PND75 VALIDATION OF AN ALGORITHM IDENTIFYING RELAPSES IN MULTIPLE SCLEROSIS USING THE FRENCH NATIONWIDE CLAIMS DATABASE

Abstract

Although the main limitation of the French nationwide claims database (SNDS) is the absence of clinical information, relapse is an outcome that can be identified to assess effects of disease modifying therapies in multiple sclerosis (MS) in real word setting. The objective of this study was to assess the validity of an algorithm identifying relapses in MS patients in SNDS. A random sample of 200 patients - 100 with at least one relapse and 100 without relapse screened by the algorithm - were randomly selected from a cohort of 37,986 MS patients previously identified in the SNDS. For each case, all data available in the SNDS, in particular those related to the dispensing of corticosteroids, hospitalizations for potential MS relapse or for administration of high dose of steroids and plasmapheresis procedures were examined by 2 neurologists to assess the presence or absence of relapses, blind to the result of the algorithm. In the event of an inter-expert discrepancy, the summary sheets were reviewed in a collegiate manner, in order to reach a consensus. Algorithm performance was estimated using the positive and negative predictive values (PPV, NPV). Among the 200 patients randomly selected- 100 with at least one relapse and 100 without relapse - the algorithm correctly detected 95% patients with relapses (PPV) and 96% of patients without relapses (NPV). This claim-based algorithm appeared to successfully detect MS relapse and could thus be applied to future observational MS studies in SNDS. It could be secondarily revised to include all changes proposed by the experts in order to optimize its performance.