MoCA results, there was also no statistical difference between the two
groups in the total score or in the respective domains ([Table 3]). The group of patients showed superior results in the test's memory domain (median
5 vs. 2, p = 0.013).
Table 3
Comparison of Montreal cognitive assessment results
|
Patient
|
Control
|
|
|
Domain
|
Median
|
Median
|
p
|
|
Visuospatial capability
|
3
|
3
|
0.890
|
|
Memory
|
5
|
2
|
0.013*
|
|
Attention, concentration, and working memory
|
5
|
6
|
0.681
|
|
Guidance
|
6
|
6
|
0.171
|
|
Domain
|
Average
|
Average
|
p
|
|
Executive function
|
2.14
|
2.42
|
0.735
|
|
Language
|
3.71
|
4.57
|
0.339
|
|
total score
|
24.14
|
23.28
|
0.757
|
Note: *p < 0.05.
The statistical methods used also showed that there were no significant differences
between the groups when comparing the MMSE and MoCA of patients and controls (MMSE
p = 0.757; MoCA p = 0.687) (patients p = 0.504; controls p = 0.129). Therefore, it was not possible to assess which of the instruments, MMSE
or MoCA, is more sensitive to identify cognitive decline in this sample.
Similarly, there was no difference between the groups with respect to the results
of the digit symbol substitution test (DSST) associated with the symbol copy test
(30s p = 0.209; 60s p = 0.143; 90s p = 0.231). We also assessed whether there was a correlation between DSST (90s) and
executive function (track test, phonemic verbal fluency, and verbal abstraction).
The results were: control group p = 0.685, patient group p = 0.717. For the comparison of DSST and visuospatial capacity (clock drawing test
and cube copy) in the MoCA, the results were: control group p = 0.625 and patients' group: p = 0.212. Therefore, no significant correlation in any of the correlations.
There was no correlation between the fatigue levels of patients assessed by the FSS
and the levels of QoL in most domains of SF-36 (p = 0.610). However, there was a strong negative correlation between fatigue levels
and the SF-36 vitality domain (rho = -0.833, p = 0.019) ([Figure 1]). There was also no significant correlation between depression (BDI) or age and
patients' quality of life. Just as there was no correlation between fatigue and depression
in patients (p = 0.610) ([Table 4]).
Table 4
Correlation between the short-form health survey domains and fatigue, depression,
and age
|
Variable
|
Functional capacity (p)
|
Limitation by physical aspects (q)
|
Pain (p)
|
General health status (p)
|
Vitality (p)
|
Social aspects (p)
|
Limitations due to emotional aspects (p)
|
Mental health (p)
|
|
Fatigue (FSS)
|
0.264
|
0.289
|
0.213
|
0.061
|
0.019*
(rho = −0.833)
|
0.196
|
0.295
|
0.090
|
|
Depression (BDI)
|
0.943
|
0.808
|
0.321
|
0.493
|
0.936
|
0.393
|
1
|
0.507
|
|
Age
|
0.725
|
0.354
|
0.953
|
0.08
|
0.159
|
0.708
|
0.738
|
0.162
|
Abbreviations: BDI, Beck depressive inventory; FSS, fatigue severity scale.
Note: *p < 0.05
Figure 1 Correlation between fatigue and quality of life – vitality domain.
Finally, QoL was assessed comparatively in both groups. The control group showed higher
levels of QoL in the domains of mental health, pain, general health, and limitation
due to physical aspects. In addition, higher levels of depression were observed in
the group of patients when compared to the CG (mean 15.28 vs. 3.42, p < 0.001).
DISCUSSION
The average age and sex of the patients in the present study found similarity with
previous studies, in which CIDP patients are on average 50 years old and mostly male.[1]
[4]
[7]
[8] There is still no information to explain the male predominance,[1]
[4]
[7] nor any hypothesis.[1] This finding suggests that some characteristics of certain neuropathies are independent
of environmental factors and vary according to the population studied.[7]
Chronic inflammatory demyelinating polyradiculopathy is a rare disease in which the
number of individuals affected is small, including in referral centers for diagnosis.
For this reason, we chose not to divide the participants into age and sex subgroups
when performing the statistical analyses presented. However, there is sufficient information
for discussion about the subject and observation of trends that can be evaluated further
in later using larger sample studies. It is relevant to notice that the CG of this
study has the same number of individuals belonging to the patients group, in addition
to a similar number of men and women and a very close level of education. The average
age is also practically the same between the two groups. This fact makes the CG a
mirror of the patients in this study and provides a more accurate comparison between
them. All of this in the expectation of not generating possible biases, as observed
in the study by Yalachkov et al., (2020), in which the CG has a much higher number
of individuals than the group studied, in addition to a higher level of education.
