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Background: Simultaneous comparisons of multiple disease-modifying therapies for relapsing-remitting multiple sclerosis (RRMS) over an extended follow-up are lacking. Here we emulate a randomised trial simultaneously comparing the effectiveness of six commonly used therapies over 5 years., Methods: Data from 74 centres in 35 countries were sourced from MSBase. For each patient, the first eligible intervention was analysed, censoring at change/discontinuation of treatment. The compared interventions included natalizumab, fingolimod, dimethyl fumarate, teriflunomide, interferon beta, glatiramer acetate and no treatment. Marginal structural Cox models (MSMs) were used to estimate the average treatment effects (ATEs) and the average treatment effects among the treated (ATT), rebalancing the compared groups at 6-monthly intervals on age, sex, birth-year, pregnancy status, treatment, relapses, disease duration, disability and disease course. The outcomes analysed were incidence of relapses, 12-month confirmed disability worsening and improvement., Results: 23 236 eligible patients were diagnosed with RRMS or clinically isolated syndrome. Compared with glatiramer acetate (reference), several therapies showed a superior ATE in reducing relapses: natalizumab (HR=0.44, 95% CI=0.40 to 0.50), fingolimod (HR=0.60, 95% CI=0.54 to 0.66) and dimethyl fumarate (HR=0.78, 95% CI=0.66 to 0.92). Further, natalizumab (HR=0.43, 95% CI=0.32 to 0.56) showed a superior ATE in reducing disability worsening and in disability improvement (HR=1.32, 95% CI=1.08 to 1.60). The pairwise ATT comparisons also showed superior effects of natalizumab followed by fingolimod on relapses and disability., Conclusions: The effectiveness of natalizumab and fingolimod in active RRMS is superior to dimethyl fumarate, teriflunomide, glatiramer acetate and interferon beta. This study demonstrates the utility of MSM in emulating trials to compare clinical effectiveness among multiple interventions simultaneously., Competing Interests: Competing interests: The authors report the following relationships: speaker honoraria, advisory board or steering committee fees, research support and/or conference travel support from Acthelion (EKH, RA), Almirall (MT, FG, RB, CRT, JLS-M), Bayer (MT, AL, PS, RA, MT, CB, JL-S, EP, VVP, RB, DS, RA, JO, JLSM, SH, CR, AGK, TC, NS, BT, MS, CAS), BioCSL (TK, AGK, BT), Biogen (TK, TS, DH, EKH, MT, GI, AL, MG, PD, PG, VJ, AVW, FG, PS, DF, RA, RH, CB, JLS, EP, VVP, FG, RB, RA, CRT, JP, JO, MB, JLSM, SH, CR, CSh, OGerlach, AGK, TC, BS, NS, BT, MS, HB), Biologix (RA), BMS/Celgene (EKH, AL), Genpharm (RA), Genzyme-Sanofi (TK, EKH, MT, GI, AL, MG, PD, PG, AVW, FG, PS, DF, RA, RH, MT, CB, JLS, EP, EP, VVP, FG, RB, RB, DS, CRT, JP, JO, MB, JLSM, SH, O Gerlach, AGK, HB), GSK (RA), Innate Immunotherapeutics (AGK), Lundbeck (EP), Merck / EMD (TK, DH, EKH, MT, GI, AL(Merck Serono), MG, PD, PG, VJ, AVW, PS, DF, RA, RH, MT, CB, JLS, EP, VVP, FG, RB, DS, RA, JO, MB, JLSM, CR, FM, O Gerlach, AGK, TC, BS, MS, HB), Mitsubishi (FG),Novartis (TK, TS, DH, EKH, MT, GI, AL, MG, PD, PG, VJ, AVW, FG, PS, DF, RA, RH, MT, CB, JLS, EP, VVP, FG, RB, DS, RA, CRT, JP, JO, MB, JLSM, SH, CR, FM, CSh, OG, AGK, TC, NS, BT, MS, HB), ONO Pharmaceuticals (FG), Roche (TK, EKH, AL, MT, CB, VVP, BT), Teva (TK, DH, EKH, MT, GI, AL, MG, PD, PG, VJ, FG, PS, DF, RH, MT, CB, JLS, VVP, RB, DS, RA, JP, JO, JLSM, CR, AGK, TC, MS, CAS), WebMD (TK), UCB (EP)., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Geographical variations in the incidence and prevalence of multiple sclerosis have been reported globally. Latitude as a surrogate for exposure to ultraviolet radiation but also other lifestyle and environmental factors are regarded as drivers of this variation. No previous studies evaluated geographical variation in the risk of secondary progressive multiple sclerosis, an advanced form of multiple sclerosis that is characterized by steady accrual of irreversible disability. We evaluated differences in the risk of secondary