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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 (&amp;gt;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.

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