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Niveditha Gopalakrishnan

BWH Job Title:

Research Assistant II

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Niveditha Gopalakrishnan, Margaret Cadden, Lindsay Barker, Brian C. Healy, Tanuja Chitnis, Howard L. Weiner, Bonnie I. Glanz

Baseline predictors of cross-sectional and longitudinal change in Symbol Digit Modalities Test performance in individuals with multiple sclerosis


Background: Approximately half of patients with Multiple Sclerosis (PwMS) are diagnosed with cognitive impairment at some point during their disease. However, not all PwMS develop cognitive difficulties, suggesting a role for important moderating factors. We examined baseline predictors of cross-sectional and longitudinal change in cognitive performance in PwMS .

Methods: 680 PwMS enrolled in The Comprehensive Longitudinal Investigation of Multiple Sclerosis at Brigham and Women’s Hospital (CLIMB) who completed the Symbol Digit Modalities Test (SDMT), a brief measure of speed of information processing, at least twice during a 10-year period were identified. Several potential baseline clinical and demographic predictors of SDMT performance were examined: age, education (college or more vs. less than college), race, sex, disability measured using the Expanded Disability Status Scale (EDSS)), disease duration, and disease category (relapsing vs. progressive MS). The following baseline patient-reported outcome (PRO) measures were also examined: the Modified Fatigue Impact Scale (MFIS), the Center for Epidemiologic Studies-Depression scale (CES-D) and the Multiple Sclerosis Quality of Life-54 (MSQOL-54). In cross-sectional analyses, the associations between SDMT and each of the baseline predictors were calculated using univariate linear regression, and multiple regression models were used to estimate the associations with three sets of predictors (demographic/clinical only, PRO only, both). In the longitudinal analysis, the associations between each baseline predictor and the longitudinal change in SDMT was estimated using linear mixed effects models with fixed effects for the predictor, time, and the time by treatment interaction as well as a random intercept and slope. Multivariable linear mixed effects including the three sets of predictors together were also fit.

Results: Age, disease duration, and EDSS each showed associations with SDMT in cross-sectional analyses. Further, group differences were observed comparing females to males, white to non-white subjects, education, and subjects with relapsing MS to subjects with progressive MS. For PRO measures, all measures showed associations with SDMT, and the strongest association was with MFIS scores. In a multivariable model including demographic/clinical features, the model explained 25% of the variance in the SDMT score, and the strongest predictors were age, race, education, and disease duration. In a model including the PROs, the model explained 11% of the variance, and the strongest predictor was MFIS score. Finally, the model including all predictors explained 29% of the variance in SDMT, and the strongest independent association was between disease duration and SDMT score. In the longitudinal model, increased baseline age and increased baseline EDSS were each associated with a greater decline in the SDMT score. None of the baseline PROs were associated with the longitudinal change in the SDMT.

Conclusion: These results demonstrate strong associations between baseline demographic, clinical and PRO measures and concurrent SDMT, but more limited associations between these measures and longitudinal change in SDMT.

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