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Aim: To evaluate the real-world comparative effectiveness and the cost-effectiveness, from a UK National Health Service perspective, of natalizumab versus fingolimod in patients with rapidly evolving severe relapsing-remitting multiple sclerosis (RES-RRMS)., Methods: Real-world data from the MSBase Registry were obtained for patients with RES-RRMS who were previously either naive to disease-modifying therapies or had been treated with interferon-based therapies, glatiramer acetate, dimethyl fumarate, or teriflunomide (collectively known as BRACETD). Matched cohorts were selected by 3-way multinomial propensity score matching, and the annualized relapse rate (ARR) and 6-month-confirmed disability worsening (CDW6M) and improvement (CDI6M) were compared between treatment groups. Comparative effectiveness results were used in a cost-effectiveness model comparing natalizumab and fingolimod, using an established Markov structure over a lifetime horizon with health states based on the Expanded Disability Status Scale. Additional model data sources included the UK MS Survey 2015, published literature, and publicly available sources., Results: In the comparative effectiveness analysis, we found a significantly lower ARR for patients starting natalizumab compared with fingolimod (rate ratio [RR] = 0.65; 95% confidence interval [CI], 0.57-0.73) or BRACETD (RR = 0.46; 95% CI, 0.42-0.53). Similarly, CDI6M was higher for patients starting natalizumab compared with fingolimod (hazard ratio [HR] = 1.25; 95% CI, 1.01-1.55) and BRACETD (HR = 1.46; 95% CI, 1.16-1.85). In patients starting fingolimod, we found a lower ARR (RR = 0.72; 95% CI, 0.65-0.80) compared with starting BRACETD, but no difference in CDI6M (HR = 1.17; 95% CI, 0.91-1.50). Differences in CDW6M were not found between the treatment groups. In the base-case cost-effectiveness analysis, natalizumab dominated fingolimod (0.302 higher quality-adjusted life-years [QALYs] and £17,141 lower predicted lifetime costs). Similar cost-effectiveness results were observed across sensitivity analyses., Conclusions: This MSBase Registry analysis suggests that natalizumab improves clinical outcomes when compared with fingolimod, which translates to higher QALYs and lower costs in UK patients with RES-RRMS.

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.)

Importance: Natalizumab cessation is associated with a risk of rebound disease activity. It is important to identify the optimal switch disease-modifying therapy strategy after natalizumab to limit the risk of severe relapses., Objectives: To compare the effectiveness and persistence of dimethyl fumarate, fingolimod, and ocrelizumab among patients with relapsing-remitting multiple sclerosis (RRMS) who discontinued natalizumab., Design, Setting, and Participants: In this observational cohort study, patient data were collected from the MSBase registry between June 15, 2010, and July 6, 2021. The median follow-up was 2.7 years. This was a multicenter study that included patients with RRMS who had used natalizumab for 6 months or longer and then were switched to dimethyl fumarate, fingolimod, or ocrelizumab within 3 months after natalizumab discontinuation. Patients without baseline data were excluded from the analysis. Data were analyzed from May 24, 2022, to January 9, 2023., Exposures: Dimethyl fumarate, fingolimod, and ocrelizumab., Main Outcomes and Measures: Primary outcomes were annualized relapse rate (ARR) and time to first relapse. Secondary outcomes were confirmed disability accumulation, disability improvement, and subsequent treatment discontinuation, with the comparisons for the first 2 limited to fingolimod and ocrelizumab due to the small number of patients taking dimethyl fumarate. The associations were analyzed after balancing covariates using an inverse probability of treatment weighting method., Results: Among 66 840 patients with RRMS, 1744 had used natalizumab for 6 months or longer and were switched to dimethyl fumarate, fingolimod, or ocrelizumab within 3 months of natalizumab discontinuation. After excluding 358 patients without baseline data, a total of 1386 patients (mean [SD] age, 41.3 [10.6] years; 990 female [71%]) switched to dimethyl fumarate (138 [9.9%]), fingolimod (823 [59.4%]), or ocrelizumab (425 [30.7%]) after natalizumab. The ARR for each medication was as follows: ocrelizumab, 0.06 (95% CI, 0.04-0.08); fingolimod, 0.26 (95% CI, 0.12-0.48); and dimethyl fumarate, 0.27 (95% CI, 0.12-0.56). The ARR ratio of fingolimod to ocrelizumab was 4.33 (95% CI, 3.12-6.01) and of dimethyl fumarate to ocrelizumab was 4.50 (95% CI, 2.89-7.03). Compared with ocrelizumab, the hazard ratio (HR) of time to first relapse was 4.02 (95% CI, 2.83-5.70) for fingolimod and 3.70 (95% CI, 2.35-5.84) for dimethyl fumarate. The HR of treatment discontinuation was 2.57 (95% CI, 1.74-3.80) for fingolimod and 4.26 (95% CI, 2.65-6.84) for dimethyl fumarate. Fingolimod use was associated with a 49% higher risk for disability accumulation compared with ocrelizumab. There was no significant difference in disability improvement rates between fingolimod and ocrelizumab., Conclusion and Relevance: Study results show that among patients with RRMS who switched from natalizumab to dimethyl fumarate, fingolimod, or ocrelizumab, ocrelizumab use was associated with the lowest ARR and discontinuation rates, and the longest time to first relapse.

