Multiple Biomarkers Improve the Prediction of Multiple Sclerosis in Clinically Isolated Syndromes.

Objectives: Since its introduction, MRI had a major impact on the early and more precise diagnosis of multiple sclerosis (MS), and the 2010 diagnostic criteria even allow a diagnosis to be made just after a single attack if stringent MRI criteria are met. Several other clinical and paraclinical markers have been reported to be associated with an increased risk of MS independently of MRI in patients with clinically isolated syndromes (CIS), but the incremental usefulness of adding them to the current criteria has not been evaluated. In this study, we determined whether multiple biomarkers improved the prediction of MS in patients with CIS in a real-world clinical practice. Materials and methods: This was a retrospective study involving patients with CIS admitted to our department between 2000 and 2013. We evaluated baseline clinical, MRI, neurophysiological, and cerebrospinal fluid (CSF) data. Results: During follow-up (median, 7.2 years), 127 of 243 participants (mean age, 31.6 years) developed MS. Cox proportional-hazards models adjusted for established MRI criteria, age at onset, number of T1 lesions, and presence of CSF oligoclonal bands significantly predicted the risk of developing MS at 2 and 5 years. The use of multiple biomarkers led to 29% net reclassification improvement at 2 years (P <.001) and 30% at 5 years (P <.001). Conclusions: The simultaneous addition of several biomarkers significantly improved the risk stratification for MS in patients with CIS beyond that of a model based only on established MRI criteria.