Identification of Therapeutic Lag in Multiple Sclerosis (1787)
2 REPERES - Recherche en Pharmaco-épidémiologie et Recours aux Soins
3 EA 4069 - Epidémiologie, Evaluation et Politiques de Santé
4 EHESP - École des Hautes Études en Santé Publique
5 METIS - Département Méthodes quantitatives en santé publique
6 CoRHASI - Collectif de recherche handicap, autonomie et société inclusive
7 CIC - Centre d'Investigation Clinique [Rennes]
8 ARENES - Arènes: politique, santé publique, environnement, médias
9 RSMS - Recherche sur les services et le management en santé
10 UR - Université de Rennes
11 Swinburne University of Technology [Melbourne]
12 CRNL - Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center
13 HCL - Hospices Civils de Lyon
14 Fondation Eugène Devic EDMUS
15 Polytechnic University of Bari / Politecnico di Bari
16 Ud'A - Università degli studi "G. d'Annunzio" Chieti-Pescara [Chieti-Pescara]
17 McGill University = Université McGill [Montréal, Canada]
18 UNIMORE - Università degli Studi di Modena e Reggio Emilia = University of Modena and Reggio Emilia
19 IRCCS - Istituti di Ricovero e Cura a Carattere Scientifico
- Fonction : Auteur
- PersonId : 18386
- IdHAL : emmanuelle-leray
- ORCID : 0000-0001-8424-4499
- IdRef : 099858185
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- PersonId : 929973
- ORCID : 0000-0002-5918-9261
- IdRef : 113936036
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- PersonId : 962598
- ORCID : 0000-0001-7337-7122
- IdRef : 07001812X
Résumé
Objective: To develop a method that allows identification of the time to full clinically manifest effect of multiple sclerosis (MS) treatments (‘therapeutic lag’) on clinical disease activity.Background: In MS, treatment start or switch is prompted by evidence of disease activity, often presenting as relapses or disability progression. Whilst immunomodulatory therapies reduce disease activity, the time required to attain maximal effect is unclear.Design/Methods: Data from MSBase, a multinational MS registry, and OFSEP, the French national registry, were used. Patients diagnosed with MS, minimum 1-year exposure to MS treatment, minimum 3-year pre-treatment 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 in the analysis. 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 represents the point of stabilisation of treatment effect, after the maximum treatment effect was observed. The method was developed using MSBase, and externally validated in OFSEP. A merged MSBase-OFSEP cohort was used for all subsequent analyses.Results: 11180 eligible treatment epochs were identified for analysis of relapses and 4088 treatment epochs for disability progression. There were no significant differences between the results of discovery and validation analyses. The duration of therapeutic lag for relapses was calculated for 10 therapies and ranged between 12–30 weeks. The duration of therapeutic lag for disability progression was calculated for 7 therapies and ranged between 30–70 weeks.Conclusions: We have developed, and externally validated, a method to objectively quantify the duration of therapeutic lag on relapses and disability progression in different therapies. This method will be applied in studies that will evaluate the effect of patient and disease characteristics on therapeutic lag.