BACKGROUND AND PURPOSE: While impaired cognitive performance is common in multiple sclerosis (MS), it has been largely underdiagnosed. Here a magnetic resonance imaging (MRI) screening algorithm is proposed to identify patients at highest risk of cognitive impairment. The objective was to examine whether assessment of lesion burden together with whole brain atrophy on MRI improves our ability to identify cognitively impaired MS patients. METHODS: Of the 1253 patients enrolled in the study, 1052 patients with all cognitive, volumetric MRI and clinical data available were included in the analysis. Brain MRI and neuropsychological assessment with the Brief International Cognitive Assessment for Multiple Sclerosis were performed. Multivariable logistic regression and individual prediction analysis were used to investigate the associations between MRI markers and cognitive impairment. The results of the primary analysis were validated at two subsequent time points (months 12 and 24). RESULTS: The prevalence of cognitive impairment was greater in patients with low brain parenchymal fraction (BPF) (<0.85) and high T2 lesion volume (T2-LV) (>3.5 ml) than in patients with high BPF (>0.85) and low T2-LV (<3.5 ml), with an odds ratio (OR) of 6.5 (95% CI 4.4-9.5). Low BPF together with high T2-LV identified in 270 (25.7%) patients predicted cognitive impairment with 83% specificity, 82% negative predictive value, 51% sensitivity and 75% overall accuracy. The risk of confirmed cognitive decline over the follow-up was greater in patients with high T2-LV (OR 2.1; 95% CI 1.1-3.8) and low BPF (OR 2.6; 95% CI 1.4-4.7). CONCLUSIONS: The integrated MRI assessment of lesion burden and brain atrophy may improve the stratification of MS patients who may benefit from cognitive assessment.

Department of Health Sciences University of Genoa Genoa Italy

Department of Medicine University of Melbourne Melbourne Vic Australia Department of Neurology Royal Melbourne Hospital Melbourne Vic Australia

Department of Neurology and Centre of Clinical Neuroscience 1st Faculty of Medicine Charles University and General University Hospital Prague Czech Republic

Department of Neurology School of Medicine and Biomedical Sciences University at Buffalo State University of New York Buffalo NY USA

Department of Radiology 1st Faculty of Medicine Charles University and General University Hospital Prague Czech Republic

Department of Statistics and Probability University of Economics Prague Prague Czech Republic

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