Background: The prognostic significance of non-disabling relapses in people with relapsing-remitting multiple sclerosis (RRMS) is unclear., Objective: To determine whether early non-disabling relapses predict disability accumulation in RRMS., Methods: We redefined mild relapses in MSBase as 'non-disabling', and moderate or severe relapses as 'disabling'. We used mixed-effects Cox models to compare 90-day confirmed disability accumulation events in people with exclusively non-disabling relapses within 2 years of RRMS diagnosis to those with no early relapses; and any early disabling relapses. Analyses were stratified by disease-modifying therapy (DMT) efficacy during follow-up., Results: People who experienced non-disabling relapses within 2 years of RRMS diagnosis accumulated more disability than those with no early relapses if they were untreated ( n  = 285 vs 4717; hazard ratio (HR) = 1.29, 95% confidence interval (CI) = 1.00-1.68) or given platform DMTs ( n  = 1074 vs 7262; HR = 1.33, 95% CI = 1.15-1.54), but not if given high-efficacy DMTs ( n  = 572 vs 3534; HR = 0.90, 95% CI = 0.71-1.13) during follow-up. Differences in disability accumulation between those with early non-disabling relapses and those with early disabling relapses were not confirmed statistically., Conclusion: This study suggests that early non-disabling relapses are associated with a higher risk of disability accumulation than no early relapses in RRMS. This risk may be mitigated by high-efficacy DMTs. Therefore, non-disabling relapses should be considered when making treatment decisions.

Background and Purpose: This study assessed the effect of patient characteristics on the response to disease-modifying therapy (DMT) in multiple sclerosis (MS)., Methods: We extracted data from 61,810 patients from 135 centers across 35 countries from the MSBase registry. The selection criteria were: clinically isolated syndrome or definite MS, follow-up ≥ 1 year, and Expanded Disability Status Scale (EDSS) score ≥ 3, with ≥1 score recorded per year. Marginal structural models with interaction terms were used to compare the hazards of 12-month confirmed worsening and improvement of disability, and the incidence of relapses between treated and untreated patients stratified by their characteristics., Results: Among 24,344 patients with relapsing MS, those on DMTs experienced 48% reduction in relapse incidence (hazard ratio [HR] = 0.52, 95% confidence interval [CI] = 0.45-0.60), 46% lower risk of disability worsening (HR = 0.54, 95% CI = 0.41-0.71), and 32% greater chance of disability improvement (HR = 1.32, 95% CI = 1.09-1.59). The effect of DMTs on EDSS worsening and improvement and the risk of relapses was attenuated with more severe disability. The magnitude of the effect of DMT on suppressing relapses declined with higher prior relapse rate and prior cerebral magnetic resonance imaging activity. We did not find any evidence for the effect of age on the effectiveness of DMT. After inclusion of 1985 participants with progressive MS, the effect of DMT on disability mostly depended on MS phenotype, whereas its effect on relapses was driven mainly by prior relapse activity., Conclusions: DMT is generally most effective among patients with lower disability and in relapsing MS phenotypes. There is no evidence of attenuation of the effect of DMT with age., (© 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

Background: In the absence of evidence from randomised controlled trials, observational data can be used to emulate clinical trials and guide clinical decisions. Observational studies are, however, susceptible to confounding and bias. Among the used techniques to reduce indication bias are propensity score matching and marginal structural models., Objective: To use the comparative effectiveness of fingolimod vs natalizumab to compare the results obtained with propensity score matching and marginal structural models., Methods: Patients with clinically isolated syndrome or relapsing remitting MS who were treated with either fingolimod or natalizumab were identified in the MSBase registry. Patients were propensity score matched, and inverse probability of treatment weighted at six monthly intervals, using the following variables: age, sex, disability, MS duration, MS course, prior relapses, and prior therapies. Studied outcomes were cumulative hazard of relapse, disability accumulation, and disability improvement., Results: 4608 patients (1659 natalizumab, 2949 fingolimod) fulfilled inclusion criteria, and were propensity score matched or repeatedly reweighed with marginal structural models. Natalizumab treatment was associated with a lower probability of relapse (PS matching: HR 0.67 [95% CI 0.62-0.80]; marginal structural model: 0.71 [0.62-0.80]), and higher probability of disability improvement (PS matching: 1.21 [1.02 -1.43]; marginal structural model 1.43 1.19 -1.72]). There was no evidence of a difference in the magnitude of effect between the two methods., Conclusions: The relative effectiveness of two therapies can be efficiently compared by either marginal structural models or propensity score matching when applied in clearly defined clinical contexts and in sufficiently powered cohorts.