In the assessment of the MMSE, there were no results below the reference value for
normality according to the predetermined score for the level of education of the population
of this study. There are few published studies on the assessment of cognitive deficits
in peripheral neuropathies and these with different applied methodologies.[4]
[5] Thus, the comparison between published papers is impractical.
The results of the MoCA of this population were curious, since the CG obtained a result
below the expected for the short-term memory domain. As neither age nor schooling
differed significantly between the groups, anxiety for excellent performance on cognitive
tests and discomfort with the research environment are likely causes of this result.
This battery of tests, like the MMSE, was not included in the few existing studies
on cognitive function in patients with CIDP.[4]
[5] This fact also makes comparison with their results impossible. Since MoCA is an
easy-to-apply battery, it can be performed even during routine outpatient consultations,
with high sensitivity for the diagnosis of mild cognitive impairment, it is an indispensable
test in any study that aims to assess cognitive function and its particularities.
The information processing speed (IPS) plays an important role in the processes that
are requested subsequently as memory, attention, and executive function. Researchers
suggest that slow IPS is a characteristic of cognitive impairment.[9]
To better evaluate this processing speed, executive functioning, working memory, concentration,
and sustained attention, we use the DSST associated with the symbol copy test. Although
we did not find statistically significant differences between the study population,
analyzing the average result obtained in each test, we observed a tendency for patients
to be slower than the CG in carrying out the tests. There is an inclination to the
CG be more effective when using the memorization and learning mechanism, causing it
to have an average time gain when copying symbols. This did not occur in average values
in the group of patients ([Table 5]). This result was different from those evidenced by the group from Germany.[4] This fact is probably due to a bias in the selection of the CG or by a very small
number of individuals in the present study, for whom it was not possible to decrease
the standard deviation of the tests, as it is frequently used and is already well
established in cognitive assessment research with neurological patients.[4] The execution of the DSST involves the recruitment of frontoparietal areas related
to selective attention processes, occipital areas related to visual attention, and
cerebellum, demanding both hemispheres for most areas.[10] These outcomes reflect the recruitment of areas related to the downward control
of attention, such as the middle and upper frontal gyri and the upper and lower parietal
gyri.[11]
[12]
Table 5
Results obtained from the digit symbol copy test and symbol copy
|
Control
|
Digit symbol
|
Average time spent per symbol
|
Average
|
|
30
|
60
|
90
|
30
|
60
|
90
|
|
1°
|
20
|
34
|
51
|
0.67
|
0.57
|
0.57
|
0.38
|
|
2°
|
4
|
8
|
15
|
0.13
|
0.13
|
0.17
|
|
3°
|
9
|
18
|
23
|
0.30
|
0.30
|
0.26
|
|
4°
|
10
|
23
|
43
|
0.33
|
0.38
|
0.48
|
|
5°
|
13
|
25
|
33
|
0.43
|
0.42
|
0.37
|
|
6°
|
7
|
14
|
19
|
0.23
|
0.23
|
0.21
|
|
7°
|
23
|
45
|
58
|
0.77
|
0.75
|
0.64
|
|
Control
|
Symbol copy
|
Average time spent per symbol
|
Average
|
|
30
|
60
|
90
|
30
|
60
|
90
|
|
1°
|
21
|
41
|
62
|
0.70
|
0.68
|
0.69
|
0.43
|
|
2°
|
8
|
12
|
16
|
0.27
|
0.20
|
0.18
|
|
3°
|
9
|
19
|
27
|
0.30
|
0.32
|
0.30
|
|
4°
|
15
|
32
|
46
|
0.50
|
0.53
|
0.51
|
|
5°
|
17
|
30
|
42
|
0.57
|
0.50
|
0.47
|
|
6°
|
7
|
12
|
19
|
0.23
|
0.20
|
0.21
|
|
7°
|
19
|
41
|
56
|
0.63
|
0.68
|
0.62
|
|
Patient
|
Digit symbol
|
Average time spent per symbol
|
Average
|
|
30
|
60
|
90
|
30
|
60
|
90
|
|
1°
|
10
|
22
|
33
|
0.33
|
0.37
|
0.37
|
0.33
|
|
2°
|
12
|
26
|
36
|
0.40
|
0.43
|
0.40
|
|
3°
|
9
|
17
|
25
|
0.30
|
0.28
|
0.28
|
|
4°
|
12
|
18
|
31
|
0.40
|
0.30
|
0.34
|
|
5°
|
19
|
37
|
44
|
0.63
|
0.62
|
0.49
|
|
6°
|
4
|
7
|
12
|
0.13
|
0.12
|
0.13
|
|
7°
|
11
|
19
|
30
|
0.37
|
0.32
|
0.33
|
|
Patient
|
Symbol copy
|
Average time spent per symbol
|
Average
|
|
30
|
60
|
90
|
30
|
60
|
90
|
|
1°
|
14
|
23
|
36
|
0.47
|
0.38
|
0.40
|
0.33
|
|
2°
|
8
|
17
|
26
|
0.27
|
0.28
|
0.29
|
|
3°
|
8
|
15
|
21
|
0.27
|
0.25
|
0.23
|
|
4°
|
10
|
19
|
26
|
0.33
|
0.32
|
0.29
|
|
5°
|
18
|
31
|
51
|
0.60
|
0.52
|
0.57
|
|
6°
|
4
|
9
|
17
|
0.13
|
