1193Developing a Multifactorial Prognostic Index in Relapsing Onset Multiple Sclerosis and Clinically Isolated Syndrome

Background Disease course in multiple sclerosis (MS) is characterised by relapses and worsening of disability. This study aims to create, from available clinical, genetic, and environmental factors; a multifactorial prognostic index (MPI) to predict disease course in MS. Methods We analysed prospectively assessed MS cases (N = 253) with 2858 repeated measurements over 10-years. Of the 253 cases, N = 219 were diagnosed as relapsing-onset, while N = 34 remained as clinically isolated syndrome by the 10th-year review. Cox regression models with Least Absolute Shrinkage and Selection Operator were used to select potential genetic, clinical, and environmental factors that are predictive of relapses and/or worsening of disability. Multivariate Cox regression models with leave-one-out cross-validation were used to construct a MPI, from which robust dynamic predictions were obtained by landmarking. The predictive performance at diagnosis was evaluated using the Kullback-Leibler and Brier prediction error curves. Results The MPI predicted a quadratic time-dynamic disease course in terms of relapses (HR = 2.16, CI: 1.74-2.68; C-index=0.85) and worsening of disability (HR = 2.74, CI: 2.00-3.76; C-index=0.76). The Kullback-Leibler and Brier dynamic prediction error curves showed reasonable performance for both short- (≤5-years from diagnosis) and long-term (>5-years from diagnosis) prognostications, respectively. Conclusions The MPI provided reliable information that is relevant for long-term prognostication and may be used as a selection criterion or risk stratification tool for clinical trials. Key messages Using relevant clinical, environmental, and genotype data, we have created a MPI for people living with MS and clinically isolated syndrome.