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The Hungarian validation of the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) battery and the correlation of cognitive impairment with fatigue and quality of life

Multiple Sclerosis and Related Disorders, Volume 4, Issue 6, November 2015, Pages 499 - 504

Abstract

Background

Multiple Sclerosis (MS) causes not only somatic, but also cognitive impairment regardless of the patients׳ age or the course of the disease. The Brief International Cognitive Assessment for MS (BICAMS) test, published in 2011, is a short cognitive questionnaire: a fast, reliable, sensitive and specific tool for the evaluation of the patients׳ cognitive state.

Objectives

Our primary objective was to assess the validity of the Hungarian version of the BICAMS test. Our secondary objective was to evaluate the impact of the cognitive impairment on the patient׳s quality of life and fatigue׳s impact on the patients׳ cognitive state.

Methods

65 RR-MS patients and 65 age, sex and education matched healthy control (HC) subjects completed the test and were retested after 3 weeks. The patients also completed the MS Quality of Life 54 (MSQoL54) and the Fatigue Impact Scale (FIS) assessments. Group differences were calculated by paired sample T-tests. The test-retest reliability was measured by intraclass correlation coefficients. To analyze the difference between the test-retest performances of the two groups we used two-way repeated measures ANOVA where the BICAMS battery was the single composite outcome and one-way repeated measures ANOVA. To assess the impact of the cognitive decline on the patients׳ quality of life and fatigue׳s impact on the cognitive state, we examined the correlations between results in the BICAMS and the MSQoL54 and FIS.

Results

We found significant difference (p≤0.001, p=0.017 in the first CVLT-II assessment) between MS patients and members of the HC group in all four evaluated parameters of BICAMS test in both sessions. The correlation coefficients were very strong between the tests and retests (r>0.8; p<0.001; r=0.678, p<0.001 between the CVLT-II assessments). We found that the HC group performed significantly (p=0.020) better in the retest sessions as compared to their original performance than the patients did and this difference is solely due to the difference between the CVLT-II performances. We have found significant negative correlation between the patients׳ cognitive function and the fatigue score (r<−0.3, p<0.05). Seven of the MSQoL-54 subscales correlated with the BICAMS performance (r>0.3; p<0.05).

Conclusions

The Hungarian version of the BICAMS test is a valid and reliable method for the evaluation of MS patients׳ cognitive function. It seems that because of the short retest period, the members of the HC group remembered the CVLT-II words thus performed better than the patients did. Also apparently fatigue can have a negative impact on the patients׳ cognitive state, and cognitive impairment could worsen the patients׳ quality of life.

Highlights

 

  • We have validated the BICAMS battery to Hungarian language.
  • We have evaluated cognitive impairment’s effect on the patients' quality of life.
  • We assessed the correlation between cognitive impairment and fatigue.

Keywords: Multiple Sclerosis, Cognition, Validation, BICAMS, Fatigue, Health-related quality of life.

1. Introduction

Multiple Sclerosis (MS) is a chronic, autoimmune and neurodegenerative disease of the central nervous system (CNS) which can cause a wide range of symptoms, including decline in the cognitive functions. The prevalence of cognitive impairment is found to be high in MS patients, ranging from 43–70% (Chiaravalloti and DeLuca, 2008 and Langdon, 2011). It can occur at all stages of the disease, even in clinically and radiologically isolated syndromes (CIS and RIS) and can affect any patient regardless of age and sex (Glanz and Holland, 2007 and Amato and Hakiki, 2012).

Cognitive impairment is not global in MS; it most commonly affects three aspects: information processing speed being the most vulnerable aspect followed by episodic memory and executive function ( Langdon, 2011 ). The degree of the impairment can be mild and patients may not be fully aware of it ( Langdon et al., 2012 ). Also patients often report the cognitive decline unreliably as depression can cause over-reporting, while metamemory-impairment can lead to underestimation (Sherman and Rapport, 2008, Kinsinger and Lattie, 2010, and Langdon and Amato, 2012).

There is significant correlation between the progression of cognitive impairment and the rate of brain atrophy according to studies involving magnetic resonance imaging (MRI) in the evaluation ( Filippi et al., 2010 ). Also the presence of cortical lesions is associated with the decline in cognition (Calabrese and Agosta, 2009 and Roosendaal and Moraal, 2009).

