Emulating randomised clinical trials in relapsing-remitting multiple sclerosis with non-randomised real-world evidence: an application using data from the MSBase Registry.

BACKGROUND: To mimic as closely as possible a randomised controlled trial (RCT) and calibrate the real-world evidence (RWE) studies against a known treatment effect would be helpful to understand if RWE can support causal conclusions in selected circumstances. The aim was to emulate the TRANSFORMS trial comparing Fingolimod (FTY) versus intramuscular interferon ß-1a (IFN) using observational data. METHODS: We extracted from the MSBase registry all the patients with relapsing-remitting multiple sclerosis (RRMS) collected in the period 2011-2021 who received IFN or FTY (0.5 mg) and with the same inclusion and exclusion criteria of the TRANSFORMS RCT. The primary endpoint was the annualised relapse rate (ARR) over 12 months. Patients were 1:1 propensity-score (PS) matched. Relapse-rate ratio (RR) was calculated by mean of a negative binomial regression. RESULTS: A total of 4376 patients with RRMS (1140 in IFN and 3236 in FTY) were selected. After PS, 856 patients in each group were matched. The ARR was 0.45 in IFN and 0.25 in FTY with a significant difference between the two groups (RR: 0.55, 95% CI: 0.45 to 0.68; p<0.001). The result of the emulation was very similar and fell within the 95% CI of that observed in the RCT (RR: 0.49, 95% CI: 0.37 to 0.64; p<0.001) with a standardised difference of 0.66 (p=0.51). CONCLUSIONS: By applying the same inclusion and exclusion criteria used in the RCT and employing appropriate methodology, we successfully replicated the RCT results with only minor discrepancies. Also, even if the confounding bias cannot be fully eliminated, conducting a rigorous target trial emulation could still yield valuable insights for comparative effectiveness research.

Predictors of relapse risk and treatment response in AQP4-IgG positive and seronegative NMOSD: A multicentre study.

BACKGROUND: Neuromyelitis optica spectrum disorder (NMOSD) can be categorised into aquaporin-4 antibody (AQP4-IgG) NMOSD or seronegative NMOSD. While our knowledge of AQP4-IgG NMOSD has evolved significantly in the past decade, seronegative NMOSD remains less understood. This study aimed to evaluate the predictors of relapses and treatment responses in AQP4-IgG NMOSD and seronegative NMOSD. METHODS: This was a multicentre, international, retrospective cohort study using the MSBase registry. Recurrent relapse risk was assessed using an Andersen-Gill model and risk of first relapse was evaluated using a Cox proportional hazards model. Covariates that putatively influence relapse risk included demographic factors, clinical characteristics and immunosuppressive therapies; the latter was assessed as a time-varying covariate. RESULTS: A total of 398 patients (246 AQP4-IgG NMOSD and 152 seronegative NMOSD) were included. The AQP4-IgG NMOSD and seronegative NMOSD patients did not significantly differ by age at disease onset, ethnicity or annualised relapse rate. Both low-efficacy and high-efficacy immunosuppressive therapies were associated with significant reductions in recurrent relapse risk, with notably greater protection conferred by high-efficacy therapies in both AQP4-IgG NMOSD (HR 0.27, 95% CI 0.15 to 0.49, p<0.001) and seronegative NMOSD (HR 0.21, 95% CI 0.08 to 0.51, p<0.001). Longer disease duration (HR 0.97, 95% CI 0.95 to 0.99, p<0.001) and male sex (HR 0.52, 95% CI 0.34 to 0.84, p=0.007) were additional protective variables in reducing the recurrent relapse risk for the AQP4-IgG NMOSD group. CONCLUSION: Although further studies are needed to improve our understanding of seronegative NMOSD, our findings underscore the importance of aggressive treatment with high-efficacy immunotherapies in both NMOSD subtypes, regardless of serostatus.

Disability accrual in primary and secondary progressive multiple sclerosis.

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.

Comparing switch to ocrelizumab, cladribine or natalizumab after fingolimod treatment cessation in multiple sclerosis.

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.