Background: Patients with relapsing-remitting multiple sclerosis commonly switch between disease-modifying therapies (DMTs). Identifying predictors of relapse when switching could improve outcomes., Objective: To determine predictors of relapse hazard when switching to cladribine., Methods: Data of patients who switched to cladribine, grouped by prior disease-modifying therapy (pDMT; interferon-β/glatiramer acetate, dimethyl fumarate, teriflunomide, fingolimod or natalizumab (NTZ)), were extracted from the MSBase Registry. Predictors of relapse hazard during the treatment gap and the first year of cladribine therapy were determined., Results: Of 513 patients, 22 relapsed during the treatment gap, and 38 within 1 year of starting cladribine. Relapse in the year before pDMT cessation predicted treatment gap relapse hazard (hazard ratio (HR) = 2.43, 95% confidence interval (CI)  = 1.03-5.71). After multivariable adjustment, relapse hazard on cladribine was predicted by relapse before pDMT cessation (HR = 2.00, 95% CI = 1.01-4.02), treatment gap relapse (HR = 6.18, 95% confidence interval (CI) = 2.65-14.41), switch from NTZ (HR compared to injectable therapies 4.08, 95% CI = 1.35-12.33) and age at cladribine start (HR = 0.96, 95% CI = 0.91-0.99)., Conclusion: Relapse during or prior to the treatment gap, and younger age, are of prognostic relevance in the year after switching to cladribine. Switching from NTZ is also independently associated with greater relapse hazard.

Background: To compare the effectiveness and treatment persistence of ocrelizumab, cladribine and natalizumab in patients with relapsing-remitting multiple sclerosis switching from fingolimod., Methods: Using data from MSBase registry, this multicentre cohort study included subjects who had used fingolimod for ≥6 months and then switched to ocrelizumab, cladribine or natalizumab within 3 months after fingolimod discontinuation. We analysed relapse and disability outcomes after balancing covariates using an inverse-probability-treatment-weighting method. Propensity scores for the three treatments were obtained using multinomial-logistic regression. Due to the smaller number of cladribine users, comparisons of disability outcomes were limited to natalizumab and ocrelizumab., Results: Overall, 1045 patients switched to ocrelizumab (n=445), cladribine (n=76) or natalizumab (n=524) after fingolimod. The annualised relapse rate (ARR) for ocrelizumab was 0.07, natalizumab 0.11 and cladribine 0.25. Compared with natalizumab, the ARR ratio (95% confidence interval [CI]) was 0.67 (0.47 to 0.96) for ocrelizumab and 2.31 (1.30 to 4.10) for cladribine; the hazard ratio (95% CI) for time to first relapse was 0.57 (0.40 to 0.83) for ocrelizumab and 1.18 (0.47 to 2.93) for cladribine. Ocrelizumab users had an 89% lower discontinuation rate (95% CI, 0.07 to 0.20) than natalizumab, but also a 51% lower probability of confirmed disability improvement (95% CI, 0.32 to 0.73). There was no difference in disability accumulation., Conclusion: After fingolimod cessation, ocrelizumab and natalizumab were more effective in reducing relapses than cladribine. Due to the low ARRs in all three treatment groups, additional observation time is required to determine if statistical difference in ARRs results in long-term disability differences., Competing Interests: Competing interests: CZ, OS, RM, AP, JK, MO, RK, SO, DM, IR and ZZ report no disclosures. VJ received conference travel support from Merck and Roche and speaker’s honoraria from Biogen and Roche outside of the submitted work. She receives research support from the Australian National Health and Medical Research Grant and MS Research Australia. MB received conference travel support from Biogen and Novartis. His institution has received research support from Biogen, Merck and Novartis. TK received conference travel support and/or speaker honoraria from WebMD Global, Eisai, Novartis, Biogen, Sanofi-Genzyme, Teva, BioCSL and Merck and received research or educational event support from Biogen, Novartis, Genzyme, Roche, Celgene and Merck. JL-S received travel compensation from Novartis, Biogen, Roche and Merck. Her institution receives the honoraria for talks and advisory board commitment and research grants from Biogen, Merck, Roche, TEVA and Novartis. SJK received compensation for scientific advisory board activity from Merck and Roche. SH received honoraria and consulting fees from Novartis, Bayer Schering and Sanofi, and travel grants from Novartis, Biogen Idec and Bayer Schering. MS participated in, but not received honoraria for, advisory board activity for Biogen, Merck, Bayer Schering, Sanofi Aventis and Novartis. AvdW served on advisory boards and receives unrestricted research grants from Novartis, Biogen, Merck and Roche. She has received speaker’s honoraria and travel support from Novartis, Roche and Merck. She also received grant support from the National Health and Medical Research Council of Australia and MS Research Australia. VVP has received travel grants from Merck Healthcare KGaA (Darmstadt, Germany), Biogen, Sanofi, Bristol Meyer Squibb, Almirall and Roche. His institution has received research grants and consultancy fees from Roche, Biogen, Sanofi, Merck Healthcare KGaA (Darmstadt, Germany), Bristol Meyer Squibb, Janssen, Almirall