Clinical and demographic predictors of long-term disability in patients with relapsing-remitting multiple sclerosis: A systematic review

Objective: To identify clinical and demographic factors associated with long-term disability in patients with relapsing-remitting multiple sclerosis. Data Sources: We searched the MEDLINE (1966-May 2005), EMBASE, CINAHL, Cochrane, and Psyc-INFO computerized databases, and reviewed reference lists of retrieved articles. Study Selection: We included studies that examined predictors of long-term disability in patients with relapsing-remitting multiple sclerosis.Weexcluded studies that did not distinguish relapsing-remitting multiple sclerosis from primary progressive multiple sclerosis, enrolled fewer than 40 subjects, observed subjects for less than 5 years, or collected follow-up information in less than 80% of the inception cohort. Data Extraction: Two reviewers assessed study quality in 4 domains: cohort assembly, definitions and assessments of prognostic factors and outcomes, and statistical methods. One reviewer extracted data on the direction, magnitude, precision, and statistical significance of the effect of each predictor on prognosis. Data Synthesis: Heterogeneity of study designs precluded us from pooling the results of 27 eligible studies. Study quality was limited by cross-sectional design, enrollment of prevalent cases from referral centers, and lack of multivariate adjustment. Sphincter symptoms at onset (hazard ratio, 1.1-3.1), incomplete recovery from the first attack (hazard ratio, 1.3-3.3), and a short interval between the first and second attack (hazard ratio, 1.6-1.9) were most strongly and consistently associated with poor prognosis. Other factors widely believed to be of prognostic importance, including sex and age at onset, demonstrated inconsistent or weak effects on prognosis. Conclusions: The most robust predictors of long-term physical disability in relapsing-remitting multiple sclerosis are sphincter symptoms at onset and early disease course outcomes. These factors can be used to guide treatment decisions for drugs with significant toxicities.