Continuous prediction of secondary progression in the individual course of multiple sclerosis

• Certain features of multiple sclerosis relapses predict impending progression.
• A fundamental relationship is derived from a representative multiple sclerosis cohort.
• A prediction score grades the yearly risk of secondary progression between 1% and 15%.
• This clinically useful score is calculated from current age and recent clinical data.
• It defines periods of increased disease activity and may prompt induction therapy.

BackgroundPrediction of the course of multiple sclerosis (MS) was traditionally based on features close to onset.ObjectiveTo evaluate predictors of the individual risk of secondary progression (SP) identified at any time during relapsing-remitting MS.MethodsWe analysed a database comprising an untreated MS incidence cohort (n=306) with five decades of follow-up. Data regarding predictors of all attacks (n=749) and demographics from patients (n=157) with at least one distinct second attack were included as covariates in a Poisson regression analysis with SP as outcome.ResultsThe average hazard function of transition to SPMS was 0.046 events per patient year, showing a maximum at age 33. Three covariates were significant predictors: age, a descriptor of the most recent relapse, and the interaction between the descriptor and time since the relapse. A hazard function termed “prediction score” estimated the risk of SP as number of transition events per patient year (range <0.01 to >0.15).ConclusionsThe insights gained from this study are that the risk of transition to SP varies over time in individual patients, that the risk of SP is linked to previous relapses, that predictors in the later stages of the course are more effective than the traditional onset predictors, and that the number of potential predictors can be reduced to a few (three in this study) essential items. This advanced simplification facilitates adaption of the “prediction score” to other (more recent, benign or treated) materials, and allows for compact web-based applications (http://msprediction.com).