and Novartis Pharma. JP received travel compensation from Novartis, Biogen, Genzyme and Teva, and speaking honoraria from Biogen, Novartis, Genzyme and Teva. DH received speaker honoraria and consulting fees from Biogen, Merck, Teva, Roche, Sanofi Genzyme and Novartis, as well as support for research activities from Biogen and Czech Ministry of Education. EKH received honoraria/research support from Biogen, Merck Serono, Novartis, Roche and Teva. GI received speaking honoraria from Biogen, Novartis, Sanofi, Merck, Roche, Almirall and Teva. FP received speaker honoraria and advisory board fees from Almirall, Bayer, Biogen, Celgene, Merck, Novartis, Roche, Sanofi-Genzyme and TEVA. He received research funding from Biogen, Merck, FISM (Fondazione Italiana Sclerosi Multipla), Reload Onlus Association and University of Catania. 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; he participated in Sanofi Aventis, Roche and Novartis clinical trials. MM has served on advisory board for Merck, has received speaker honoraria from Merck and Biogen. Her institution receives funding from Merck, Australian National Health Medical Research Council, Brain Foundation, Charles and Sylvia Viertel Foundation, Bethlehem Griffith Foundation and MS Research Australia. HB has received institutional (Monash University) funding from Biogen, F. Hoffmann-La Roche Ltd, Merck, Alexion, CSL and Novartis; has carried out contracted research for Novartis, Merck, F. Hoffmann-La Roche Ltd and Biogen; has taken part in speakers’ bureaus for Biogen, Genzyme, UCB, Novartis, F. Hoffmann-La Roche Ltd and Merck; has received personal compensation from Oxford Health Policy Forum for the Brain Health Steering Committee., (© Author(s) (or their employer(s)) 2022. 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: Natalizumab and fingolimod are used as high-efficacy treatments in relapsing-remitting multiple sclerosis. Several observational studies comparing these two drugs have shown variable results, using different methods to control treatment indication bias and manage censoring. The objective of this empirical study was to elucidate the impact of methods of causal inference on the results of comparative effectiveness studies., Methods: Data from three observational multiple sclerosis registries (MSBase, the Danish MS Registry and French OFSEP registry) were combined. Four clinical outcomes were studied. Propensity scores were used to match or weigh the compared groups, allowing for estimating average treatment effect for treated or average treatment effect for the entire population. Analyses were conducted both in intention-to-treat and per-protocol frameworks. The impact of the positivity assumption was also assessed., Results: Overall, 5,148 relapsing-remitting multiple sclerosis patients were included. In this well-powered sample, the 95% confidence intervals of the estimates overlapped widely. Propensity scores weighting and propensity scores matching procedures led to consistent results. Some differences were observed between average treatment effect for the entire population and average treatment effect for treated estimates. Intention-to-treat analyses were more conservative than per-protocol analyses. The most pronounced irregularities in outcomes and propensity scores were introduced by violation of the positivity assumption., Conclusions: This applied study elucidates the influence of methodological decisions on the results of comparative effectiveness studies of treatments for multiple sclerosis. According to our results, there are no material differences between conclusions obtained with propensity scores matching or propensity scores weighting given that a study is sufficiently powered, models are correctly specified and positivity assumption is fulfilled., (© 2022. The Author(s).)

Background: Increasingly, people with relapsing-remitting multiple sclerosis (RRMS) are switched to highly effective disease-modifying therapies (DMTs) such as ocrelizumab., Objective: To determine predictors of relapse and disability progression when switching from another DMT to ocrelizumab., Methods: Patients with RRMS who switched to ocrelizumab were identified from the MSBase Registry and grouped by prior disease-modifying therapy (pDMT; interferon-β/glatiramer acetate, dimethyl fumarate, teriflunomide, fingolimod or natalizumab) and washout duration (<1 month, 1-2 months or 2-6 months). Survival analyses including multivariable Cox proportional hazard regression models were used to identify predictors of on-ocrelizumab relapse within 1 year, and 6-month confirmed disability progression (CDP)., Results: After adjustment, relapse hazard when switching from fingolimod was greater than other pDMTs, but only in the first 3 months of ocrelizumab therapy (hazard ratio (HR) = 3.98, 95% confidence interval (CI) = 1.57-11.11, p  = 0.004). The adjusted hazard for CDP was significantly higher with longer washout (2-6 m compared to <1 m: HR = 9.57, 95% CI = 1.92-47.64, p  = 0.006)., Conclusion: The risk of disability worsening during switch to ocrelizumab is reduced by short treatment gaps. Patients who cease fingolimod are at heightened relapse risk in the first 3 months on ocrelizumab. Prospective evaluation of strategies such as washout reduction may help optimise this switch.