Fatigue is a very common symptom of MS; studies indicate its prevalence to be 76–92% (Krupp and Alvarez, 1988, Fisk and Pontefract, 1994, and Ford and Trigwell, 1998). Many patients report it to be the worst or one of the worst symptoms of the disease ( Ford et al., 1998 ). Studies show significant correlations between the Expanded Disability Status Scale (EDSS) score and fatigue׳s presence and severity ( Colosimo et al., 1995 ). Hypothetically fatigue can have a negative impact on the patients׳ cognitive abilities, though recent studies did not find significant correlation between cognitive status and subjective fatigue (Bailey and Channon, 2007 and Morrow and Drake, 2010).

Health-related quality of life (HRQoL) by definition represents “the functional effect of an illness and its consequent therapy upon a patient, as perceived by the patient” ( Miller, 2002 ). In the different clinical courses of MS differences were found between the subgroups: quality of life was lower of the patients with longer disease duration and with a more severe and progressive clinical course ( Pfennings et al., 1999 ). Cognitive impairment is an extremely important aspect of HRQoL as it is an important determinant of employment status and social costs ( Langdon et al., 2012 ). Also cognitive deficit impairs the patients׳ activities in everyday life by reducing physical independence, driving ability, coping, symptom management and rehabilitation potential ( Langdon et al., 2012 ). It was found that MS patients are more concerned about their mental health than physical disability ( Rothwell, 1998 ).

The importance of assessing the cognitive status of patients with MS cannot be overstated. There are several psychometric assessment batteries available, the most frequently utilized are the Brief Repeatable Battery of Neuropsychological tests (BRB-N) ( Rao, 1990 ) and the Minimal Assessment of Cognitive Function in MS (MACFIMS) ( Benedict et al., 2002 ). The problem with these batteries are that they are time-consuming (BRB-N requires 45 min, MACFIMS approximately 90 min to perform) and they require special expertise which is not routinely available in many places ( Benedict et al., 2002 ). For this reason a specialized battery has been recommended by the BICAMS committee, which is short, simple to administer and to score, and does not require any special equipment or training but is also highly sensitive ( Langdon et al., 2012 ).

2. Objectives

The objectives of our study were

  • 1. The cross-cultural validation of the BICAMS battery to Hungarian language.
  • 2. Measuring the impact of cognitive impairment on the patients׳ quality of life and fatigue׳s effect on the patients׳ cognitive state by assessing the correlations between BICAMS performance with the Hungarian versions of both the FIS ( Losonczi et al., 2011 ) and MSQoL-54 ( Fuvesi et al., 2008 ) batteries.

3. Patients and methods

3.1. Methods

The BICAMS battery comprises of 3 individual tests: the symbol digit modalities test (SDMT), the first five recall trials of the california verbal learning test II. (CVLT-II) and the first three recall trials of the brief visuospatial memory test revised (BVMT-R) ( Langdon et al., 2012 ).

The SDMT measures the information processing speed of the participant. It is a sheet of nine symbols in pseudo-randomized lines, each paired with a digit in a key at the top of the sheet. After a short practice, the patients have to pair as many of the symbols with the digits as they can in 90 s. There is both a written and an oral version of the test. During the written version the patients are given the sheet and they have to write the correct digits on the paper, whereas in the oral version both the administrator and the patient are given one sheet each and the patient is required to say the correct number out loudly while the administrator writes it down. Both versions of SDMT were administered for the capability to assess patients with impaired motor functions. The dependent variable is the total number of correct responses ( Smith, 1982 ).

The CVLT-II is a tool for measuring verbal learning. The first five recall trials of CVLT-II are comprised of a list of 16 words clustered into 4 semantic groups. During administration the administrator reads the list aloud at the approximate speed of 1 word/second. The patients have to recall as many of the words as they can and repeat them back to the administrator in arbitrary order, who writes them down. There is no time limit for this test. The dependent variable is the total number of correct words recalled during the five trials ( Delis et al., 2000 ).

The BVMT-R is the measure of visual memory. It is administered by giving the patients a blank sheet of paper divided into six equal parts and a pencil. Then the administrator shows them a matrix of 6 abstract designs for 10 s which the patients are required to reproduce on the blank sheet of paper as accurately as they can. Each design receives a score of 0, 1, or 2 based on accuracy and location criteria. There is no time limit for this test. The dependent variable was the total points received for the reproduced designs during the three trials ( Benedict, 1997 ).