0.15
|
0.19
|
|
7°
|
11
|
24
|
33
|
0.37
|
0.40
|
0.37
|
Concerning the QoL, it is being considered increasingly important regarding the evaluation
of the progression of the disease, treatment, and management of care provided to patients
with demyelinating diseases.[13] In the sample analyzed in this study, no correlation was observed between the fatigue
levels of the FSS patients and the QoL levels in most SF-36 domains. However, there
was a strong negative correlation between fatigue levels and the vitality domain among
the patients affected by CIDP in the present study ([Figure 1]), with 57% reporting severe fatigue.
In patients with neurological diseases, fatigue is different from that reported by
other patients, leading to a greater impact on daily life.[14] Our sample demonstrated to believe that vitality and fatigue are present together,
belonging to the same context.
There was no significant correlation between the levels of depression measured by
the BDI-I and the patients' QoL, just as there was no correlation between depression
and the results obtained in the cognitive tests.
Previous published papers with patients with CIDP or another chronic disease showed
that there is a greater impairment of functional capacity and physical health than
emotional and social aspects[1]
[15] ([Figure 2]). However, in our sample, when we compared the CG with the patients, the CG showed
higher QoL in the domains of mental health, pain, general health, and limitation due
to physical aspects. The group of patients had higher levels of depression when compared
to controls, (average BDI-I score 15.28 vs. 3.42, p < 0.001). Although it was not statistically significant, our patients with CIDP had
a limitation due to emotional aspects, higher than those evidenced by other groups
([Figure 3]).
Figure 2 Comparison of the results of the domains of quality of life assessed by the SF-36
and depression by the BDI. Notes: ns: Not significant; *p < 0.05; **p > 0.01; ***p < 0.001.
Figure 3 Comparison in the SF-36 domains between studies. Notes: *Approximate data obtained
through graphical analysis. **Abbreviations: PC, physical capacity; LFA, limitation
by functional aspects; PA, pain; GHS, general health status; V, vitality; SA, social
aspects; LEA, limitation by emotional aspects; MH, mental health.
Comparing our findings with other studies on QoL in patients with CIDP, we can see
that the affected patients have practically the same level of functional capacity,
and that our sample has limitations due to physical aspects similar to that presented
by the Swedes.[1]
[2] It is much lower than what the group from Universidade de São Paulo found in its
sample.[1]
The SF-36 pain score was very similar in all samples. Nevertheless, only 28.8% of
the patients in the present study self-reported pain and 57.1% reported cramps.
Regarding the global health and vitality scale scores, although all samples exhibit
similar results, our patients show a worse quality in these domains compared to the
other groups. Concerning the mental health domain, the CIDP patients in this study
have a worse quality of life in this regard, pointing out that the limitation due
to emotional aspects (EAs) is present.
Study limitations
The small sample size of patients was an important limitation in our study. This compromised
the power of the statistical tests. That is, one may not find significant differences
due to the type 2 error, in which the alternative hypothesis is rejected, generating
a false negative result.
In conclusion, the SF-36 proved to be an excellent instrument for assessing the quality
of life of these patients.[1]
[2]
[3] As for the cognitive deficit, there were no significant differences between the
case group and the control group. Despite this, we verified, through the DSST associated
with the symbol copy test, a tendency to decrease the processing speed of patients
with CIDP. The application of a broader battery of tests that thoroughly assess the
executive function in these individuals may provide more information on this issue.
However, due to the small sample size, we could not fill the knowledge gap on cognitive
impairment. Chronic inflammatory demyelinating polyradiculopathy is a rare disease
and, therefore, it is difficult to acquire many patients and, consequently, have statistical
relevance in any study that investigates this pathology. Future multicenter studies,
in which a greater number of individuals with CIDP may be present, would be interesting
to better assess cognitive impairment in this disease.