Introduction: Natalizumab has proved to be more effective than fingolimod in reducing disease activity in relapsing-remitting multiple sclerosis (RRMS). Whether this association is universal for all patient groups remains to be determined., Objective: The aim of this study was to compare the relative effectiveness of natalizumab and fingolimod in RRMS subgroups defined by the baseline demographic and clinical characteristics of interest., Methods: Patients with RRMS who were given natalizumab or fingolimod were identified in a merged cohort from three international registries. Efficacy outcomes were compared across subgroups based on patients' sex, age, disease duration, Expanded Disability Status Scale (EDSS) score, and disease and magnetic resonance imaging (MRI) activity 12 months prior to treatment initiation. Study endpoints were number of relapses (analyzed with weighted negative binomial generalized linear model) and 6-month confirmed disability worsening and improvement events (weighted Cox proportional hazards model), recorded during study therapy. Each patient was weighted using inverse probability of treatment weighting based on propensity score., Results: A total of 5148 patients (natalizumab 1989; fingolimod 3159) were included, with a mean ± standard deviation age at baseline of 38 ± 10 years, and the majority (72%) were women. The median on-treatment follow-up was 25 (quartiles 15-41) months. Natalizumab was associated with fewer relapses than fingolimod (incidence rate ratio [IRR]; 95% confidence interval [CI]) in women (0.76; 0.65-0.88); in those aged ≤ 38 years (0.64; 0.54-0.76); in those with disease duration ≤ 7 years (0.63; 0.53-0.76); in those with EDSS score < 4 (0.75; 0.64-0.88), < 6 (0.80; 0.70-0.91), and ≥ 6 (0.52; 0.31-0.86); and in patients with pre-baseline relapses (0.74; 0.64-0.86). A higher probability of confirmed disability improvement on natalizumab versus fingolimod (hazard ratio [HR]; 95% CI) was observed among women (1.36; 1.10-1.66); those aged > 38 years (1.34; 1.04-1.73); those with disease duration > 7 years (1.33; 1.01-1.74); those with EDSS score < 6 (1.21; 1.01-1.46) and ≥ 6 (1.93; 1.11-3.34); and patients with no new MRI lesion (1.73; 1.19-2.51)., Conclusions: Overall, in women, younger patients, those with shorter disease durations, and patients with pre-treatment relapses, natalizumab was associated with a lower frequency of multiple sclerosis relapses than fingolimod. It was also associated with an increased chance of recovery from disability among most patients, particularly women and those with no recent MRI activity., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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: Natalizumab and fingolimod were the first preparations recommended for disease breakthrough in priorly treated relapsing-remitting multiple sclerosis. Of three published head-to-head studies two showed that natalizumab is the more effective to prevent relapses and EDSS worsening., Methods: By re-analyzing original published results from MSBase, France, and Denmark using uniform methodologies, we aimed at identifying the effects of differences in methodology, in the MS-populations, and at re-evaluating the differences in effectiveness between the two drugs. We gained access to copies of the individual amended databases and pooled all data. We used uniform inclusion/exclusion criteria and statistical methods with Inverse Probability Treatment Weighting., Results: The pooled analyses comprised 968 natalizumab- and 1479 fingolimod treated patients. The on-treatment natalizumab/fingolimod relapse rate ratio was 0.77 (p=0.004). The hazard ratio (HR) for a first relapse was 0.82 (p=0.030), and the HR for sustained EDSS improvement was 1.4 (p=0.009). There were modest differences between each of the original published studies and the replication study, but the conclusions of the three original studies remained unchanged: in two of them natalizumab was more effective, but in the third there was no difference between natalizumab and fingolimod., Conclusion: The results were largely invariant to the epidemiological and statistical methods but differed between the MS populations. Generally, the advantage of natalizumab was confirmed., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Objective: To test the hypothesis that immunotherapy prevents long-term disability in relapsing-remitting multiple sclerosis (MS), we modeled disability outcomes in 14,717 patients., Methods: We studied patients from MSBase followed for ≥1 year, with ≥3 visits, ≥1 visit per year, and exposed to MS therapy, and a subset of patients with ≥15-year follow-up. Marginal structural models were used to compare the cumulative hazards of 12-month confirmed increase and decrease in disability, Expanded Disability Status Scale (EDSS) step 6, and the incidence of relapses between treated and untreated periods. Marginal