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Predictors of Health- Related Quality of Life and Vocational Status in Multiple Sclerosis

Predictors of Health- Related Quality of Life and Vocational Status in Multiple Sclerosis. Faculty Ralph H.B. Benedict, PhD Professor of Neurology, Psychiatry, and Psychology Department of Neurology University of Buffalo Buffalo General Hospital Buffalo, New York. Chair

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Predictors of Health- Related Quality of Life and Vocational Status in Multiple Sclerosis

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  1. Predictors of Health-RelatedQuality of Life and Vocational Status in Multiple Sclerosis Faculty Ralph H.B. Benedict, PhD Professor of Neurology, Psychiatry, and Psychology Department of Neurology University of Buffalo Buffalo General Hospital Buffalo, New York Chair Timothy L. Vollmer, MD Professor of Neurology University of Colorado, Denver Aurora, Colorado

  2. Study Rationale and Objective • Rationale • Previous research has studied separate predictors of MS health-related quality of life (HQOL) only; multiple predictors have not been considered together • Objectives • To examine potential predictors simultaneously • To determine the parameters that account for the most variance in predicting HQOL* and vocational status/employability • Participants • 120 MS patients • 90 relapsing-remitting • 28 secondary progressive • 2 primary progressive • 44 healthy volunteers * Measured on the MS Quality of Life-54 Benedict R, et al. J Neurol Sci. 2005;231:29-34.

  3. Vocational Status MSQOL-54 Age, education, etc Disease features Physical disability Cognitive function Fatigue Personality Mood disorder Behavior disorder Predicting QOL in Multiple Sclerosis Accounting for Disease Characteristics, Physical Disability, Fatigue, Cognition, Mood Disorder, Personality, and Behavior Change Linear regression analysis predicting QOL outcomes in representative sample of 120 MS patients Benedict R, et al. J Neurol Sci. 2005;231:29-34.

  4. Parameters Examined • Disease characteristics • Relapsing-remitting vs progressive; disease duration • Physical disability • Expanded Disability Status Scale (EDSS) • Fatigue • Fatigue Severity Scale (FSS) • Cognitive function • Minimal Assessment of Cognitive Function in MS (MACFIMS) battery • Personality traits • Agreeableness and conscientiousness subscales of the Revised NEO Personality Inventory (NEOPI) • Mood disorder • Beck Depression Inventory (BDI), Beck Depression Inventory-Fast Screen(BDI-FS), Center for Epidemiologic Studies Depression Scale (CESD-10) • Behavioral dysfunction • Neuropsychiatric Inventory (NPI) Benedict R, et al. J Neurol Sci. 2005;231:29-34.

  5. Variance in MS HQOL • Physical health composite • 69% of variance (P <.001) • Fatigue (FSS P <.001) • Depression (CESD-10 P <.001; BDI P <.05) • Physical disability (EDSS P <.01) • Mental health composite • 71% of variance (P <.001) • Depression (CESD-10 P <.001) • Fatigue (FSS P <.01) • Overall index • 63% of variance (P <.001) • Depression (CESD-10 P <.001; BDI P <.01) • NEO Personality Inventory – conscientiousness (self-report)(P <.01) Benedict R, et al. J Neurol Sci. 2005;231:29-34.

  6. Variance in Vocational Status • MS patients employed and not disabled (n = 43) vs MS patients unemployed and disabled(n = 54)* • Predictors accounting for variance • Components of the Minimal Assessment of Cognitive Function in MS battery • Symbol Digit Modalities Test (P <.001) • Wisconsin Card Sorting Test (P <.01) • Judgment of Line Orientation Test (P <.05) • NEO Personality Inventory – conscientiousness (informant report) (P = .01) • Disease duration (P <.05) *23 patients not classified due to ambiguous information or unemployed for other reasons (eg, homemaker, retired, etc) Benedict R, et al. J Neurol Sci. 2005;231:29-34.

  7. Summary and Conclusions • Variance in HQOL was accounted for primarily by depression and fatigue, but not by cognitive function • Most of the variance in vocational status was accounted for by cognitive function; depression was not a significant factor • MS patients should be carefully screened for depression/fatigue and treated as soon as possible • MS-related cognitive function is common; patients may benefit by psychological compensatory interventions Benedict R, et al. J Neurol Sci. 2005;231:29-34.

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