The validation was conducted per the international standards given by Benedict et al. in 2012 ( Benedict et al., 2012 ). As the first step, the CVLT-II list of words were translated and retranslated from English to Hungarian and vice versa respectively (the other two tests did not require translation due to their nature). In the second step, the relevant parts of the tests manuals were translated into Hungarian. The third step was the testing, and after 3 weeks, the retesting of the patients and members of the HC group matched in age, sex and years of education between December 2013 and March 2014.

Also for the assessment of the correlations between the cognitive status and fatigue and the cognitive status and quality of life, all 65 patients had completed the Hungarian versions of the Fatigue Impact Scale (FIS) and the Multiple Sclerosis Quality of Life-54 (MSQoL54) batteries during that period.

The FIS battery is comprised of 40 questions, divided into 3 subscales: 20 questions related to social functions, 10 questions to physical functions and 10 questions to cognitive functions. Each question is marked from 0 (minimal degree) to 4 (severe degree) points (Fisk and Ritvo, 1994 and Losonczi and Bencsik, 2011).

MSQoL-54 is a battery of 54 questions which can be divided into numerous subscales representing many aspects of life: physical health, emotional well-being, role limitations due to physical problems, role limitations due to emotional problems, pain, energy, health perceptions, social function, cognitive function, health distress, sexual function, satisfaction with sexual function, change in health and overall quality of life. From these subscales, two composite scores can be derived: the physical health composite score and the mental health composite score (Vickrey and Hays, 1995 and Fuvesi and Bencsik, 2008).

We used the first BICAMS tests of the patients for comparison with FIS and MSQoL-54.

3.2. Patients and the HC group

We recruited 65 patients treated at the Multiple Sclerosis Outpatient׳s Unit of the Department of Neurology of the University of Szeged and 65 healthy controls matched in age, sex and years of education for the validation process. The patients were recruited cross-sectionally, there was no pre-selection applied for cognitive impairment. Of the patients 16 were men, 49 women; 31 of the patients studied ≤12 years, and 34 of them studied for at least 13 years. Their average age was 41.9 (±8.9) years, the average age for disease onset was 29.8 (±9.9) years and the average time from MS onset was 11.1 (±7.6) years. Their average EDSS score was 2.5 (±1.8). Of the 65 members of the HC group, 16 were men and 49 women; 31 of them studied ≤12 years, and 34 of them studied for at least 13 years. Their average age was 40.9 (±11.8) years.

We have included patients and healthy control individuals aged between 18–65 years. Their first language is Hungarian, and all the patients were diagnosed with the relapsing-remitting (R-R) course of the disease by McDonald׳s criteria. All patients were in remission during the evaluation and their EDSS score ranged from 0–6.5.

We have excluded patients suffering from CIS, secondary or primary progressive course of the disease in order to work with a homogenous population for statistical reasons. As progressive MS patients only total up to 20% of the population in Hungary ( Zsiros et al., 2014 ), their numbers would not have been representative in a patient pool of 65. We also excluded patients undergoing acute infection or an acute relapse. It was observed that psychiatric diseases, mood disorders and personality disorders have a fairly large co-incidence with MS (the leading co-morbidity being depression) ( Marrie et al., 2015 ). As it is also established that these disorders can cause cognitive impairment (Thornton and Raz, 1997, Arnett and Higginson, 1999, Bowie and Depp, 2010, Ruocco and Lam, 2014, and McIntyre and Soczynska, 2015), any patient or possible member of the HC group with diagnosed psychiatric, mood or personality disorder was excluded from our study. Also it was published recently that chronic alcohol abuse has a high prevalence among MS patients (14.8%) ( Marrie et al., 2015 ). Chronic alcoholism severely impairs the cognitive state ( Bernardin et al., 2014 ), therefore any patients or possible member of the HC group, with history of chronic alcohol abuse, were excluded from participation in the validation process.