structural models were continuously readjusted for patient age, sex, pregnancy, date, disease course, time from first symptom, prior relapse history, disability, and MRI activity., Results: A total of 14,717 patients were studied. During the treated periods, patients were less likely to experience relapses (hazard ratio 0.60, 95% confidence interval [CI] 0.43-0.82, p = 0.0016), worsening of disability (0.56, 0.38-0.82, p = 0.0026), and progress to EDSS step 6 (0.33, 0.19-0.59, p = 0.00019). Among 1,085 patients with ≥15-year follow-up, the treated patients were less likely to experience relapses (0.59, 0.50-0.70, p = 10 -9 ) and worsening of disability (0.81, 0.67-0.99, p = 0.043)., Conclusion: Continued treatment with MS immunotherapies reduces disability accrual by 19%-44% (95% CI 1%-62%), the risk of need of a walking aid by 67% (95% CI 41%-81%), and the frequency of relapses by 40-41% (95% CI 18%-57%) over 15 years. This study provides evidence that disease-modifying therapies are effective in improving disability outcomes in relapsing-remitting MS over the long term., Classification of Evidence: This study provides Class IV evidence that, for patients with relapsing-remitting MS, long-term exposure to immunotherapy prevents neurologic disability., (© 2020 American Academy of Neurology.)

Background: The risk factors for conversion from relapsing-remitting to secondary progressive multiple sclerosis remain highly contested., Objective: The aim of this study was to determine the demographic, clinical and paraclinical features that influence the risk of conversion to secondary progressive multiple sclerosis., Methods: Patients with adult-onset relapsing-remitting multiple sclerosis and at least four recorded disability scores were selected from MSBase, a global observational cohort. The risk of conversion to objectively defined secondary progressive multiple sclerosis was evaluated at multiple time points per patient using multivariable marginal Cox regression models. Sensitivity analyses were performed., Results: A total of 15,717 patients were included in the primary analysis. Older age (hazard ratio (HR) = 1.02, p  < 0.001), longer disease duration (HR = 1.01, p  = 0.038), a higher Expanded Disability Status Scale score (HR = 1.30, p  < 0.001), more rapid disability trajectory (HR = 2.82, p  < 0.001) and greater number of relapses in the previous year (HR = 1.07, p  = 0.010) were independently associated with an increased risk of secondary progressive multiple sclerosis. Improving disability (HR = 0.62, p  = 0.039) and disease-modifying therapy exposure (HR = 0.71, p  = 0.007) were associated with a lower risk. Recent cerebral magnetic resonance imaging activity, evidence of spinal cord lesions and oligoclonal bands in the cerebrospinal fluid were not associated with the risk of conversion., Conclusion: Risk of secondary progressive multiple sclerosis increases with age, duration of illness and worsening disability and decreases with improving disability. Therapy may delay the onset of secondary progression.

Objective: Oral immunotherapies have become a standard treatment in relapsing-remitting multiple sclerosis. Direct comparison of their effect on relapse and disability is needed., Methods: We identified all patients with relapsing-remitting multiple sclerosis treated with teriflunomide, dimethyl fumarate or fingolimod, with minimum 3-month treatment persistence and disability follow-up in the global MSBase cohort study. Patients were matched using propensity scores. Three pairwise analyses compared annualised relapse rates and hazards of disability accumulation, disability improvement and treatment discontinuation (analysed with negative binomial models and weighted conditional survival models, with pairwise censoring)., Results: The eligible cohorts consisted of 614 (teriflunomide), 782 (dimethyl fumarate) or 2332 (fingolimod) patients, followed over the median of 2.5 years. Annualised relapse rates were lower on fingolimod compared with teriflunomide (0.18 vs 0.24; p=0.05) and dimethyl fumarate (0.20 vs 0.26; p=0.01) and similar on dimethyl fumarate and teriflunomide (0.19 vs 0.22; p=0.55). No differences in disability accumulation (p≥0.59) or improvement (p≥0.14) were found between the therapies. In patients with ≥3-month treatment persistence, subsequent discontinuations were less likely on fingolimod than teriflunomide and dimethyl fumarate (p<0.001). Discontinuation rates on teriflunomide and dimethyl fumarate were similar (p=0.68)., Conclusion: The effect of fingolimod on relapse frequency was superior to teriflunomide and dimethyl fumarate. The effect of the three oral therapies on disability outcomes was similar during the initial 2.5 years on treatment. Persistence on fingolimod was superior to the two comparator drugs., Competing Interests: Competing interests: TK served on scientific advisory boards for Roche, Genzyme-Sanofi, Novartis, Merck