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Predictors of treatment switching in the Big Multiple Sclerosis Data Network

BackgroundTreatment 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...

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Autori principali: Tim Spelman, Melinda Magyari, Helmut Butzkueven, Anneke Van Der Walt, Sandra Vukusic, Maria Trojano, Pietro Iaffaldano, Dana Horáková, Jirí Drahota, Fabio Pellegrini, Robert Hyde, Pierre Duquette, Jeannette Lechner-Scott, Seyed Aidin Sajedi, Patrice Lalive, Vahid Shaygannejad, Serkan Ozakbas, Sara Eichau, Raed Alroughani, Murat Terzi, Marc Girard, Tomas Kalincik, Francois Grand'Maison, Olga Skibina, Samia J. Khoury, Bassem Yamout, Maria Jose Sa, Oliver Gerlach, Yolanda Blanco, Rana Karabudak, Celia Oreja-Guevara, Ayse Altintas, Stella Hughes, Pamela McCombe, Radek Ampapa, Koen de Gans, Chris McGuigan, Aysun Soysal, Julie Prevost, Nevin John, Jihad Inshasi, Leszek Stawiarz, Ali Manouchehrinia, Lars Forsberg, Finn Sellebjerg, Anna Glaser, Luigi Pontieri, Hanna Joensen, Peter Vestergaard Rasmussen, Tobias Sejbaek, Mai Bang Poulsen, Jeppe Romme Christensen, Matthias Kant, Morten Stilund, Henrik Mathiesen, Jan Hillert, The Big MS Data Network: a collaboration of the Czech MS Registry, the Danish MS Registry, Italian MS Registry, Swedish MS Registry, MSBase Study Group, and OFSEP
Natura: Artigo
Lingua:Inglês
Pubblicazione: Frontiers Media S.A. 2023-12-01
Serie:Frontiers in Neurology
Soggetti:
multiple sclerosis
disease modifying treatment (DMT)
treatment switching
disease registry
real world evidence (RWE)
Accesso online:https://www.frontiersin.org/articles/10.3389/fneur.2023.1274194/full
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