progressive multiple sclerosis in relation to latitude and country of residence, modified by high-to-moderate efficacy immunotherapy in a geographically diverse cohort of patients with relapsing-remitting multiple sclerosis. The study included relapsing-remitting multiple sclerosis patients from the global MSBase registry with at least one recorded assessment of disability. Secondary progressive multiple sclerosis was identified as per clinician diagnosis. Sensitivity analyses used the operationalized definition of secondary progressive multiple sclerosis and the Swedish decision tree algorithm. A proportional hazards model was used to estimate the cumulative risk of secondary progressive multiple sclerosis by country of residence (latitude), adjusted for sex, age at disease onset, time from onset to relapsing-remitting phase, disability (Multiple Sclerosis Severity Score) and relapse activity at study inclusion, national multiple sclerosis prevalence, government health expenditure, and proportion of time treated with high-to-moderate efficacy disease-modifying therapy. Geographical variation in time from relapsing-remitting phase to secondary progressive phase of multiple sclerosis was modelled through a proportional hazards model with spatially correlated frailties. We included 51 126 patients (72% female) from 27 countries. The median survival time from relapsing-remitting phase to secondary progressive multiple sclerosis among all patients was 39 (95% confidence interval: 37 to 43) years. Higher latitude [median hazard ratio = 1.21, 95% credible interval (1.16, 1.26)], higher national multiple sclerosis prevalence [1.07 (1.03, 1.11)], male sex [1.30 (1.22, 1.39)], older age at onset [1.35 (1.30, 1.39)], higher disability [2.40 (2.34, 2.47)] and frequent relapses [1.18 (1.15, 1.21)] at inclusion were associated with increased hazard of secondary progressive multiple sclerosis. Higher proportion of time on high-to-moderate efficacy therapy substantially reduced the hazard of secondary progressive multiple sclerosis [0.76 (0.73, 0.79)] and reduced the effect of latitude [interaction: 0.95 (0.92, 0.99)]. At the country-level, patients in Oman, Tunisia, Iran and Canada had higher risks of secondary progressive multiple sclerosis relative to the other studied regions. Higher latitude of residence is associated with a higher probability of developing secondary progressive multiple sclerosis. High-to-moderate efficacy immunotherapy can mitigate some of this geographically co-determined risk., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: Some studies comparing primary and secondary progressive multiple sclerosis (PPMS, SPMS) report similar ages at onset of the progressive phase and similar rates of subsequent disability accrual. Others report later onset and/or faster accrual in SPMS. Comparisons have been complicated by regional cohort effects, phenotypic differences in sex ratio and management and variable diagnostic criteria for SPMS., Methods: We compared disability accrual in PPMS and operationally diagnosed SPMS in the international, clinic-based MSBase cohort. Inclusion required PPMS or SPMS with onset at age ≥18 years since 1995. We estimated Andersen-Gill hazard ratios for disability accrual on the Expanded Disability Status Scale (EDSS), adjusted for sex, age, baseline disability, EDSS score frequency and drug therapies, with centre and patient as random effects. We also estimated ages at onset of the progressive phase (Kaplan-Meier) and at EDSS milestones (Turnbull). Analyses were replicated with physician-diagnosed SPMS., Results: Included patients comprised 1872 with PPMS (47% men; 50% with activity) and 2575 with SPMS (32% men; 40% with activity). Relative to PPMS, SPMS had older age at onset of the progressive phase (median 46.7 years (95% CI 46.2-47.3) vs 43.9 (43.3-44.4); p<0.001), greater baseline disability, slower disability accrual (HR 0.86 (0.78-0.94); p<0.001) and similar age at wheelchair dependence., Conclusions: We demonstrate later onset of the progressive phase and slower disability accrual