This study was presented as a poster (P06.074.) at the 63rd Annual Meeting of the American Academy of Neurology held in April, 2011.
Predictors of quality of life among multiple sclerosis patients: a comprehensive analysis
Article first published online: 8 JAN 2013
© 2013 The Author(s) European Journal of Neurology © 2013 EFNS
European Journal of Neurology
Volume 20, Issue 5, pages 756–764, May 2013
How to Cite
Yamout, B., Issa, Z., Herlopian, A., El Bejjani, M., Khalifa, A., Ghadieh, A. S. and Habib, R. H. (2013), Predictors of quality of life among multiple sclerosis patients: a comprehensive analysis. European Journal of Neurology, 20: 756–764. doi: 10.1111/ene.12046
- Issue published online: 11 APR 2013
- Article first published online: 8 JAN 2013
- Manuscript Accepted: 8 OCT 2012
- Manuscript Received: 17 APR 2012
- multiple sclerosis;
- multivariate analysis;
- overall quality of life;
- quality of life
Background and purpose
Multiple sclerosis (MS) is a debilitating neurological disease of young people with substantial consequences on patients' quality of life (QOL). A variety of QOL instruments have been used to evaluate the efficacy of treatments. However, no study assessed the role of the different demographic, clinical, physical, social, economic and psychological parameters in the perception of patients with MS of their QOL.
Two-hundred and one consecutive patients attending outpatient clinics were prospectively studied and objectively assessed using Expanded Disability Status Scale (EDSS), 8-m walk test, and Symbol Digit Modality Test. Patients completed the following questionnaires: MS QOL-54, Hamilton Depression Rating Scale, Fatigue Severity Scale, Brief Pain Inventory Average Pain Score, Drug Side-Effects Severity Scale, Social Support, Religiosity, Physiotherapy and Exercise, and Socioeconomic Profile. Overall, QOL, physical (PHCS) and mental (MHCS) health composite scores were computed as outcome measures from MSQOL-54.
Depression, social support, religiosity, education years and living area predicted overall QOL by linear regression (R2 = 0.43). Unemployment and absence of fatigue correlated with poor and good QOL, respectively. Fatigue, pain, depression, EDSS, social support, MS type and anti-cholinergic treatment predicted PHCS (R2 = 0.81). Fatigue, pain, depression, education years and social support predicted MHCS (R2 = 0.70).
The QOL in patients with MS is not solely determined by physical disability, but rather by the level of social support, living area, depression, level of education, employment, fatigue and religiosity. Accordingly, we suggest that these should be evaluated in every patient with MS as they may be modified by targeted interventions.