Early prediction of the long term evolution of multiple sclerosis: The Bayesian Risk Estimate for Multiple Sclerosis (BREMS) score

Abstract

Aim: To propose a simple tool for early prediction of unfavourable long term evolution of multiple sclerosis (MS). Methods: A Bayesian model allowed us to calculate, within the first year of disease and for each patient, the Bayesian Risk Estimate for MS (BREMS) score that represents the risk of reaching secondary progression (SP). Results: The median BREMS scores were higher in 158 patients who reached SP within 10 years compared with 1087 progression free patients (0.69 vs 0.30; p<0.0001). The BREMS value was related to SP risk in the whole cohort (p<0.0001) and in the subgroup of 535 patients who had never been treated with immune therapies, thus reasonably representing the natural history of the disease (p<0.000001). Conclusions: The BREMS score may be useful both to identify patients who are candidates for early or for more aggressive therapies and to improve the design and analysis of clinical therapeutic trials and of observational studies.