3.3. Statistical analysis

To measure the differences between the patients and members of the HC group we used paired sample T-tests. Assessing the test-retest reliability and the correlation between BICAMS and FIS and BICAMS and MSQoL54 were done by calculating the Pearson correlation coefficients. To analyze the difference between the test and retest performances of the patients and the HC group we used two-way repeated measures ANOVA with the result of the BICAMS battery being a composite outcome and one-way repeated measures ANOVA to assess the differences by the battery-types that compose the BICAMS battery. Statistical analysis was done by using SPSS 21.0 software.

The assessment was authorized by the Ethics Committee of the University of Szeged (authorization number 127/2013).

4. Results

4.1. BICAMS validity

Both the patients and the HC group consisted of 16 men and 49 women, in both groups 31 individuals studied ≤12 years, and 34 of them studied for at least 13 years. There was no difference between the mean age of the two groups (p=0.610).

Table 1 shows the group data and the differences between the patients׳ and HC groups׳ raw scores in all 4 trials and during the retests as well. It is apparent that the MS patients performed significantly worse in all trials than the members of the HC group (p≤0.001 in all tests other than the first CVLT-II; p=0.017 in the first CVLT-II).

Table 1 Group data comparing patients with multiple sclerosis and healthy controls.

  Raw score (±SD) p
Patients HC group
SDMT written 44.31(±11.76) 54.88(±10.32) <0.001
SDMT written retest 49.38(±14.91) 62.31(±12.78) <0.001
BVMT-R 22.54(±8.54) 26.68(±5.56) 0.001
BVMT-R retest 26.89(±8.21) 31.81(±4.13) <0.001
SDMT oral 55.62(±15.48) 66.82(±12.44) <0.001
SDMT oral retest 59.40(±18.29) 72.84(±13.88) <0.001
CVLT-II. 55.37(±10.71) 59.03(±8.29) 0.017
CVLT-II. retest 61.77(±14.20) 70.58(±8.29) <0.001

SD, standard deviation; HC group, healthy control group; SDMT, symbol digit modalities test; CVLT-II., california verbal learning test II.; BVMT-R, brief visuospatial memory test revised (BVMT-R).

We identified 34 of the 65 (52.3%) patients with cognitive impairment, by the proposed criterion of “one or more abnormal tests”. As no validated threshold of cognitive impairment for BICAMS was available, we used the thresholds given in the manuals of the separate tests (SDMT, BVMT-R, CVLT-II).

We have found that 26 patients (40.0%) had abnormal SDMT scores, 24 (36.9%) patients had abnormal BVMT-R tests and 9 (13.8%) patients׳ performance was abnormal during CVLT-II testing. Per the “one or more abnormal tests” criterion we found 15 patients (23.0%) with one abnormal test, 13 patients (20.1%) with two abnormal tests and 6 patients (9.2%) with three abnormal tests.

We identified 9 (13.8%) patients with abnormal score only in their SDMT performance, 6 (9.2%) only in their BVMT-R tests. We found no patient with sole CVLT-II abnormality. Ten (15.5%) patients performed impaired in both the SDMT and the BVMT-R tests, 1 (1.5%) patient in both the SDMT and CVLT-II tests, 2 (3.1%) patients in both the BVMT-R and CVLT-II tests. 6 (9.2%) patients had abnormal scores in all three tests.

Table 2 shows the correlation between the tests and the retests. Overall correlations are very strong (r>0.8 p<0.001; r=0.678; p<0.001 between the CVLT-II assessments). Regarding the performance of the patients and the HC group separately, the patients׳ performance shows slightly higher values than the HC group, the biggest difference being in the CVLT-II test (r=0.743; p<0.001 in case of the patients, r=0.453; p<0.001 in case of the HC group).

Table 2 Correlation coefficients between the tests and the retests.

  Overall Patients HC group
r p r p r p
SDMT written 0.874 <0.001 0.883 <0.001 0.796 <0.001
BVMT-R 0.864 <0.001 0.873 <0.001 0.805 <0.001
SDMT oral 0.830 <0.001 0.884 <0.001 0.664 <0.001
CVLT-II 0.678 <0.001 0.743 <0.001 0.453 <0.001

HC group, healthy control group; SDMT, symbol digit modalities test; CVLT-II., california verbal learning test II.; BVMT-R, the brief visuospatial memory test revised (BVMT-R)

We evaluated the difference between the patients׳ and the HC group׳s test-retest scores. There are greater differences between the test and the retest mean scores in the HC group than between the patients׳ scores ( Table 1 ) and also the correlation between the tests and the retests are weaker within the HC group as was mentioned above ( Table 2 ). We found significant difference between the patients׳ and the HC group׳s test-retest performances with a multivariable test where the result of the BICAMS battery was the single composite outcome (p=0.020, Table 3 ). We also conducted multiple univariate tests sorting by the battery types and we found significant difference only in the CVLT-II performances (p=0.003, Table 4 ). Our model satisfied the assumption of sphericity (p>0.05).