in SPMS versus PPMS. This may balance greater baseline disability in SPMS, yielding convergent disability trajectories across phenotypes. The different rates of disability accrual should be considered before amalgamating PPMS and SPMS in clinical trials., Competing Interests: Competing interests: IR served on scientific advisory boards for Novartis and Merck, and received conference travel support and/or speaker honoraria from Roche, Novartis, Biogen, Teva, Sanofi Genzyme, and Merck. A-LN received grants from MS Research Australia; grants, personal fees, and nonfinancial support from Biogen; grants and personal fees from Merck Serono; personal fees from Teva and Novartis; and nonfinancial support from Roche and Sanofi Genzyme. GI received speaking honoraria from Biogen, Novartis, Sanofi, Merck, Roche, Almirall, and Teva. SE received speaker honoraria and consultant fees from Biogen Idec, Novartis, Merck, Bayer, Sanofi Genzyme, Roche, and Teva. FP received speaker honoraria and advisory board fees from Almirall, Bayer, Biogen, Celgene, Merck, Novartis, Roche, Sanofi Genzyme, and Teva, and research funding from Biogen, Merck, FISM (Fondazione Italiana Sclerosi Multipla), Reload Onlus Association, and the University of Catania. DH received speaker honoraria and consulting fees from Biogen, Merck, Teva, Roche, Sanofi Genzyme, and Novartis, and support for research activities from Biogen and the Czech Ministry of Education (project PROGRES Q27/LF1). EVH received honoraria or research support from Biogen, Merck Serono, Novartis, Roche, and Teva; has been a member of advisory boards for Actelion, Biogen, Celgene, Merck Serono, Novartis, and Sanofi Genzyme; and received research support from the Czech Ministry of Education (project PROGRES Q27/LF1). MG received consulting fees from Teva Canada Innovation, Biogen, Novartis, and Sanofi Genzyme; lecture payments from Teva Canada Innovation, Novartis, and EMD; and research support from the Canadian Institutes of Health Research. PD served on editorial boards for, and has been supported to attend meetings by, EMD, Biogen, Novartis, Genzyme, and Teva Neuroscience; he holds grants from the Canadian Institutes of Health Research and the MS Society of Canada, and received funding for investigator-initiated trials from Biogen, Novartis, and Genzyme. FG’M received honoraria or research funding from Biogen, Genzyme, Novartis, Teva Neurosciences, Mitsubishi, and ONO Pharmaceuticals. AL received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities from Biogen, Merck Serono, Mylan, Novartis, Roche, Sanofi Genzyme, and Teva; her institutions have received research grants from Novartis (in the past 4 years). PG served on advisory boards for Novartis, EMD Serono, Roche, Biogen Idec, Sanofi Genzyme, and Pendopharm; received grant support from Genzyme and Roche; and received research grants for his institution from Biogen Idec, Sanofi Genzyme, and EMD Serono. MPA received honoraria as a consultant on scientific advisory boards for Biogen, Bayer Schering, Merck, Teva, and Sanofi-Aventis, and received research grants by Biogen, Bayer Schering, Merck, Teva, and Novartis. PS served on scientific advisory boards for Biogen Idec and Teva; received funding for travel and speaker honoraria from Biogen Idec, Merck, Teva, Sanofi Genzyme, Novartis, and Bayer; and received research grants for her institution from Bayer, Biogen, Merck, Novartis, Sanofi, and Teva. DF received travel grants and/or speaker honoraria from Merck, Teva, Novartis, Biogen, and Sanofi Genzyme. KB received honoraria and consulting fees from Biogen, Teva, Novartis, Sanofi Genzyme, Roche, Merck, CSL, and Grifols. JL-S received travel compensation from Novartis, Biogen, Roche, and Merck; her institution received honoraria for talks and advisory board commitments, as well as research grants from Biogen, Merck, Roche, Teva, and Novartis. RA received honoraria as a speaker and for serving on scientific advisory boards from Bayer, Biogen, GSK, Merck, Novartis, Roche, and Sanofi Genzyme. CB received conference travel support from