Predictors of Health-Related
Quality of Life and Vocational Status
in Multiple Sclerosis
Chair
Faculty
Timothy L. Vollmer, MD
Professor of Neurology
University of Colorado, Denver
Aurora, Colorado
Ralph H.B. Benedict, PhD
Professor of Neurology, Psychiatry,
and Psychology
Department of Neurology
University of Buffalo
Buffalo General Hospital
Buffalo, New York
Study Rationale and Objective
• Rationale
– Previous research has studied separate predictors of MS healthrelated 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.
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
Age,
education,
etc
Disease
features
Physical
disability
Cognitive
function
Benedict R, et al. J Neurol Sci. 2005;231:29-34.
MSQOL-54
Fatigue
Personality
Mood
disorder
Behavior
disorder
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.
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.
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.
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.