Table 3 Multivariable test of the test–retest performances of the patients and HC group.

Within subject effect p Observed power
Test-Retest and Patient-HC group 0.020 * 0.790

Significant at the level of p<0.05.

HC group, healthy control group.

Table 4 Univariate tests of the test–retest performances of the patients and HC group by battery types.

Within subject effect Measure p Observed power
Test–retest and Patients-HC group SDMT written 0.063 0.460
BVMT-R 0.313 0.171
SDMT oral 0.231 0.223
CVLT-II 0.003 * 0.862

Significant at the level of p<0.05

HC group, healthy control group; SDMT, symbol digit modalities test; CVLT-II., california verbal learning test II.; BVMT-R, the brief visuospatial memory test revised (BVMT-R).

4.2. Correlations with fatigue and quality of life

We assessed the impact of subjective fatigue on the patients׳ cognitive status by examining the correlations between their scores during the BICAMS battery and the scores of the FIS battery.

We have found significant negative correlations (r<−0.3; p<0.05) between the patients׳ overall subjective fatigue scores and their cognitive performance in all parts of BICAMS, shown in Table 5 . Though we made the observation, that regarding the subscales, the decline in the cognitive functions measured by BICAMS correlated best with the physical dimension subscale of FIS, then with the social subscale; while considering the cognitive subscale of FIS we only found significant correlation with the oral SDMT and the CVLT-II performance.

Table 5 Correlations between the FIS battery and its subscales with the parts of the BICAMS battery.

  Total points Cognitive dimension Physical dimension Social dimension
r p r p r p r p
SDMT written −0.346 0.008 −0.248 0.052 −0.406 0.001 −0.293 0.023
BVMT-R −0.322 0.014 −0.247 0.053 −0.351 0.006 −0.255 0.049
SDMT oral −0.359 0.006 −0.279 0.028 −0.411 <0.001 −0.239 0.065
CVLT-II. −0.301 0.022 −0.311 0.014 −0.302 0.018 −0.316 0.014

Significant at the level of p<0.05

SDMT, symbol digit modalities test; CVLT-II., california verbal learning test II.; BVMT-R, the brief visuospatial memory test revised (BVMT-R).

Assessment of the impact of cognitive impairment on the patients׳ quality of life was done by examining the correlations between the performance in BICAMS battery and the scores in MSQoL-54.

Table 6 shows that cognitive impairment correlates significantly with 7 subsequent subscales: physical functioning, social and cognitive functioning, general health scale, overall quality of life and sexual performance and satisfaction with sexual performance.

Table 6 Correlations between performance in BICAMS battery and score in MSQoL-54 battery.

MSQoL-54 scale SDMT written BVMT-R SDMT oral CVLT-II
r p r p r p r p
Physical functioning 0.440 <0.001 0.279 0.025 0.427 0.001 0.252 0.045
Cognitive functioning 0.300 0.015 0.298 0.016 0.302 0.014 0.325 0.008
Social functioning 0.252 0.043 0.326 0.008 0.331 0.007 0.334 0.005
General health 0.448 <0.001 0.396 0.001 0.441 <0.001 0.375 0.002
Overall quality of life 0.251 0.045 0.273 0.029 0.280 0.025 0.239 0.057
Sexual functioning 0.445 <0.001 0.332 0.010 0.461 <0.001 0.402 0.002
Satisfaction with sexual functioning 0.391 0.002 0.337 0.009 0.402 0.002 0.376 0.003

SDMT, symbol digit modalities test; CVLT-II., california verbal learning test II.; BVMT-R, the brief visuospatial memory test revised (BVMT-R).