Biogen, Novartis, Bayer Schering, Merck, and Teva, and participated in clinical trials by Sanofi-Aventis, Roche, and Novartis. VVP received travel grants from Merck, Biogen, Sanofi, Celgene, Almirall, and Roche; his institution received research grants and consultancy fees from Roche, Biogen, Sanofi, Celgene, Merck, and Novartis Pharma. MT received travel grants from Novartis, Bayer Schering, Merck, and Teva, and participated in clinical trials by Sanofi-Aventis, Roche, and Novartis. DM received speaker honoraria for advisory board service and travel grants from Almirall, Biogen, Merck, Novartis, Roche, Sanofi Genzyme, and Teva. CR-T received research funding, compensation for travel, or speaker honoraria from Biogen, Novartis, Genzyme, and Almirall. DLS received honoraria as a consultant on scientific advisory boards from Bayer Schering, Novartis, and Sanofi-Aventis, and compensation for travel from Novartis, Biogen, Sanofi-Aventis, Teva, and Merck. FG received an institutional research grant from Biogen and Sanofi Genzyme; served on scientific advisory boards for Biogen, Novartis, Merck, Sanofi Genzyme, and Roche; and received funding for travel and speaker honoraria from Biogen, Merck, and Sanofi-Aventis. MS participated in, but did not receive honoraria for, advisory board activity for Biogen, Merck, Bayer Schering, Sanofi-Aventis, and Novartis. RB received speaker honoraria from Bayer Schering, Biogen, Genzyme, Merck, Novartis, Sanofi-Aventis, and Teva; research grants from Bayer Schering, Biogen, Merck, Novartis, Sanofi-Aventis, and Teva; and congress, travel, and accommodation expense compensations from Almirall, Bayer Schering, Biogen, Genzyme, Merck, Novartis, Sanofi-Aventis, and Teva. RA received conference travel support from Novartis, Teva, Biogen, Bayer, and Merck, and participated in clinical trials by Biogen, Novartis, Teva, and Actelion. JLS-M received travel compensation from Novartis and Biogen; received speaking honoraria from Biogen, Novartis, Sanofi, Merck, Almirall, Bayer, and Teva; and participated in a clinical trial by Biogen. JP received travel compensation from Novartis, Biogen, Genzyme, and Teva, and speaking honoraria from Biogen, Novartis, Genzyme and Teva. TC-T received speaking or consulting fees and/or travel funding from Bayer, Biogen, Merck, Novartis, Roche, Sanofi Genzyme, and Teva. GL received travel and/or consultancy compensation from Sanofi Genzyme, Roche, Teva, Merck, Novartis, Celgene, and Biogen. JO received research funding from the MS Society of Canada, the National MS Society, Brain Canada, Biogen Idec, Roche, and EMD Serono, and personal compensation for consulting or speaking from EMD Serono, Sanofi Genzyme, Biogen Idec, Roche, Celgene, and Novartis. AA received personal fees and speaker honoraria from Teva, Merck, Biogen Gen Pharma, Roche, Novartis, Bayer, and Sanofi Genzyme, and received travel and registration grants from Merck, Biogen Gen Pharma, Roche, Sanofi Genzyme, and Bayer. HB received compensation for consulting, talks, and advisory or steering board activities from Biogen, Merck, Novartis, Genzyme, Alfred Health, and Oxford Health Policy Forum, and research support from Novartis, Biogen, Roche, Merck, the National Health and Medical Research Council of Australia, Pennycook Foundation, and MS Research Australia MB served on scientific advisory boards for Biogen, Novartis, and Genzyme, received conference travel support from Biogen and Novartis, and serves on steering committees for trials conducted by Novartis; his institution received research support from Biogen, Merck, and Novartis. EC Cristiano received honoraria as a consultant on scientific advisory boards for Biogen, Bayer Schering, Merck, Genzyme, and Novartis, and participated in clinical trials or other research projects by Merck, Roche, and Novartis. SH received honoraria and consulting fees from Novartis, Bayer Schering, and Sanofi, and travel grants from Novartis, Biogen Idec, and Bayer Schering. GI received compensation for travel, accommodations, and meeting expenses from Bayer