Background: Although relapsing-remitting multiple sclerosis (RRMS) has a chronic course, little information is known about the comparison between the disease-modifying therapies (DMT) for long-term outcomes. We aimed to conduct a systematic review of randomized clinical trial (RCT) extension and observational studies to examine the efficacy and safety of all available DMT for RRMS, compare the evidence with that derived from mid-term studies, and investigate whether the published long-term data are robust and reliable enough to inform clinical decision-making concerning RRMS treatment.
Method: PubMed, Scopus, and manual searches were performed until October 2019. The clinical outcomes of long- and mid-term studies were compared. ROBINS-I was used to assess the methodological qualities of the long-term studies. PROSPERO number CRD42019123361.
Results: Nineteen long-term studies (9,018 participants) were included in the systematic review. All studies presented serious or critical risks of bias that were mainly due to confounding, selection, and missing data biases. The annualised relapse rates (ARR) observed in the long-term studies are lower (better) than those from the mid-term studies for most treatments. The main reason for this ARR decrease could be a selection bias for good responders in the long-term studies, since many studies show a loss of patients between the mid- and long-term phases. The safety profiles depend on the study, follow-up, report, and outcome (i.e., discontinuation or number of patients with at least one serious adverse event).
Conclusion: The currently available long-term data for patients with RRMS exhibit serious or critical risks of bias that preclude robust comparisons between long-term studies. High quality comparative observational studies with long-term follow-ups or RCT extensions with intention-to-treat analyses are needed to support clinical and regulatory practice. Until reliable long-term evidence is available, neurologists should continue to base their conduct on mid-term studies, patient`s experience and, most importantly, patient`s needs and predictor factors, according to personalized medicine.