Chair Timothy L. Vollmer, MD Professor of Neurology

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Presentation transcript:

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

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.

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.

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.

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.

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.