Schering, Biogen, Merck, Novartis, Sanofi-Aventis, and Teva. LK received research support from Acorda, Actelion, Allozyne, BaroFold, Bayer HealthCare, Bayer Schering, Bayhill Therapeutics, Biogen, Elan, European Union, Genmab, Gianni Rubatto Foundation, GlaxoSmithKline, Glenmark, MediciNova, Merck, Novartis, Novartis Research Foundation, Roche, Roche Research Foundation, Sanofi-Aventis, Santhera, the Swiss MS Society, the Swiss National Research Foundation, Teva Neuroscience, UCB, and Wyeth. BW-G participated in speakers' bureaus and/or served as a consultant for Biogen, EMD Serono, Novartis, Genentech, Celgene/Bristol Meyers Squibb, Sanofi Genzyme, Bayer, Janssen, and Horizon; received grant/research support from these same agencies; and serves on editorial boards for BMJ Neurology, Children, CNS Drugs, MS International, and Frontiers Epidemiology. BVW received research and travel grants and honoraria for advisory and speaking fees from Bayer Schering, Biogen, Sanofi Genzyme, Merck, Novartis, Roche, and Teva. TK served on scientific advisory boards for BMS, Roche, Sanofi Genzyme, Novartis, Merck, and Biogen, and the steering committee for the Brain Atrophy Initiative by Sanofi Genzyme; received conference travel support and/or speaker honoraria from WebMD Global, Novartis, Biogen, Sanofi Genzyme, Teva, BioCSL, and Merck; and received support for research or educational events from Biogen, Novartis, Genzyme, Roche, Celgene, and Merck., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Background and Objectives: To evaluate the rate of return of disease activity after cessation of multiple sclerosis (MS) disease-modifying therapy., Methods: This was a retrospective cohort study from 2 large observational MS registries: MSBase and OFSEP. Patients with relapsing-remitting MS who had ceased a disease-modifying therapy and were followed up for the subsequent 12 months were included in the analysis. The primary study outcome was annualized relapse rate in the 12 months after disease-modifying therapy discontinuation stratified by patients who did, and did not, commence a subsequent therapy. The secondary endpoint was the predictors of first relapse and disability accumulation after treatment discontinuation., Results: A total of 14,213 patients, with 18,029 eligible treatment discontinuation epochs, were identified for 7 therapies. Annualized rates of relapse (ARRs) started to increase 2 months after natalizumab cessation (month 2-4 ARR 0.47, 95% CI 0.43-0.51). Commencement of a subsequent therapy within 2-4 months reduced the magnitude of disease reactivation (mean ARR difference: 0.15, 0.08-0.22). After discontinuation of fingolimod, rates of relapse increased overall (month 1-2 ARR: 0.80, 0.70-0.89) and stabilized faster in patients who started a new therapy within 1-2 months (mean ARR difference: 0.14, -0.01 to 0.29). The magnitude of disease reactivation for other therapies was low but reduced further by commencement of another treatment 1-10 months after treatment discontinuation. Predictors of relapse were a higher relapse rate in the year before cessation, female sex, younger age, and higher EDSS score. Commencement of a subsequent therapy reduced both the risk of relapse (HR 0.76, 95% CI 0.72-0.81) and disability accumulation (0.73, 0.65-0.80)., Discussion: The rate of disease reactivation after treatment cessation differs among MS treatments, with the peaks of relapse activity ranging from 1 to 10 months in untreated cohorts that discontinued different therapies. These results suggest that untreated intervals should be minimized after stopping antitrafficking therapies (natalizumab and fingolimod)., Classification of Evidence: This study provides Class III that disease reactivation occurs within months of discontinuation of MS disease-modifying therapies. The risk of disease activity is reduced by commencement of a subsequent therapy., (© 2022 American Academy of Neurology.)

