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Validation of BICAMS in a Danish MS population with early MS.
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CVLT-R, SDMT and BVMT-R were significant in pwMS compared to controls.
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The Danish translation of BICAMS was a reliable cognitive assessment tool.
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Self-reported cognition, fatigue, depression and quality of life was assessed.
Abstract
Background
Cognitive impairment is highly prevalent in multiple sclerosis (MS). Due to the lack of specialized neuropsychological resources in many MS clinics, a brief cognitive monitoring tool that can be administered by other MS clinic staff is needed. BICAMS (Brief International Cognitive As-sessment for Multiple Sclerosis) has been developed and recommended by MS experts to monitor MS-related cognitive impairment. International validations of the tool are warranted.
Objective
The primary aim of the study was to establish a Danish translation of BICAMS as a feasible cognitive monitoring tool and to provide a Danish contribution to the international validation of BI-CAMS. A secondary aim was to determine if BICAMS correlated with self-reported cognition. The study population comprised people with MS (pwMS) with relatively early MS and newly diagnosed.
Methods
65 pwMS were compared to healthy controls (HCs) matched on age, sex and education. PwMS and controls completed the BICAMS test battery which includes the Symbol Digit Modalities Test (SDMT, oral version), California Verbal Learning Test-II (CVLT-II) and the Brief Visuospatial Memory Test-Revised (BVMT-R). In addition, self-reported cognition, fatigue, depression and quality of life were assessed. To evaluate the reliability of the BICAMS test, all participants were retested 2–3 weeks later with alternate versions of the tests.
Results
Mean age of the MS group was 37.2 years; 63% were female and all pwMS had a relapsing-remitting disease course. MS disease duration was relatively short; mean disease duration was 3.9 years and 32 of 65 pwMS (49%) were newly diagnosed with MS, i.e. diagnosed within the last 2 years. Mean EDSS was 1.8 with a span from 0–4. Comparison of the groups showed that the MS group performed significantly below the control group on the 3 BICAMS measures: SDMT (p<0.005), CVLT-II (p<0.05) and BVMT-R (p<0.05). When the results were controlled for influence from depression and fatigue by regression analysis, group differences were limited to the SDMT (p<0.05) and the BVMT-R (p<0.05) and these group differences were only found at the retest session. The BICAMS measures were reliable over time (r = 0.90 for SDMT, r = 0.82 for CVLT-II and r = 0.68 for BVMT-R). 32.3% of the MS population was cognitively impaired on at least one of the 3 BICAMS tests (defined as -1.5 SD compared to HCs). In the MS group 20% were impaired on the SDMT; 16.9% were impaired on the BVMT-R and 10.7% were impaired on the CVLT-II. There was no relationship between BICAMS test-scores and subjectively reported cognition, fatigue or depression.
Conclusion
The Danish translation of BICAMS was a reliable and feasible cognitive assessment tool. This finding was confirmed even in an MS population characterized by relatively early MS and high cognitive reserve. Frequency of cognitive dysfunction detected by BICAMS in this study was relatively low due to population characteristics.
). MS is associated with a range of physical, psy-chiatric and cognitive symptoms. A large proportion of people with MS (pwMS) (40–75%) have cognitive im-pairment, primarily reduced information processing speed, memory and executive functions, and these symptoms may appear already in the early course of the disease (
). Cognitive impairment in MS is often subtle and is not necessarily detected in routine clinical consultations. Self-reporting of cognitive functioning is confounded by other fac-tors such as depression. Hence, objective assessment of cognitive functions in MS is needed.
A full neuropsychological assessment of cognitive functions is time-consuming and requires expert personnel and specialized equipment, which may not be available at many MS clinics. This in turn poses an obstacle for the implementation in routine clinical practice and, consequently, there is an inherent risk of cognitive impairment being underdiagnosed in pwMS.
The BICAMS is a set of three tests selected by a consensus panel of MS experts as part of an interna-tional initiative to develop a brief cognitive monitoring tool with adequate reliability, validity, sensitivity and specificity to assess cognitive impairment in MS (
). The BICAMS consists of the oral ver-sion of the Symbol Digit Modalities Test (SDMT), the California Verbal Learning Test-II (CVLT-II) and the Brief Visuospatial Memory Test Revised (BVMT-R), these can be performed by health care personal without any formal neuropsychological training (e.g. MS nurses). It takes approximately 15 min to conduct and, as such, the BICAMS represents a useful tool for implementation in routine clinical practice to detect early and unrecognized cognitive impairment in MS.
It has been emphasized that cultural norms of specific populations are markedly different, and that this might interfere with the reliability of cognitive testing. Hence there is a need for validation studies in individual countries. To facilitate international implementation of BICAMS, validation recommendations have been proposed to ensure the reliability and validity of the test battery across different countries (
). Currently, the BICAMS has been validated in several countries according to these recom-mendations, and according to a recent systematic review and meta-analysis, BICAMS has up till now been validated across 11 languages and in 14 countries (
The objective of the present study was to validate BICAMS in a Danish population of pwMS. A popula-tion of pwMS with relapsing-remitting multiple sclerosis (RRMS) was selected to ensure homogeneity of studied group. Furthermore, persons with relatively short disease duration were selected for the study to address BICAMS sensitivity in the early stages of the disease. A secondary objective of the study was to evaluate self-reported cognition vs. BICAMS measures. The validation of BICAMS in the Danish patient popu-lation followed the international standards for validation (
This was an open-label study conducted at a single centre (the Danish Multiple Sclerosis Center (DMSC) at Copenhagen University Hospital Rigshospitalet) to validate BICAMS.
People diagnosed with RRMS as defined by the 2010 revised McDonald criteria (
) and attending the DMSC or other Danish MS clinics in the period between Q2 2018 to Q4 2018 were asked to participate in the study. Both newly diagnosed, treatment naïve pwMS and pwMS who had previously received treatment for MS were eligible for the study. All pwMS were on first- or second-line treatment. The inclusion criteria were: Expanded Disability Status Scale (EDSS) score of 0 – 5.0 (both inclusive) at screening; no evidence of relapse within 30 days prior to enrolment; 18–59 years of age and fluent in Danish. The exclusion criteria were: neurological, psychiatric or medical conditions besides MS and depression that might affect cognition; a history of learning disabilities; seizures; uncorrected visual acuity problems; corticosteroid treatment within two months of enrolment; current MS exacerbation; current use of legal or illegal drugs that could impact cognitive functions.
A matching group of healthy controls (HCs) with similar age (±2 years), gender and education were re-cruited. All participants gave written informed consent before entering the study.
To asses test-retest reliability, all participants completed the BICAMS at baseline and were retested with alternate versions of the tests after 1–3 weeks. The study was done in full accordance with the Declara-tion of Helsinki as adopted by the World Medical Association as well as Danish laws and regulations.
2.2 Assessment
2.1.1 Baseline assessments
At baseline, subjects’ eligibility to participate in the study was evaluated and informed consent was obtained. Demographic data were then collected, including age, gender, disease duration, years of educa-tion and employment status. EDSS was registered from the latest neurological assessment. Furthermore, timed 25-foot walk (T25-FW), 9-hole peg test and low contrast visual acuity test were assessed and noted for each participant.
2.2.1 Brief international cognitive assessment for multiple sclerosis (BICAMS)
The BICAMS consists of a set of three individual tests: the SDMT (oral version), CVLT-II and BVMT-R tests (
). The SDMT is designed to evaluate information processing speed. The CVLT-II assesses verbal learning and memory. In the CVLT-II, the examiner reads out a list of a total of sixteen words within four different semantic categories. The BVMT-R assesses visuospatial memory. In the BVMT-R, the subject is presented with a piece of paper with six abstract symbols for 10 s. The paper is then removed, and the subject is asked to replicate the six symbols on a blank piece of paper. The test is repeated in three trials.
In accordance with the BICAMS validation protocol (
Following collection of demographic data and other baseline assessments, the BICAMS was conducted. 1–3 weeks after the initial test all participants were retested in a follow-up session. In the follow-up sessions alternate test forms were used to reduce practice effects. The instruction and administration of the BICAMS test and retest was done by the same neuropsychologist or neuropsychologist student during the entire study.
In addition to BICAMS, questionnaires were applied to measure self-reported cognition, fatigue, de-pression, and quality of life. Self-reported cognition was assessed with the Multiple Sclerosis Neuropsycho-logical Questionnaire (MSNQ), which is a validated measure of subjective cognitive deficits related to MS (
All statistical tests were performed using a two-sided test at a 5% significance level. Results from the analyses are presented with point estimates, 95% confidence intervals and p-values. Data from all subjects (pwMS and HCs) were included in the statistical analyses and no imputation of missing data was made.
For the comparison of categorical variables between the MS and HC group, chi-square and Fisher's ex-act test were applied, and for continuous variables Students t-test or Wilcoxon test were applied to test for group differences. Furthermore, for continuous variables, the effect size was calculated using Cohen's d sta-tistic (
). To evaluate the relationship between assessments at test and retest, Pearson or Spearman correlation coefficients were calculated.
The distribution of BICAMS test data predicting cognitive impairment (SDMT, CVLT-II and BVLT-R) was visualized by group with histograms and QQ-plots. Based on these plots the raw test scores were scaled scores (M = 10; SD = 3) using the cumulative frequency distribution of each measure (Bland et al., 1986;
). For the BICAMS tests, raw or scaled scores regression models were applied on the control group with the test result as response variable, age, age2, BDI score and FSMC-total as continuous explanatory variables, and sex and educational level as categorical explanatory variables. The estimated regression models from the control group were used to calculate predicted values for the MS group, and the predicted values were subtracted from the observed test results of the pwMS group.
Receiver operating curves (ROC) were plotted to illustrate the predictive ability of the different BI-CAMS tests. The confidence limits were estimated using a bootstrap approach (
Demographic data for the entire study population are presented in table 1. The HC group and the MS group did not differ in age, gender or education. Mean age was 37.2 years in the MS group and 36.8 years in the control-group. For both groups, 63% were females. Mean years of education (YOE) was 15.2 years in the MS group and 15.9 years in the control group. All pwMS had a RRMS disease course. In the MS group, the mean EDSS score was 1.8 and the range was 0–4. Average disease duration was 3.9 years.
Table 1Demographic and clinical profiles of the population at baseline.
Demographic parameter
MS
HC
Number of subjects, N (female/male)
65 (41/24)
65 (41/24)
Age, years (SD, range)
37.2 (8.8, 19–56)
36.8 (9.6, 18–56)
Years of education, years (SD, range)
15.2 (2.4, 10–20)
15.9 (2.1, 10–20)
EDSS (SD, range)
1.8 (1.2, 0–4)
NA
Disease duration, years (SD, range)
3.9 (2.7, 1–10)
NA
MSQN-S
20.5 (13.3)
11.2 (5.5)
FSMC
53.6 (22.2)
24.5 (4.9)
BDI-II
7.8 (7.4)
3.3 (3.5)
MSIS-29
51.3 (20.0)
NA
25FWT (seconds)
3.5 (0.5)
4.1 (1.6)
9-HPT (seconds)
17.0 (2.0)
18.9 (5.3)
LCVA (right eye)
25.0 (7.0)
25.2 (9.8)
Abbreviations: EDSS = Expanded Disability Scale Score; HC = healthy control; MS = multiple sclerosis; SD = standard deviation; NA = not applicable; MSQN-S = Multiple Sclerosis Neuropsychological Questionnaire – Self form; FSMC = Fatigue Scale for Motor and Cognitive Functions; BDI-II = Beck Depression Inventory-II; MSIS-29 = Multiple Sclerosis Impact Scale; 25FWT = timed 25-foot walk; 9-HPT = 9-Hole Peg Test; LCVA = Low contrast visual acuity.
3.2.1 BICAMS test scores at baseline and follow-up
For each of the BICAMS tests, the parameter estimates, corresponding standard errors and p-values from the regression analyses are tabulated (table 2). BICAMS test score values are shown in table 2. PwMS scored significantly lower than the control group on SDMT (p<0.005), CVLT-II (p<0.05) and BVMT-R (p<0.05) at the first visit. When data were analysed with regression analysis controlling for the effects of depression and fatigue, group differences were limited to the SDMT (p<0.05) and the BVMT-R (p<0.05) and these group differences were only found at the re-test session (table 3). The effect sizes of the BICAMS tests were small to medium at baseline and at follow-up; at baseline a medium effect size was found for the SDMT (d = 0.51), a small to medium effect size was found for the BVMT-T (d = 0.45), and a small effect size was found for the CVLT-II (d = 0.38) (table 4). Cognitive impairment has previously been defined as a score 1.5 standard devia-tions below the average on one or more BICAMS tests. Using this criterion 21 out of 65 (32.3%) of the pwMS were cognitively impaired at baseline whereas 20% in the HC group were cognitively impaired (Fig. 1). In the patient group, 14 out of 65 (21.5%) were impaired on one of the BICAMS tests, 4 out of 65 pwMS were impaired on two BICAMS tests (6.2%) and 3 out of 65 pwMS (4.6%) were impaired on all three BICAMS tests. Most pwMS were impaired on the SDMT (20%); 16.9% were impaired on the BVMT-R and 10.7% were impaired on the CVLT-II (Fig. 1).
Table 2BICAMS scores at baseline and follow up.
HCs
MS
P val-ue
SDMT
Baseline
66.0 (9.6)
61.0 (10.0)
< 0.005
Follow up
66.3 (9.1)
60.5 (10.0)
< 0.001
CVLT-II
Baseline
68.6 (6.4)
65.4 (9.9)
< 0.05
Follow up
69.6 (5.7)
67.7 (9.0)
= 0.154
BVMT-R
Baseline
29.6 (3.7)
27.4 (5.8)
< 0.05
Follow up
30.5 (3.6)
28.9 (5.0)
< 0.05
Abbreviations: MS = multiple sclerosis; HC = healthy control; SDMT = Symbol Digit Modalities Test; CVLT-II = California Verbal Learning Test-II; BVMT-R = Brief Visuospatial Memory Test-Revised. Numbers in parentheses denote standard deviation.
Fig. 1N = 130. Frequencies in% of impaired (z = < −1.5) patients with multiple sclerosis (MS) and healthy controls (HCs) by mean scores at baseline for each test (SDMT, CVLT-II, BVMT-R) and total BICAMS (one or more test are impaired).
By using a BICAMS test result of 1.5 SD below the mean as a criteria of impairment, the SDMT classi-fied 20% of pwMS and was the most sensitive test to measure cognitive dysfunction in the population. The CVLT-II and the BVMT-R correctly classified 10.8% and 16.9% of participants, respectively. The sensitivity was relatively low, but specificity was high (see table 5).
Table 5Sensitivity and specificity of BICAMS test using recommended cut-off of 1.5 standard deviation.
BICAMS test
AUC
Sensitivity
Specificity
SDMT
0.585
20.0%
95.4%
CVLT-II
0.569
10.8%
89.2%
BVMT-R
0.585
16.9%
93.8%
SDMT – Symbol Digit Modalities Test; CVLT-II – California Verbal Learning Test-II; BVMT-R-Brief Visuospatial Memory Test-Revised; AUC-Area under the curve.
There was no difference in BICAMS administration time between the two groups (p = 0.711). Mean administration time in the MS group was 17 min, ranging from 11–25 min. Thus, in both groups, the time consumption was within the predicted range of 15–20 min.
3.2.2 Reliability
The test-retest reliability data for the MS group are presented in table 3. All three BICAMS-tests demonstrated reliability over time. The SDMT showed the strongest correlation.
Strength of correlations were modest to strong (range from 0.90 for the SDMT to 0.68 for the BVMT-R).
Practice effects were found between baseline assessments and follow-up for the CVLT-II and the BVMT-R in the MS group despite the use of alternate test forms. In the HC group a close to significant prac-tice effect was found for the BVMT-R only. No practice effects were found for the SDMT.
3.3 Relationship between bicams measures and subjective cognition
Self-reported cognition in the MS group as assessed by the MSNQ did not correlate with any of the BI-CAMS measures (r ranging from −0.18 to −0.21). Self-reported cognition did, however, correlate moderately with depression at the baseline assessment (r = 0.54, p<0.01).
3.4 Relationship between bicams measures and depression, fatigue and quality of life
BICAMS measures did not correlate with depression in the MS-group as measured by the BDI (SDMT: r = −0.18, p< 0.01; CVLT-II: r = −0.04, p = 0.75; BVMT-R: r = −0.075, p = 0.55). Furthermore, BI-CAMS scores did not correlate with fatigue in the MS group. In the MS group a measure of QOL (MSIS-29) correlated with the SDMT-score (r = −0.34, p<0.01), while no correlations were found for the CVLT-II or the BVMT-R.
4. Discussion
The BICAMS has been suggested by MS experts as an assessment and monitoring tool for cognition in MS, i.e. a tool that reliably identifies the typical cognitive deficits associated with MS. Specific purposes of the tool are monitoring of cognition over time, feasibility in the clinics by healthcare professionals without neuropsychological test experience and quick administration time (15–20 min). BICAMS as a measure to reliably evaluate cognitive impairment in MS has so far been validated in several languages and countries (
) and is the first to publish Danish validation data. The study aimed to evaluate the BICAMS in a Danish population, and to study whether BICAMS discriminated between pwMS and healthy controls, as well as whether BICAMS demonstrated test-retest reliability.
Regarding the practical purposes of BICAMS, the expected BICAMS administration time of 15–20 min was confirmed with administration times ranging from 11 to 25 min in the MS-group. Thus, our data confirm the feasibility of BICAMS as a quick monitoring tool in the clinic.
The study population was characterized by relatively early MS, exclusively RRMS disease course and a low burden of disease as measured by EDSS (mean 1.8). Consequently, there was only a small difference in BICAMS test performances between pwMS and controls. EDSS and YOE both had a significant impact on BICAMS test results as confirmed in other studies where lower levels of education predict cognitive function in pwMS (
). BICAMS was able to identify cognitive impairment in 32.3% of the MS-sample using the criteria of impairment defined by one or more abnormal tests. This result is comparable to 32.6% in the German validation study (
). The finding of cognitive impairment even in early stages of MS is comparable to other studies addressing the prevalence of cognitive impairment in early stages of MS (
). Other BICAMS validation studies have found somewhat higher frequencies of cognitive impairment: For example, 57.9% in the Canadian validation study (
) and 52% in the Hungarian study (Sandi et al., 2015). The difference in frequencies be-tween our study and other studies are likely attributable to differences in population characteristics, since prevalence rates of cognitive impairment in MS are highly dependent on the patient group studied. It is of significance that different disease courses have been associated with different cognitive profiles (Ruet et al., 2013). Furthermore, the population was characterized by a relatively high educational level compared to other studies (
; Niccolai et al., 2015; Spedo et al., 2015). Thus, cognitive reserve might also have been a factor influencing the results. Regarding representativeness of the MS population and inclusion procedures in the present study, the pwMS were not selected for the study but were recruited via adver-tisement. Thus, pwMS with more pronounced cognitive impairment might not have entered the study. The present study controlled for both depression (BDI) and fatigue (FSMC) in the regression analysis which may result in lower impairment frequencies. After accounting for depression and fatigue, only the SDMT and the BVMT-R were able to discriminate between the MS group and the control group, and this was only the case at the follow-up session. The CVLT-II did not discriminate between groups at any time point. Similar findings have been reported in the Hungarian study (Sandi et al., 2015), the Canadian study (
). It has been suggested that the lack of discrimination between groups on the verbal memory measure may be related to higher education level/cognitive reserve. However, in our study a regression analysis revealed that YOE did not correlate with the CVLT-II outcome. An alternative explanatory factor could be a difference in the distribution of memory impairment in MS populations between validation studies. Changes in MS cognition over time may be in memory and executive functions, hence disease duration and the number of included pwMS with progres-sive MS in the validation studies could matter.
As in other BICAMS validation studies, no correlation was found between BICAMS measures and sub-jectively reported cognition. Subjectively reported cognitive complaints, typically in the memory domain, often co-occur with depression, in the absence of objective cognitive deficits (
). Thus, the findings support the need for objective measures of cognition in the clinic. It could be argued that depres-sion is an important factor to be considered when monitoring cognitive functions in MS. Depression is a highly prevalent symptom in the MS population with life time prevalence rates as high as 50% (
). In this study we did not make an a priori correction for depression, which is an important limitation of the study. However, correlation analyses did not show any association between depression and cognition.
It is of note that using the suggested criterion for cognitive impairment of 1.5 standard deviations be-low the mean, as much as 20% of the healthy controls were also characterized as cognitively impaired. While the BICAMS tests identified a statistically significant effect at the group level, the effect sizes were small to medium. Thus, the precision of the tests at the individual level is less than perfect. Hence, for the clinical use of BICAMS national norms based on a larger representative population are warranted to evalu-ate individual performances. Furthermore, normalization using linear regression modelling to control for demographic effects on test performance, i.e. a regression-based norms approach, is relevant.
Evaluation of test-rest reliability of the three BICAMS measures confirmed good reliability over time for the SDMT (r = 0.90) and the CVLT (r = 0.82), while an acceptable reliability was found for the BVMT-T (r = 0.68). Reliability in this study was evaluated over a period of 1–3 weeks. Alternate test forms were applied at baseline and follow-up to reduce practice effects. Despite the use of alternate forms some practice effects were observed. This finding is important when using BI-CAMS as a monitoring tool over time, since improvement between sessions cannot necessarily be attributed to actual improvement over time. If scores decline between sessions, a true decline might be plausible, but a finding of no change between sessions also might be attributed to a true decline. Regarding the clinical implementation of BICAMS, it remains to be decided what is a reliable change between assessments, and control procedures for test-retest artefacts may be relevant.
5. Conclusion
Data from this Danish validation study support the suggestion of BICAMS as a highly effective tool for detection of cognitive impairment in pwMS and for reliable monitoring of cognitive performance over time. Furthermore, data support BICAMS’ feasibility in a clinical setting. This finding was confirmed even in a MS study population with early MS, low burden of disease measured by EDSS and an apparently high cognitive reserve. Results were comparable to those reported in studies from other cultures when accounting for population differences regarding factors such as disease duration, MS course, degree of impairment and educational level. The relatively comparable results and equivalent frequencies across studies and countries support the international applicability of BICAMS.
Role of funding source
This study was financially supported by Novartis Healthcare A/S Denmark and the Danish Multiple Sclerosis Society.
Disclosures:
Lisbet Marstrand has served on scientific advisory boards, served as a consultant, received support for congress participation, received speaker honoraria from Biogen and Novartis.
Ole Østerberg is an employee of LEO Pharma.
Trine Walsted have nothing to disclose
Amalie C. Skov have nothing to disclose.
Karen I. Schreiber has served on scientific advisory boards, been on the steering committees of clinical trials, served as a consultant, received support for congress participation, received speaker honoraria from Biogen, Merck, Novartis, Roche, Sanofi Genzyme and Teva.
Finn Sellebjerg has served on scientific advisory boards, been on the steering committees of clinical trials, served as a consultant, received support for congress participation, received speaker honoraria, or received research support for his laboratory from Biogen, Merck, Novartis, Roche, Sanofi Genzyme and Teva.
Declaration of Competing Interest
None
Acknowledgements
This study was financially supported by Novartis and the Danish Multiple Sclerosis Society. The authors would like to thank all participating persons in the study. We also thank Dr. Dawn Langdon for her advice and support during the study.
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