Predicting Neuropsychological Impairment in Relapsing Remitting Multiple Sclerosis: The Role of Clinical Measures, Treatment, and Neuropsychiatry Symptoms

Abstract

Objective: This retrospective observational study aimed to define neuropsychological impairment (NI) profiles and determine the influence of clinical, demographic, and neuropsychiatric measures in specific cognitive domains in a cohort of relapsing-remitting multiple sclerosis (RRMS) patients.

Methods: Ninety-one RRMS patients underwent a neurological examination and a brief neuropsychological assessment. Patients were classified according to the disease-modifying therapies (DMTs) received (platform or high-efficacy). Differences between groups and multiple regression analyses were performed to determine the predictive value of the assessed measures in cognitive performance.

Results: More than two-thirds of the patients showed NI. Specifically, mild to moderate NI was presented in approximately half of the participants. Paced Auditory Serial Addition Test (PASAT-3) and Symbol Digit Modalities Test (SDMT) were the most frequently impaired cognitive tests (45.3% and 41.3%, respectively) followed by phonemic verbal fluency (PVF) (27.8%). Expanded Disability Status Scale (EDSS), age, depressive symptoms, and disease duration were the best predictors of SDMT (R2 = .34; p < .01), whereas disease duration, EDSS, and anxiety-state levels predicted PASAT-3 (R2 = .33, p < .01). Educational level, age, EDSS, and depressive symptoms demonstrated the strongest association with PVF (R2 = .31, p < .01).

Conclusions: Our results indicated a significant prevalence of NI in RRMS patients that was not dependent on the DMT type. In addition to the meaningful working memory (PASAT-3) and information processing speed (SDMT) impairments found, PVF deficits may also be an important marker of cognitive impairment in RRMS patients. This study supports the relevance of standard clinical measures and reinforces the importance of quantifying clinical and neuropsychiatric symptoms to predict subsequent cognitive performance on a similar multiple sclerosis phenotype and disease stage.