and Biogen, steering committee for Brain Atrophy Initiative by Genzyme, received conference travel support and/or speaker honoraria from WebMD Global, Novartis, Biogen, Genzyme-Sanofi, Teva, BioCSL and Merck and received research support from Biogen. EKH received speaker honoraria and consultant fees from Actelion, Biogen, Celgene, Merck, Novartis, Roche, Sanofi and Teva, and support for research activities from Czech Ministry of Education (project PROGRES Q27/LF1). DH received speaker honoraria and consulting fees from Biogen, Merck, Teva, Roche, Sanofi Genzyme and Novartis, as well as support for research activities from Biogen and Czech Ministry of Education (project PROGRES Q27/LF1). GI received speaking honoraria from Biogen, Novartis, Sanofi, Merck, Roche, Almirall and Teva. MG received consulting fees from Teva Canada Innovation, Biogen, Novartis and Genzyme Sanofi; lecture payments from Teva Canada Innovation, Novartis and EMD. He has also received a research grant from Canadian Institutes of Health Research. PD served on editorial boards and has been supported to attend meetings by EMD, Biogen, Novartis, Genzyme and TEVA Neuroscience. He holds grants from the CIHR and the MS Society of Canada and has received funding for investigator-initiated trials from Biogen, Novartis and Genzyme. PG is a Merck, Novartis, Teva-Neuroscience, Biogen and Genzyme advisory board member, consultant for Merck, received payments for lectures by Merck, Teva-Neuroscience and Canadian Multiple Sclerosis Society, and received grants for travel from Teva-Neuroscience and Novartis. AL served as a Bayer, Biogen, Merck, Novartis, Roche, Sanofi/Genzyme and Teva Advisory Board Member. She received congress and travel/accommodation expense compensations and speaker honoraria from Bayer, Biogen, Merck, Novartis, Sanofi/Genzyme, Teva and Fondazione Italiana Sclerosi Multipla (FISM). Her institutions received research grants from Novartis. LK received research support from Acorda, Actelion, Allozyne, BaroFold, Bayer Health Care, 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, Swiss MS Society, Swiss National Research Foundation, Teva Neuroscience, UCB and Wyeth. JK received consulting fees from Novartis, Protagen; speaker fees from the Swiss MS Society, Biogen, Novartis, Roche, Genzyme; travel expenses from Merck Serono, Novartis; grants from ECTRIMS Research Fellowship Programme, University of Basel, Swiss MS Society, Swiss National Research Foundation, Bayer (Schweiz), Genzyme and Novartis. MT received travel grants from Novartis, Bayer Schering, Merck and Teva; has participated in clinical trials by Sanofi Aventis, Roche and Novartis. JLS accepted travel compensation from Novartis, Biogen and Merck. Her institution receives the honoraria for talks and advisory board commitment from Bayer Health Care, Biogen, Genzyme Sanofi, Merck, Novartis and Teva; has been involved in clinical trials with Biogen, Novartis and Teva. CB received conference travel support from Biogen, Novartis, Bayer Schering, Merck and Teva; has participated in clinical trials by Sanofi Aventis, Roche and Novartis. FGM received honoraria or research funding from Biogen, Genzyme, Novartis, Teva Neurosciences, Mitsubishi and ONO Pharmaceuticals. JP accepted travel compensation from Novartis, Biogen, Genzyme and Teva and speaking honoraria from Biogen, Novartis, Genzyme and Teva. PS served on scientific advisory boards for Biogen Idec and TEVA; she has received funding for travel and speaker honoraria from Biogen Idec, Merck, Teva, Sanofi Genzyme, Novartis and Bayer and 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. FG received an institutional research grant from Biogen and Sanofi Genzyme; served on scientific advisory boards for Biogen, Novartis, Merck, Sanofi Genzyme and Roche; received funding for travel and speaker honoraria from Biogen, Merck and Sanofi-Aventis. MT received speaker honoraria from Biogen-Idec, Bayer Schering, Sanofi Aventis, Merck, Teva, Novartis and Almirall; has received research grants for her Institution from Biogen-Idec, Merck 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; congress and travel/accommodation expense compensations by Almirall, Bayer Schering, Biogen, Genzyme, Merck, Novartis, Sanofi-Aventis and Teva. EP served on scientific advisory boards for Merck, Genzyme and Biogen; he has received honoraria and travel grants from Sanofi Aventis, Novartis, Biogen, Merck, Genzyme and Teva; he has received travel grants and equipment from 'Associazione Marchigiana Sclerosi Multipla e altre malattie neurologiche'. VVP received travel grants from Biogen, Bayer Schering, Genzyme, Merck, Teva and Novartis Pharma. His institution receives honoraria for consultancy and lectures from Biogen, Bayer Schering, Genzyme, Merck, Roche, Teva and Novartis Pharma as well as research grants from Novartis Pharma and Bayer Schering. BVW received research and travel grants, honoraria