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

2. Study Rationale and Objective
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. 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
Vocational
Status
Fatigue
Age,
education,
etc
Disease
features
Physical
disability
Cognitive
function
Mood
disorder
Personality
Behavior
MSQOL-54
Benedict R, et al. J Neurol Sci. 2005;231:29-34.

4. Parameters Examined Disease characteristics Physical disability
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 Mental 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.