5. Discussion

Cognitive impairment is a frequent yet not routinely assessed symptom of multiple sclerosis ( Langdon, 2011 ). A special committee of neurologists and neuropsychologists has recommended the simple yet highly reliable BICAMS for assessing cognitive impairment in patients with MS ( Langdon et al., 2012 ). Our validation process found significant differences (p≤0.001 in all tests other than the first CVLT-II; p=0.017 in the first CVLT-II) between the patients׳ scores and the scores of the members of the HC group, also it has established strong correlations (r>0.67, p<0,001) when assessing the test-retest reliability. With this method we have shown that the Hungarian version of the BICAMS battery is just as valid as the original English ( Langdon et al., 2012 ) and recently validated Czech counterparts ( Dusankova et al., 2012 ).

We have also evaluated the possible reason behind the slightly weaker CVLT-II correlation and the differences between the test-retest scores and the correlation coefficients of the patients and the HC group. We have found that the members of the HC group performed significantly (p=0.020) better at the retest period comparing to their original performance than the patients did. This difference can seemingly solely be attributed to their CVLT-II (p=0.003) performances, though we found a borderline significance of the within-subject effect of the written SDMT scores (p=0.063) with a relatively lower observed power value (OP=0.460), which could very well mean that the lack of power is responsible for the lack of true significance. This could imply that the difference may be attributed partly to the SDMT performances as well, yet the only true significant difference was between the two groups CVLT-II scores. The reason can very well be the short interval between re-administering and the use of the same form of the CVLT-II both times (Benedict and Zgaljardic, 1998 and Benedict, 2005), hence the examinees remembered the words from the previous examination three weeks before. Another important factor can be the lack of novelty effect during the second administration, thus the anxiety of a new situation did not interfere with the performance of the examinees ( O׳Neil-Pirozzi et al., 2010 ). As the control group consisted of healthy individuals while more than half (52%) of the patients had cognitive impairment it is a possibility that – despite the relative better performance of the MS patients during the retest period comparing to their original scores – cognitive impairment prevented them from improving as much as the healthy individuals did, but this cannot be stated without further assessment.

Studies estimate the prevalence of cognitive impairment to be 43–70% among patients with MS (Chiaravalloti and DeLuca, 2008 and Langdon, 2011). Recently, Dusankova et al. found the prevalence of cognitive impairment in the Czech MS population to be 55% while validating the Czech version of MACFIMS and BICAMS ( Dusankova et al., 2012 ). As there was no consensus yet established, they have proposed the using of “one or more abnormal tests” for identifying cognitive deficit ( Dusankova et al., 2012 ). By using this proposed criterion, we have found that 52% of our patients had cognitive impairment–similarly to their findings and other studies in the literature (Chiaravalloti and DeLuca, 2008, Langdon, 2011, and Dusankova and Kalincik, 2012).

A relationship between fatigue and cognitive decline has been proposed, but there haven׳t been many studies dedicated to it. Patients often report that fatigue impairs their cognitive abilities ( Krupp et al., 1988 ), yet most of the studies dedicated to assess the relation between self-reported fatigue and objective cognitive performance have failed to find any significant correlation between the two (Johnson and Lange, 1997, Paul and Beatty, 1998, Bailey and Channon, 2007, Bol and Duits, 2010, and Morrow and Drake, 2010). However Andreasen et al. did report that fatigued MS patients׳ information processing speed was slower than non-fatigued patients׳( Andreasen et al., 2010 ). Our findings may imply similarly as Andreasen et al., that fatigue can have a negative impact on the patients׳ cognitive state: overall FIS points showed significant negative correlations with the patients׳ BICAMS performance (r<−0.3; p<0.05), and the strongest correlation was shown with the SDMT performances of the patients. Yet to draw any clear conclusions, this should be investigated further on a larger population of patients by multivariable models as age, disability, disease duration and other factors could be (at least partially) responsible for the correlation. Regarding the subscales of FIS, the physical subscale showed the strongest correlation with cognitive status and the cognitive subscale only showed significant correlation with the oral version of SDMT and the CVLT-II tests. This could imply that the physical aspect of fatigue could be primarily responsible for the worsening, but as said above, clear conclusions cannot be drawn without further investigations. As was mentioned, it is important that our patient pool was comprised of only 65 patients, so evaluation on a larger population might yield different results. Also we only assessed patients of the relapsing-remitting disease course and it has been suggested that cognitive impairment might be a corresponding marker of the progressive component of MS (Amato and Ponziani, 2001 and Lynch and Parmenter, 2005), so future evaluations should take this into consideration.

