Discontinuation of disease-modifying therapies (DMTs) for MS is common. MSBase, a large global observational registry, affords a unique opportunity to investigate predictors of ‘post-DMT’ relapses ...and confirmed disability progression (CDP) in a diverse group of patients exposed to different DMTs.
Main inclusion criteria: clinician-confirmed MS diagnosis (2010 McDonald criteria); age ≥ 18 at index DMT start; ