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Original article
Cognitive impairment among patients with multiple sclerosis: associations with employment and quality of life
  1. J Campbell1,
  2. W Rashid2,
  3. M Cercignani1,
  4. D Langdon3
  1. 1Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Falmer, UK
  2. 2Department of Neurology, Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
  3. 3Department of Neuropsychology, Royal Holloway, University of London, Egham, Surrey, UK
  1. Correspondence to Dr J Campbell, Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Falmer BN1 9RH, UK; jamie.campbell{at}belfasttrust.hscni.net

Abstract

Objectives To explore the relationship between cognitive impairment and conventional measures of disability in multiple sclerosis (MS), quality of life (QOL) and employment status using the brief international cognitive assessment for multiple sclerosis (BICAMS) in the routine outpatient clinic.

Methods 62 patients with MS were assessed on the BICAMS test battery for cognitive impairment. Data were obtained on employment status and a number of questionnaires completed including fatigue severity score, multiple sclerosis neuropsychological questionnaire, hospital anxiety and depression scale, the functional assessment of multiple sclerosis (FAMS) as well as on the EuroQOL five dimension questionnaire (EQ-5D). Other assessments include the patient activation measure and unidimensional self-efficacy scale for multiple sclerosis.

Results Cognitive assessment revealed 44 subjects (65%) had evidence of cognitive impairment on formal testing. In comparison with patients without evidence of cognitive impairment, cognitively impaired patients exhibited significantly higher rates of unemployment (p=0.009). The symbol digits modalities test was the most significant predictor of unemployment. Cognitive impairment was associated with lower QOL scores on the FAMS (p=0.001) and EQ-5D (p<0.001).

Conclusions BICAMS provides a sensitive and easy to administer screening test for cognitive impairment within the outpatient setting. Cognitive impairment is common in our cohort of patients with MS attending outpatients and appears to be associated with increased rates of unemployment and lower measures of QOL.

  • NEUROLOGY

http://dx.doi.org/10.1136/postgradmedj-2016-134071

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    Introduction

    Multiple sclerosis (MS) is a common inflammatory condition affecting the central nervous system and is the most common cause of acquired, non-traumatic disability in young adults.1 As well as the physical disability associated with MS, there exist significant non-motor manifestations. Rates of depression and cognitive dysfunction are higher among patients with MS than the general population.2 Of these, cognitive dysfunction is particularly under-recognised and currently presents limited opportunity for intervention or treatment. Studies have revealed that cognitive impairment is present in 40–65% of individuals with clinically definite MS on neuropsychological testing.2 Cognitive impairment has been identified at all stages and affects all subtypes of MS. Studies have shown cognitive deficits (in particular, deficits in information processing speed, concentration and working memory) to be present in the early stages of MS including clinically isolated syndrome.3 ,4 The presence of cognitive impairment may therefore provide a measure of diffuse brain pathology early in the course of MS.

    Cognitive impairment has a broad negative impact on the lives of people with MS independent of physical symptoms.5 Perhaps the most severe and far-reaching effect is unemployment, which results in extensive personal, social and financial costs. Cognition has been linked to unemployment in many countries.6 ,7 Cognition has been shown to mediate the effects of disability on employment status.8 Within 10 years of diagnosis, about 50% of people with MS are unemployed.9

    It is clearly important to identify people with MS who may be at risk of unemployment so that appropriate management strategies can be put in place.10 There are a number of cognitive test batteries that can be used to identify cognitive impairment in MS.11 ,12 The brief international cognitive assessment for multiple sclerosis (BICAMS) is a brief (15 min) screening tool for healthcare professionals to identify cognitive impairment in MS.13 BICAMS comprises the first five learning trials of the California verbal learning test II (CVLT-II), the first three recall trials brief visuospatial memory test revised (BVMT-R) and the symbol digits modalities test (SDMT).14

    This study was designed to explore the utility of BICAMS in the outpatient clinic to identify cognitive impairment and examine how cognitive impairment relates to disability, employment and quality of life (QOL) measures in our patient population. It provides the first UK data on the application of BICAMS in relation to many of these measures including employment status.

    Subjects and methods

    Participants

    Sixty-two patients (43 females, 19 males) were included in the study between February 2014 and February 2015. Participants were recruited through outpatient clinics while attending for routine neurology outpatient appointments at Brighton and Sussex University Hospitals NHS Trust. Those with cognitive impairment went on to participate in a study cognitive rehabilitation. We report here the baseline data of all patients screened.

    All participants signed informed written consent before undergoing testing. The study was approved by the Northern Ireland Research Ethics Committee. Inclusion criteria were as follows: (a) age between 18 and 65 and (b) clinically definite MS, according to the McDonald criteria.15 All participants underwent a detailed neurological assessment including Kurtzke expanded disability status scale (EDSS)16 scoring by an EDSS-certified neurologist (http://www.neurostatus.net).

    Patients were excluded if they had a history of significant psychiatric disorders, alcohol or drug abuse, visual acuity <6/18 corrected, oscillopsia or diplopia that would interfere with testing. Patients were also excluded if they had an MS relapse, received corticosteroids or had changes made to antidepressants or psychoactive medications within the previous month.

    Cognitive and behavioural assessment

    Participants underwent neuropsychological assessment and were defined as having cognitive impairment if they scored below the 5th centile for normative data (adjusted for age, sex and years of formal education) on one or more of the BICAMS tests.17

    Patients also completed the following assessments: EuroQOL five dimension questionnaire, a generic health-related QOL scale,18 functional assessment of multiple sclerosis (FAMS) (an MS-specific QOL scale),19 patient activation measure (a 13-item generic scale for chronic illness management) and a measure of patient empowerment in MS,20 unidimensional self-efficacy scale for multiple sclerosis,21 the hospital anxiety and depression scale,22 multiple sclerosis neuropsychological questionnaire (MSNQ) (a patient self-reported measure of cognitive function),23 and the fatigue severity scale.24

    Statistical analysis

    Descriptive statistics for normally distributed continuous variables are expressed as mean and SD. Skewed continuous variables were summarised using median and IQR. Categorical variables are summarised by frequencies and percentages.

    Those patients with cognitive impairment were compared with those without cognitive impairment across a number of variables. Normality of continuous variables was assessed using the Kolmogorov-Smirnov test.

    Categorical variables were compared by the Pearson χ2 test. The means of continuous variables were compared using the independent-samples t-test or the Mann-Whitney U test for skewed data. All tests were two-tailed; p values <0.05 were considered significant. Analyses were performed using SPSS V.21 (IBM, Armonk, New York, USA).

    Results

    Demographic and cognitive outcomes

    Six-two patients underwent clinical and neuropsychological assessment. Forty patients (65%) showed evidence of cognitive impairment, as defined as scoring below the 5th centile for normative data on one or more of the BICAMS scales.17

    At entry, 44 patients (71%) had relapsing remitting MS and 18 patients (29%) had secondary progressive MS. Participants were aged between 31 and 63 years of age. The mean (SD) age of participants was 49.35 (8.88) years, range was 31–63 years. The mean (SD) duration of MS from diagnosis to enrolment was 12 (8.0), range 1–40 years. Median EDSS was 4.0 (range 1.0–6.5). Thirty-one participants (50%) were on disease-modifying therapy (DMT) at enrolment (natalizumab n=7, β-interferon n=9, glatiramer acetate n=2, fingolimod n=11 and teriflunomide n=2).

    Among the cognitively impaired patients, 21 (53%) failed one test, 11 (28%) failed two tests and 8 (20%) failed all three tests of the BICAMS test battery. The most frequently failed exam was the SDMT with 56% of all patients scoring below the 5th centile, 29% failed the CVLT-II and 23% failed the BVMT-R.

    Baseline and demographic characteristics of the two groups (cognitively impaired vs non-cognitively impaired) are shown in table 1. Gender, age, education, duration of illness, treatment with DMT and EDSS did not differ significantly between the groups. Overall, 27 (44%) patients were in employment; however, patients with cognitive impairment on one or more tests were significantly more likely to be unemployed with an OR (95% CI) of 4.5 (1.5 to 13.6). Greater rates of unemployment were associated with increased number of tests failed (p=0.011) (see figure 1). The OR of being unemployed with patients failing on 1, 2 and 3 tests increased to 4.3, 5.7 and 6.4, respectively.

    View this table:
    Table 1

    Characteristics by group

    Figure 1
    Figure 1

    Brief international cognitive assessment for multiple sclerosis (BICAMS) performance and employment. Rates of employment versus number of tests failed showing increasing rates of unemployment the more BICAMS tests that are impaired.

    The SDMT score was found to be the most significant predictor of unemployment (table 2). The strength of association did not change significantly when adjusting for other variables.

    View this table:
    Table 2

    Univariate analysis of employment status versus cognitive impairment

    Rates of unemployment were seen to increase with lower performance on the SDMT (figure 2). When adjusting for potential confounding variables (age and years of formal education), the strength of association did not change significantly (p<0.01).

    Figure 2
    Figure 2

    Rates of employment with increasing performance on symbol digits modalities test (SDMT). SDMT score versus rate of employment showing increasing rates of employment with higher SDMT scores.

    QOL and behavioural data

    Patients with cognitive impairment had significantly lower FAMS scores (p=0.001) (indicating lower QOL), higher EuroQOL five dimension questionnaire (EQ-5D) scores (p<0.001) (indicating lower QOL) and higher MSNQ (p=0.004) scores in keeping with a greater subjective perception of cognitive impairment. QOL and behaviour outcome measures are shown in table 3. QOL measures were seen to improve with increasing performance on the SDMT (figure 3).

    View this table:
    Table 3

    Quality of life (QOL) and behavioural measures

    Figure 3
    Figure 3

    Relationship between symbol digits modalities test (SDMT) score and quality of life (QOL) measures. Graph showing increase mean (95% CI) functional assessment of multiple sclerosis (FAMS) score (indicating better QOL) with increasing SDMT performance.

    Discussion

    Cognitive impairment is common among patients with MS. The incidence of cognitive impairment in our cohort of patients with MS attending outpatients at 65% was similar to that previously reported elsewhere.2

    Rates of unemployment have been shown to increase over time in patients with MS and present significant cost implications to the individual and society.25 Various risk factors for unemployment in MS have been identified including low education level, extent of disability, progressive subtype and personality as well as cognitive impairment.7 ,26

    BICAMS provides a sensitive and easy-to-conduct means of identifying cognitive impairment in the outpatient setting and may provide a means for identification of patients with other, more complex psychosocial needs. This study provides the first UK data pertaining to the use of BICAMS in assessing employment status within a UK cohort. Factors linked to unemployment in our study were advanced age and disease duration, increased disability and cognitive impairment. Of these, the SDMT was the most significant predictor of unemployment. The strength of association did not change significantly when adjusting for other variables.

    Of the tests used within BICAMS, the SDMT may provide among the most sensitive measures of cognitive impairment in MS.27 Previous work examining cognitive impairment in relation to employment in MS had identified the SDMT to also be one of the main predictors of employment status;6 ,7 ,28 the CVLT-II has also predicted reduced employment status in a longitudinal study.7 Although simple strategies may overcome memory deficits, at least when they are mild, the ‘bandwidth’ limitations of reduced information processing speed are less easy to manage.

    Patients with MS have consistently been shown to have a reduced QOL with multiple factors related to the disease such as disability, pain, fatigue, personality, depression and unemployment likely to be implicated.28 ,29 Cognitive impairment has been shown to negatively impact upon QOL independent of physical disability5 and employment status has been more closely linked to QOL than EDSS.30

    In our study, there was a statistically significant difference in QOL between those patients with cognitive impairment compared with those without as measured on both the MS-specific QOL scale, (FAMS) as well as a generic QOL scale (EQ-5D). A better understanding of the specific cognitive deficits in MS has led to more sensitive patient-reported measures being developed in which relatively close correlation is seen between subjective measures of cognitive impairment and objective outcomes.23 Recent work has also suggested that patient-reported perceptions of cognitive difficulties may be predictive of unemployment status independent of mood and objective memory performance.6 This finding may reflect a relative insensitivity of standard tests to subtle cognitive impairments at a time when ‘subclinical’ cognitive impairment is beginning to cause difficulties with work.

    To explore this, we used the MSNQ, a patient self-reported measure perceived competency with activities of daily living and day-to-day cognitive tasks.23 Cognitively impaired patients had significantly lower MSNQ scores than patients without cognitive impairment.

    How best to manage cognitive impairment is, at present, far from clear. However, on a clinical basis, early identification of cognitive impairment allows identification of patients who are at risk of employment difficulties, which may enable support to be put in place that enables them to continue in their current employment with accommodations.10 This will extend their time in employment and potentially allow them to avoid having employment terminated for poor performance. Instead, individuals might achieve medical retirement, a better personal and financial outcome in most countries. More broadly, by identifying patients who may struggle with disease management, more optimal communication strategies and monitoring protocols can be adopted to reduce the morbidity associated with cognitive impairment.5

    Limitations of this current study are that it involved a relatively small cohort and was cross-sectional in nature. Through recruiting patients from a specialist clinic, there does exist the potential for selection bias (possibly reflecting more complex or patients with more highly active disease).

    Other limitations include the retrospective acquisition of the employment data, a lack of descriptive data pertaining to employment status such as full-time versus part-time employment and details pertaining to when an individual ceased to be in employment and how this relates to duration of illness. The type of employment (eg, professional vs manual occupations) may also be impacted upon differently by cognitive impairment. Future work might examine these factors in detail.

    Main messages

    • Cognitive impairment is common in patients with multiple sclerosis.

    • Cognitive impairment is associated with increased rates of unemployment.

    • Cognitive impairment is associated with lower measures of quality of life and may be partly independent of physical disability.

    Current research questions

    • What is the pathophysiology of cognitive impairment in multiple sclerosis?

    • What is the relationship between cognitive impairment and employment status in multiple sclerosis?

    • What are the optimum interventions to ameliorate the negative impact of cognitive impairment in multiple sclerosis?

    Acknowledgments

    The authors thank Stephen Bremner, Senior Statistician, Brighton and Sussex Medical School for his help with this manuscript.

    References

    1. ↵
      Mapping Multiple Sclerosis Around the World. Multiple Sclerois International Federation, 2013. http://www.msif.org/wpcontent/uploads/2014/09/Atlas-of-MS.pdf
    2. ↵
      1. Benedict RH,
      2. Zivadinov R
      . Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nat Rev Neurol 2011;7:332–42. doi:10.1038/nrneurol.2011.61
      OpenUrlCrossRefPubMed
    3. ↵
      1. Potagas C,
      2. Giogkaraki E,
      3. Koutsis G, et al
      . Cognitive impairment in different MS subtypes and clinically isolated syndromes. J Neurol Sci 2008;267:100–6. doi:10.1016/j.jns.2007.10.002
      OpenUrlCrossRefPubMedWeb of Science
    4. ↵
      1. Amato MP,
      2. Ponziani G,
      3. Pracucci G, et al
      . Cognitive impairment in early-onset multiple sclerosis. Pattern, predictors, and impact on everyday life in a 4-year follow-up. Arch Neurol 1995;52:168–72. doi:10.1001/archneur.1995.00540260072019
      OpenUrlCrossRefPubMedWeb of Science
    5. ↵
      1. Langdon DW
      . Cognition in multiple sclerosis. Curr Opin Neurol 2011;24:244–9. doi:10.1097/WCO.0b013e328346a43b
      OpenUrlCrossRefPubMed
    6. ↵
      1. Honan CA,
      2. Brown RF,
      3. Batchelor J
      . Perceived Cognitive Difficulties and Cognitive Test Performance as Predictors of Employment Outcomes in People with Multiple Sclerosis. J Int Neuropsychol Soc 2015:21:156–68. doi:10.1017/S1355617715000053
      OpenUrlCrossRefPubMed
    7. ↵
      1. Morrow SA,
      2. Drake A,
      3. Zivadinov R, et al
      . Predicting loss of employment over three years in multiple sclerosis: clinically meaningful cognitive decline. Clin Neuropsychol 2010;24:1131–45. doi:10.1080/13854046.2010.511272
      OpenUrlCrossRefPubMed
    8. ↵
      1. Cadden M,
      2. Merritt V,
      3. Ukueberuwa D, et al
      . B-59 Comprehensive Examination of Factors Contributing to Employment Status in Multiple Sclerosis. Arch Clin Neuropsychol 2014;29:559. doi:10.1093/arclin/acu038.147
      OpenUrlAbstract/FREE Full Text
    9. ↵
      1. Kobelt G,
      2. Berg J,
      3. Lindgren P, et al
      . Costs and quality of life of patients with multiple sclerosis in Europe. J Neurol Neurosurg Psychiatr 2006;77:918–26. doi:10.1136/jnnp.2006.090365
      OpenUrlAbstract/FREE Full Text
    10. ↵
      1. Doogan C,
      2. Playford ED
      . Supporting work for people with multiple sclerosis. Mult Scler 2014;20:646–50. doi:10.1177/1352458514523499
      OpenUrlAbstract/FREE Full Text
    11. ↵
      1. Benedict RH,
      2. Fischer JS,
      3. Archibald CJ, et al
      . Minimal neuropsychological assessment of MS patients: a consensus approach. Clin Neuropsychol 2002;16:381–97. doi:10.1076/clin.16.3.381.13859
      OpenUrlCrossRefPubMedWeb of Science
    12. ↵
      1. Rao S
      . A manual for the Brief Repeatable Battery of Neuropsychological Tests in multiple sclerosis. New York: National MS Society, 1991.
    13. ↵
      BICAMS. Brief International Cognitive Assessment for MS. 2013. https://wwwbicamsnet
    14. ↵
      1. Langdon DW,
      2. Amato MP,
      3. Boringa J, et al
      . Recommendations for a Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS). Mult Scler 2012;18:891–8. doi:10.1177/1352458511431076
      OpenUrlAbstract/FREE Full Text
    15. ↵
      1. McDonald WI,
      2. Compston A,
      3. Edan G, et al
      . Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 2001;50:121–7. doi:10.1002/ana.1032
      OpenUrlCrossRefPubMedWeb of Science
    16. ↵
      1. Kurtzke JF
      . Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983;33:1444–52. doi:10.1212/WNL.33.11.1444
      OpenUrlCrossRefPubMed
    17. ↵
      1. Parmenter BA,
      2. Testa SM,
      3. Schretlen DJ, et al
      . The utility of regression-based norms in interpreting the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). J Int Neuropsychol Soc 2010;16:6–16. doi:10.1017/S1355617709990750
      OpenUrlCrossRefPubMedWeb of Science
    18. ↵
      1. Rabin R,
      2. de Charro F
      . EQ-5D: a measure of health status from the EuroQol Group. Ann Med 2001;33:337–43. doi:10.3109/07853890109002087
      OpenUrlCrossRefPubMedWeb of Science
    19. ↵
      1. Cella DF,
      2. Dineen K,
      3. Arnason B, et al
      . Validation of the functional assessment of multiple sclerosis quality of life instrument. Neurology 1996;47:129–39. doi:10.1212/WNL.47.1.129
      OpenUrlCrossRefPubMed
    20. ↵
      1. Stepleman L,
      2. Rutter MC,
      3. Hibbard J, et al
      . Validation of the patient activation measure in a multiple sclerosis clinic sample and implications for care. Disabil Rehabil 2010;32:1558–67. doi:10.3109/09638280903567885
      OpenUrlCrossRefPubMed
    21. ↵
      1. Young CA,
      2. Mills RJ,
      3. Woolmore J, et al
      . The unidimensional self-efficacy scale for MS (USE-MS): developing a patient based and patient reported outcome. Mult Scler 2012;18:1326–33. doi:10.1177/1352458512436592
      OpenUrlAbstract/FREE Full Text
    22. ↵
      1. Zigmond AS,
      2. Snaith RP
      . The hospital anxiety and depression scale. Acta Psychiatr Scand 1983;67:361–70. doi:10.1111/j.1600-0447.1983.tb09716.x
      OpenUrlCrossRefPubMedWeb of Science
    23. ↵
      1. Benedict RH,
      2. Cox D,
      3. Thompson LL, et al
      . Reliable screening for neuropsychological impairment in multiple sclerosis. Mult Scler 2004;10:675–8. doi:10.1191/1352458504ms1098oa
      OpenUrlAbstract/FREE Full Text
    24. ↵
      1. Amtmann D,
      2. Bamer AM,
      3. Noonan V, et al
      . Comparison of the psychometric properties of two fatigue scales in multiple sclerosis. Rehabil Psychol 2012;57:159–66. doi:10.1037/a0027890
      OpenUrlCrossRefPubMed
    25. ↵
      1. Pfleger CC,
      2. Flachs EM,
      3. Koch-Henriksen N
      . Social consequences of multiple sclerosis (1): early pension and temporary unemployment—a historical prospective cohort study. Mult Scler 2010;16:121–6. doi:10.1177/1352458509352196
      OpenUrlAbstract/FREE Full Text
    26. ↵
      1. Strober LB,
      2. Christodoulou C,
      3. Benedict RH, et al
      . Unemployment in multiple sclerosis: the contribution of personality and disease. Mult Scler 2012;18:647–53. doi:10.1177/1352458511426735
      OpenUrlAbstract/FREE Full Text
    27. ↵
      1. Parmenter BA,
      2. Weinstock-Guttman B,
      3. Garg N, et al
      . Screening for cognitive impairment in multiple sclerosis using the Symbol digit Modalities Test. Mult Scler 2007;13:52–7. doi:10.1177/1352458506070750
      OpenUrlAbstract/FREE Full Text
    28. ↵
      1. Benedict RH,
      2. Wahlig E,
      3. Bakshi R, et al
      . Predicting quality of life in multiple sclerosis: accounting for physical disability, fatigue, cognition, mood disorder, personality, and behavior change. J Neurol Sci 2005;231:29–34. doi:10.1016/j.jns.2004.12.009
      OpenUrlCrossRefPubMedWeb of Science
    29. ↵
      1. Miller DM,
      2. Rudick RA,
      3. Baier M, et al
      . Factors that predict health-related quality of life in patients with relapsing-remitting multiple sclerosis. Mult Scler 2003;9:1–5. doi:10.1191/1352458503ms888oa
      OpenUrlAbstract/FREE Full Text
    30. ↵
      1. Kwiatkowski A,
      2. Marissal JP,
      3. Pouyfaucon M, et al
      . Social participation in patients with multiple sclerosis: correlations between disability and economic burden. BMC Neurol 2014;14:115. doi:10.1186/1471-2377-14-115
      OpenUrlCrossRefPubMed

    Footnotes

    • Contributors JC is the lead author on this work and takes full responsibility for the data, analysis, interpretation and presentation. Each author has full access to the relevant data and has reviewed the work prior to submission. All authors have agreed to conditions outlined in the guidance for authors.

    • Funding Brighton and Sussex University Hospitals NHS Trust.

    • Competing interests WR has accepted educational grants and travel bursaries from Genzyme, Biogen-Idec, Novartis and Teva and has also participated on advisory boards with Bayer, Novartis, Biogen-Idec and Genzyme. DL has received research grants from Bayer, Novartis, Biogen and has also participated on advisory board with Bayer, Novartis, Teva. DL is also in the speaker Bureau for Teva, Roche, Bayer, Novartis, Biogen.

    • Ethics approval Northern Ireland Research Ethics Committee.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement All pertinent data contained within the manuscript.