Background and Purpose: The prevention of disability over the long term is the main treatment goal in multiple sclerosis (MS); however, randomized clinical trials evaluate only short-term treatment effects on disability. This study aimed to define criteria for 6-month confirmed disability progression events of MS with a high probability of resulting in sustained long-term disability worsening., Methods: In total, 14,802 6-month confirmed disability progression events were identified in 8741 patients from the global MSBase registry. For each 6-month confirmed progression event (13,321 in the development and 1481 in the validation cohort), a sustained progression score was calculated based on the demographic and clinical characteristics at the time of progression that were predictive of long-term disability worsening. The score was externally validated in the Cladribine Tablets Treating Multiple Sclerosis Orally (CLARITY) trial., Results: The score was based on age, sex, MS phenotype, relapse activity, disability score and its change from baseline, number of affected functional system domains and worsening in six of the domains. In the internal validation cohort, a 61% lower chance of improvement was estimated with each unit increase in the score (hazard ratio 0.39, 95% confidence interval 0.29-0.52; discriminatory index 0.89). The proportions of progression events sustained at 5 years stratified by the score were 1: 72%; 2: 88%; 3: 94%; 4: 100%. The results of the CLARITY trial were confirmed for reduction of disability progression that was &gt;88% likely to be sustained (events with score ˃1.5)., Conclusions: Clinicodemographic characteristics of 6-month confirmed disability progression events identify those at high risk of sustained long-term disability. This knowledge will allow future trials to better assess the effect of therapy on long-term disability accrual., (© 2022 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Background: A delayed onset of treatment effect, termed therapeutic lag, may influence the assessment of treatment response in some patient subgroups., Objectives: The objective of this study is to explore the associations of patient and disease characteristics with therapeutic lag on relapses and disability accumulation., Methods: Data from MSBase, a multinational multiple sclerosis (MS) registry, and OFSEP, the French MS registry, were used. Patients diagnosed with MS, minimum 1 year of exposure to MS treatment and 3 years of pre-treatment follow-up, were included in the analysis. Studied outcomes were incidence of relapses and disability accumulation. Therapeutic lag was calculated using an objective, validated method in subgroups stratified by patient and disease characteristics. Therapeutic lag under specific circumstances was then estimated in subgroups defined by combinations of clinical and demographic determinants., Results: High baseline disability scores, annualised relapse rate (ARR) ⩾ 1 and male sex were associated with longer therapeutic lag on disability progression in sufficiently populated groups: females with expanded disability status scale (EDSS) < 6 and ARR < 1 had mean lag of 26.6 weeks (95% CI = 18.2-34.9), males with EDSS < 6 and ARR < 1 31.0 weeks (95% CI = 25.3-36.8), females with EDSS < 6 and ARR ⩾ 1 44.8 weeks (95% CI = 24.5-65.1), and females with EDSS ⩾ 6 and ARR < 1 54.3 weeks (95% CI = 47.2-61.5)., Conclusions: Pre-treatment EDSS and ARR are the most important determinants of therapeutic lag.

Background: Cerebellar and brainstem symptoms are common in early stages of multiple sclerosis (MS) yet their prognostic values remain unclear., Objective: The aim of this study was to investigate long-term disability outcomes in patients with early cerebellar and brainstem symptoms., Methods: This study used data from MSBase registry. Patients with early cerebellar/brainstem presentations were identified as those with cerebellar/brainstem relapse(s) or functional system score ⩾ 2 in the initial 2 years. Early pyramidal presentation was chosen as a comparator. Andersen-Gill models were used to compare cumulative hazards of (1) disability progression events and (2) relapses between patients with and without early