ArticleFrontiers in neurology2023
Predictors of treatment switching in the Big Multiple Sclerosis Data Network.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Big data in multiple sclerosis.Current opinion in neurology · 2026Review
- International Expert Opinion on Optimal Switching to Cladribine Tablets from Other High-Efficacy Disease-Modifying Therapies for Relapsing-Remitting Multiple Sclerosis: Opportunities and Challenges.Neurology and therapy · 2026Review
- Building Towards Initiation, Moderation, De-Escalation and Cessation of Disease-Modifying Treatments for Multiple Sclerosis in Greece: An Expert Panel Consensus Meeting.Brain sciences · 2026Article
- Patient profiling in relapsing multiple sclerosis: insights from a real-world observational study into the unmet medical needs of patients on disease-modifying therapy.Journal of neurology · 2026Observational
- Did COVID-19 Pandemic Change the Course of Multiple Sclerosis? A Retrospective Eight-Year Study.Health science reports · 2025Article
- Real-World Perspectives on Switching Patients with Multiple Sclerosis from Anti-CD20 Therapy to Cladribine Tablets from USA-Based Advanced Practice Providers: A Podcast.Advances in therapy · 2025Article
- Predictors of therapy switching to high-efficacy disease-modifying therapies in patients with multiple sclerosis: a single center, retrospective, observational study.Frontiers in neurology · 2025Article
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57 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Treatment switching is a common challenge and opportunity in real-world clinical practice. Increasing diversity in disease-modifying treatments (DMTs) has generated interest in the identification of reliable and robust predictors of treatment switching across different countries, DMTs, and time periods. Objective: The objective of this retrospective, observational study was to identify independent predictors of treatment switching in a population of relapsing-remitting MS (RRMS) patients in the Big Multiple Sclerosis Data Network of national clinical registries, including the Italian MS registry, the OFSEP of France, the Danish MS registry, the Swedish national MS registry, and the international MSBase Registry. Methods: In this cohort study, we merged information on 269,822 treatment episodes in 110,326 patients from 1997 to 2018 from five clinical registries. Patients were included in the final pooled analysis set if they had initiated at least one DMT during the relapsing-remitting MS (RRMS) stage. Patients not diagnosed with RRMS or RRMS patients not initiating DMT therapy during the RRMS phase were excluded from the analysis. The primary study outcome was treatment switching. A multilevel mixed-effects shared frailty time-to-event model was used to identify independent predictors of treatment switching. The contributing MS registry was included in the pooled analysis as a random effect. Results: Every one-point increase in the Expanded Disability Status Scale (EDSS) score at treatment start was associated with 1.08 times the rate of subsequent switching, adjusting for age, sex, and calendar year (adjusted hazard ratio [aHR] 1.08; 95% CI 1.07-1.08). Women were associated with 1.11 times the rate of switching relative to men (95% CI 1.08-1.14), whilst older age was also associated with an increased rate of treatment switching. DMTs started between 2007 and 2012 were associated with 2.48 times the rate of switching relative to DMTs that began between 1996 and 2006 (aHR 2.48; 95% CI 2.48-2.56). DMTs started from 2013 onwards were more likely to switch relative to the earlier treatment epoch (aHR 8.09; 95% CI 7.79-8.41; reference = 1996-2006). Conclusion: Switching between DMTs is associated with female sex, age, and disability at baseline and has increased in frequency considerably in recent years as more treatment options have become available. Consideration of a patient's individual risk and tolerance profile needs to be taken into account when selecting the most appropriate switch therapy from an expanding array of treatment choices.
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