for MS-Expert advisor and speaker fees from Bayer Schering, Biogen, Sanofi Genzyme, Merck, Novartis, Roche and Teva. CS served on scientific advisory boards for Merck, Genzyme, Almirall and Biogen; received honoraria and travel grants from Sanofi Aventis, Novartis, Biogen, Merck, Genzyme and Teva. CRT received research funding, compensation for travel or speaker honoraria from Biogen, Novartis, Genzyme and Almirall. MS has participated in, but not received honoraria for, advisory board activity for Biogen, Merck, Bayer Schering, Sanofi Aventis 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. DLS received honoraria as a consultant on scientific advisory boards by Bayer Schering, Novartis and Sanofi-Aventis and compensation for travel from Novartis, Biogen, Sanofi Aventis, Teva and Merck. JLSM accepted travel compensation from Novartis and Biogen, speaking honoraria from Biogen, Novartis, Sanofi, Merck, Almirall, Bayer and Teva and has participated in a clinical trial by Biogen. RA received conference travel support from Novartis, Teva, Biogen, Bayer and Merck and has participated in a clinical trial by Biogen, Novartis, Teva and Actelion. SH received honoraria and consulting fees from Merck, Novartis, Bayer Schering and Sanofi, and travel grants from Novartis, Biogen Idec and Bayer Schering. Ricardo Fernandez Bolaños received speaking honoraria from Biogen, Novartis, Merck and Teva. VJ received conference travel support from Teva, Novartis and Merck, and speaker honoraria from Biogen. TS received honoraria for consultancy, funding for travel and compensation for serving on scientific advisory boards from Biogen and speaker honoraria from Novartis. HB served on scientific advisory boards for Biogen, Novartis and Sanofi-Aventis and has received conference travel support from Novartis, Biogen and Sanofi Aventis. He serves on steering committees for trials conducted by Biogen and Novartis, and has received research support from Merck, Novartis and Biogen., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Exposure to disease-modifying therapy (DMT) during early pregnancy in women with relapsing-remitting MS (RRMS) may be increasing., Objective: To retrospectively determine incidence of pregnancy, DMT exposure and pregnancy outcomes in women with RRMS., Methods: We identified all women with RRMS aged 15-45 years in the MSBase Registry between 2005-2016. Annualised pregnancy incidence rates were calculated using Poisson regression models. DMT exposures and pregnancy outcomes were assessed., Results: Of 9,098 women meeting inclusion criteria, 1,178 (13%) women recorded 1,521 pregnancies. The annualised incidence rate of pregnancy was 0.042 (95% CI 0.040, 0.045). A total of 635 (42%) reported pregnancies were conceived on DMT, increasing from 27% in 2006 to 62% in 2016. The median duration of DMT exposure during pregnancy was 30 days (IQR: 9, 50). There were a higher number of induced abortions on FDA pregnancy class C/D drugs compared with pregnancy class B and no DMT (p = 0.010); but no differences in spontaneous abortions, term or preterm births., Conclusions: We report low pregnancy incidence rates, with increasing number of pregnancies conceived on DMT over the past 12-years. The median duration of DMT exposure in pregnancy was relatively short at one month., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Objective: This propensity score-matched analysis from MSBase compared the effectiveness of cladribine with interferon β, fingolimod or natalizumab., Methods: We identified all patients with relapse-onset multiple sclerosis, exposure to the study therapies and ⩾1-year on-treatment follow-up from MSBase. Three pairwise propensity score-matched analyses compared treatment outcomes over 1 year. The outcomes were hazards of first relapse, disability accumulation and disability improvement events. Sensitivity analyses were completed., Results: The cohorts consisted of 37 (cladribine), 1940 (interferon), 1892 (fingolimod) and 1410 patients (natalizumab). The probability of experiencing a relapse on cladribine was lower than on interferon ( p = 0.05), similar to fingolimod ( p = 0.31) and higher than on natalizumab ( p = 0.042). The probability of disability accumulation on cladribine was similar to interferon ( p = 0.37) and fingolimod ( p = 0.089) but greater than natalizumab ( p = 0.021). The probability of disability improvement was higher on cladribine than interferon ( p = 0.00017), fingolimod ( p = 0.0025) or natalizumab ( p = 0.00099). Sensitivity analyses largely confirmed the above results., Conclusion: Cladribine is an effective therapy for relapse-onset multiple sclerosis. Its effect on relapses is comparable to fingolimod and its effect on disability accrual is comparable to interferon β and fingolimod. Cladribine may potentially associate with superior recovery from disability relative to interferon, fingolimod and natalizumab.