cerebellar/brainstem symptoms. Mixed effect models were used to estimate the associations between early cerebellar/brainstem presentations and expanded disability status scale (EDSS) scores., Results: The study cohort consisted of 10,513 eligible patients, including 2723 and 3915 patients with early cerebellar and brainstem symptoms, respectively. Early cerebellar presentation was associated with greater hazard of progression events (HR = 1.37, p  < 0.001) and EDSS (β = 0.16, p  < 0.001). Patients with early brainstem symptoms had lower hazard of progression events (HR = 0.89, p  = 0.01) and EDSS (β = -0.06, p  < 0.001). Neither presentation was associated with changes in relapse risk., Conclusion: Early cerebellar presentation is associated with unfavourable outcomes, while early brainstem presentation is associated with favourable prognosis. These presentations may be used as MS prognostic markers and guide therapeutic approach.

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., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Patients with the 'aggressive' form of multiple sclerosis accrue disability at an accelerated rate, typically reaching Expanded Disability Status Score (EDSS) ≥ 6 within 10 years of symptom onset. Several clinicodemographic factors have been associated with aggressive multiple sclerosis, but less research has focused on clinical markers that are present in the first year of disease. The development of early predictive models of aggressive multiple sclerosis is essential to optimize treatment in this multiple sclerosis subtype. We evaluated whether patients who will develop aggressive multiple sclerosis can be identified based on early clinical markers. We then replicated this analysis in an independent cohort. Patient data were obtained from the MSBase observational study. Inclusion criteria were (i) first recorded disability score (EDSS) within 12 months of symptom onset; (ii) at least two recorded EDSS scores; and (iii) at least 10 years of observation time, based on time of last recorded EDSS score. Patients were classified as having 'aggressive multiple sclerosis' if all of the following criteria were met: (i) EDSS ≥ 6 reached within 10 years of symptom onset; (ii) EDSS ≥ 6 confirmed and sustained over ≥6 months; and (iii) EDSS ≥ 6 sustained until the end of follow-up. Clinical predictors included patient variables (sex, age at onset, baseline EDSS, disease duration at first visit) and recorded relapses in the first 12 months since disease onset (count, pyramidal signs, bowel-bladder symptoms, cerebellar signs, incomplete relapse recovery, steroid administration, hospitalization). Predictors were evaluated using Bayesian model averaging. Independent validation was performed using data from the Swedish Multiple Sclerosis Registry. Of the 2403 patients identified, 145 were classified as having aggressive multiple sclerosis (6%). Bayesian model averaging identified three statistical predictors: age > 35 at symptom onset, EDSS ≥ 3 in the first year, and the presence of pyramidal signs in the first year. This model significantly predicted aggressive multiple sclerosis [area under the curve (AUC) = 0.80, 95% confidence intervals (CIs): 0.75, 0.84, positive predictive value = 0.15, negative predictive value = 0.98]. The presence of all three signs was strongly predictive, with 32% of such patients meeting aggressive disease criteria. The absence of all three signs was associated with a 1.4% risk. Of the 556 eligible patients in the Swedish Multiple Sclerosis Registry cohort, 34 (6%) met criteria for aggressive multiple sclerosis. The combination of all three signs was also predictive in this cohort (AUC = 0.75, 95% CIs: 0.66, 0.84, positive predictive value = 0.15, negative predictive value = 0.97). Taken together, these findings suggest that older age at symptom onset, greater disability during the first year, and pyramidal signs in the first year are early indicators of aggressive multiple sclerosis., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Recent studies on date of birth of