| Literature DB >> 32947619 |
Izanne Roos1,2, Emmanuelle Leray3, Federico Frascoli4, Romain Casey5,6,7,8, J William L Brown9, Dana Horakova10, Eva K Havrdova10, Maria Trojano11, Francesco Patti12,13, Guillermo Izquierdo14, Sara Eichau14, Marco Onofrj15, Alessandra Lugaresi16,17, Alexandre Prat18, Marc Girard18, Pierre Grammond19, Patrizia Sola20, Diana Ferraro20, Serkan Ozakbas21, Roberto Bergamaschi22, Maria José Sá23, Elisabetta Cartechini24, Cavit Boz25, Franco Granella26,27, Raymond Hupperts28, Murat Terzi29, Jeannette Lechner-Scott30,31, Daniele Spitaleri32, Vincent Van Pesch33, Aysun Soysal34, Javier Olascoaga35, Julie Prevost36, Eduardo Aguera-Morales37, Mark Slee38, Tunde Csepany39, Recai Turkoglu40, Youssef Sidhom41, Riadh Gouider41, Bart Van Wijmeersch42, Pamela McCombe43,44, Richard Macdonell45,46, Alasdair Coles9, Charles B Malpas1,2, Helmut Butzkueven47,48,49, Sandra Vukusic5,6,7, Tomas Kalincik1,2.
Abstract
In multiple sclerosis, treatment start or switch is prompted by evidence of disease activity. Whilst immunomodulatory therapies reduce disease activity, the time required to attain maximal effect is unclear. In this study we aimed to develop a method that allows identification of the time to manifest fully and clinically the effect of multiple sclerosis treatments ('therapeutic lag') on clinical disease activity represented by relapses and progression-of-disability events. Data from two multiple sclerosis registries, MSBase (multinational) and OFSEP (French), were used. Patients diagnosed with multiple sclerosis, minimum 1-year exposure to treatment, minimum 3-year pretreatment follow-up and yearly review were included in the analysis. For analysis of disability progression, all events in the subsequent 5-year period were included. Density curves, representing incidence of relapses and 6-month confirmed progression events, were separately constructed for each sufficiently represented therapy. Monte Carlo simulations were performed to identify the first local minimum of the first derivative after treatment start; this point represented the point of stabilization of treatment effect, after the maximum treatment effect was observed. The method was developed in a discovery cohort (MSBase), and externally validated in a separate, non-overlapping cohort (OFSEP). A merged MSBase-OFSEP cohort was used for all subsequent analyses. Annualized relapse rates were compared in the time before treatment start and after the stabilization of treatment effect following commencement of each therapy. We identified 11 180 eligible treatment epochs for analysis of relapses and 4088 treatment epochs for disability progression. External validation was performed in four therapies, with no significant difference in the bootstrapped mean differences in therapeutic lag duration between registries. The duration of therapeutic lag for relapses was calculated for 10 therapies and ranged between 12 and 30 weeks. The duration of therapeutic lag for disability progression was calculated for seven therapies and ranged between 30 and 70 weeks. Significant differences in the pre- versus post-treatment annualized relapse rate were present for all therapies apart from intramuscular interferon beta-1a. In conclusion we have developed, and externally validated, a method to objectively quantify the duration of therapeutic lag on relapses and disability progression in different therapies in patients more than 3 years from multiple sclerosis onset. Objectively defined periods of expected therapeutic lag allows insights into the evaluation of treatment response in randomized clinical trials and may guide clinical decision-making in patients who experience early on-treatment disease activity. This method will subsequently be applied in studies that evaluate the effect of patient and disease characteristics on therapeutic lag.Entities:
Keywords: multiple sclerosis; therapeutic lag
Year: 2020 PMID: 32947619 DOI: 10.1093/brain/awaa231
Source DB: PubMed Journal: Brain ISSN: 0006-8950 Impact factor: 13.501