1. Predictors of treatment switching in the Big Multiple Sclerosis Data Network.
- Author
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Spelman, T, Magyari, M, Butzkueven, H, Van Der Walt, A, Vukusic, S, Trojano, M, Iaffaldano, P, Horáková, D, Drahota, J, Pellegrini, F, Hyde, R, Duquette, P, Lechner-Scott, J, Sajedi, SA, Lalive, P, Shaygannejad, V, Ozakbas, S, Eichau, S, Alroughani, R, Terzi, M, Girard, M, Kalincik, T, Grand'Maison, F, Skibina, O, Khoury, SJ, Yamout, B, Sa, MJ, Gerlach, O, Blanco, Y, Karabudak, R, Oreja-Guevara, C, Altintas, A, Hughes, S, McCombe, P, Ampapa, R, de Gans, K, McGuigan, C, Soysal, A, Prevost, J, John, N, Inshasi, J, Stawiarz, L, Manouchehrinia, A, Forsberg, L, Sellebjerg, F, Glaser, A, Pontieri, L, Joensen, H, Rasmussen, PV, Sejbaek, T, Poulsen, MB, Christensen, JR, Kant, M, Stilund, M, Mathiesen, H, Hillert, J, 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, Spelman, T, Magyari, M, Butzkueven, H, Van Der Walt, A, Vukusic, S, Trojano, M, Iaffaldano, P, Horáková, D, Drahota, J, Pellegrini, F, Hyde, R, Duquette, P, Lechner-Scott, J, Sajedi, SA, Lalive, P, Shaygannejad, V, Ozakbas, S, Eichau, S, Alroughani, R, Terzi, M, Girard, M, Kalincik, T, Grand'Maison, F, Skibina, O, Khoury, SJ, Yamout, B, Sa, MJ, Gerlach, O, Blanco, Y, Karabudak, R, Oreja-Guevara, C, Altintas, A, Hughes, S, McCombe, P, Ampapa, R, de Gans, K, McGuigan, C, Soysal, A, Prevost, J, John, N, Inshasi, J, Stawiarz, L, Manouchehrinia, A, Forsberg, L, Sellebjerg, F, Glaser, A, Pontieri, L, Joensen, H, Rasmussen, PV, Sejbaek, T, Poulsen, MB, Christensen, JR, Kant, M, Stilund, M, Mathiesen, H, Hillert, J, and 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
- 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 1
- Published
- 2023