multiple sclerosis (MS) patients showed an association between month of birth and the risk of developing MS. This association has not been investigated in an African country., Objective: We aimed to determine if the risk of MS is associated with month of birth in Tunisia., Methods: Data concerning date of birth for MS patients in Tunisia (n = 1912) was obtained. Birth rates of MS patients were compared with all births in Tunisia matched by year of birth (n = 11,615,912). We used a chi-squared analysis and the Hewitt's non-parametric test for seasonality., Results: The distribution of births among MS patients compared with the control population was not different when tested by the chi-squared test. The Hewitt's test for seasonality showed an excess of births between May and October among MS patients (p = 0.03). The peak of Births of MS patients in Tunisia was in July and the nadir in December., Conclusion: Our data does support the seasonality hypothesis of month of birth as risk factor for MS in Tunisia. Low vitamin D levels during pregnancy could be a possible explanation that needs further investigation., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background: Few epidemiological data are available on multiple sclerosis (MS) patients in North Africa (NA). Studies of immigrants from NA showed a more aggressive course compared to European patients., Objective: The aim of this study is to describe clinical and long term course characteristics of MS in Tunisia and to compare it to European cohorts., Method: A total of 437 MS patients from three hospital based cohorts in Tunisia and having prospective follow up between 2010 and 2012 were analyzed. We considered as endpoints the time to reach EDSS scores of 3, 4 and 6 in the different clinical forms of MS and the beginning of a secondary progressive (SP) phase., Results: Sex ratio was 2.34. Mean age of onset was 30.3 years. The course was relapsing-remitting (RR) in 91% of patients and primary progressive (PP) in 9%. The most frequent isolated onset symptoms were respectively motor (28%), optic neuritis (20%) and sensory (16%) dysfunction. Median time to SP onset was 19.1 years. Median times from onset of multiple sclerosis to assignment of a score of 3, 4 and 6 were 8, 10.7 and 15 years respectively. Benign form of MS represented 31.5%. Median interval from the onset of the disease to EDSS score of 3, 4 and 6 was shorter in PP-MS than in RR-MS. However, there was no difference between these two groups for the median time from the assignment of EDSS 4 to the assignment EDSS 6., Conclusions: Our study shows that Tunisian MS patients have a quite similar clinical feature to European patients. Still, larger MS multicenter cohort studies in NA with longer follow-up duration could clearly respond to the issue., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Background: The prevalence of psychiatric disturbance for patients with multiple sclerosis (MS) is higher than that observed in other chronic health conditions. We report three cases of MS and bipolar disorder and we discuss the possible etiological hypothesis and treatment options., Observations: All patients fulfilled the McDonald criteria for MS. Two patients were followed up in psychiatry for manic or depressive symptoms before developing MS. A third patient was diagnosed with MS and developed deferred psychotic symptoms. Some clinical and radiological features are highlighted in our patients: one manic episode induced by high dose corticosteroids and one case of a new orbitofrontal MRI lesion concomitant with the emergence of psychiatric symptoms. All patients needed antipsychotic treatment with almost good tolerance for high dose corticosteroids and interferon beta treatment., Conclusions: MRI lesions suggest the possible implication of local MS-related brain damage in development of pure "psychiatric fits" in MS. Genetic susceptibility is another hypothesis for this association. We have noticed that interferon beta treatments were well tolerated while high dose corticosteroids may induce manic fits.