Multiple Sclerosis affects the patients׳ quality of life on several levels, and the impact of cognitive impairment on the patients׳ HRQoL is a highly important problem. As MS patients have a high prevalence of cognitive dysfunction, and it was shown that MS patients are more concerned about their mental status than their physical disability, the correlation between their cognitive performance and their self-reported quality of life becomes a very important question. Several studies found that the decline in cognitive status worsens the patients QoL (Mitchell and Benito-Leon, 2005, Turpin and Carroll, 2007, and Glanz and Degano, 2012). Benito-León et al. reported that during their evaluation, QoL significantly correlated with cognitive status, depression and anxiety – which means that the worse the cognitive performance is, the lower the patient׳s QoL is ( Benito-Leon et al., 2002 ). Interestingly however during the COGIMUS study, they only found significant difference between cognitively impaired and cognitively healthy MS patients in two subscales of MSQoL-54 and the cognitively impaired patients reported better QoL ( Patti et al., 2011 ). Also Kenealy et al. observed that MS patients with impaired autobiographical memory reported significantly better HRQoL ( Kenealy et al., 2000 ). However it is known that high cognitive reserve (which was the case in COGIMUS as main IQ was over 102) protects from cognitive decline and may suggest better coping mechanisms ( Patti et al., 2007 ) which can lead to better perception of QoL. Kenealy et al. concluded that patients with impaired autobiographical memory may not be able to validly compare their past QoL to the present which can resulted in these findings.

Our findings yielded similar results as the studies mentioned earlier (Benito-Leon and Morales, 2002, Mitchell and Benito-Leon, 2005, Turpin and Carroll, 2007, and Glanz and Degano, 2012). Seven subscales of MSQoL-54 showed significant correlation with scores on the BICAMS battery–cognitive, physical and social functioning subscales, overall quality of life and general health subscales, the sexual function and the satisfaction with sexual function subscales showed significant r levels. This means that cognitive impairment may be responsible for a serious negative impact on the patients׳ QoL and not just in some aspects of life, but in most. Though, as well as fatigue׳s impact on cognition, this should be evaluated on a larger patient pool and more variables (demographic, co-morbidity etc.) should be considered.

As a conclusion we can state that the Hungarian version of BICAMS, which is the third version to be validated in Europe, is a short, easily administered yet highly sensitive and specific tool for clinical evaluation of the cognitive impairment in MS. Also our findings suggest that fatigue can have a negative impact on the patients׳ cognitive state and that the decline in cognition could damage the patients׳ quality of life. Therefore the assessment and the follow-up of the patients׳ cognitive status should be as much a priority as the evaluation of somatic manifestations of the disease.

Conflict of interests

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.

We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.

We further confirm that any aspect of the work covered in this manuscript that has involved either experimental animals or human patients has been conducted with the ethical approval of all relevant bodies and that such approvals are acknowledged within the manuscript.

Acknowledgments

This study was sponsored by the Hungary–Serbia IPA Cross-border Co-operation Program (Szerb-Magyar IPA Határon Átnyúló Együttműködési Program) (HUSRB/1002/214/082). We want to express our thanks to the Foundation For The Neurology In Szeged (Szegedi Neurológiáért Alapítvány) for its financial support in buying the tests. Also we want to express our gratitude to the BIOGEN IDEC corp. for the financial support it lent to the Foundation For The Neurology In Szeged. And last, but not least we would like to thank all the patients and all the members of the healthy control group for their co-operation in the validation process.

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Footnotes

a Department of Neurology, Faculty of General Medicine, Albert Szent-Györgyi Clinical Centre, University of Szeged, Szeged, Hungary

b Department of Psychiatry, Faculty of General Medicine, Albert Szent-Györgyi Clinical Centre, University of Szeged, Szeged, Hungary

c Royal Holloway, University of London, Surrey, UK

d MTA-SZTE Neuroscience Research Group, Szeged, Hungary

Corresponding author. Tel.: +36 62 545 356; fax: +36 62 545 597.


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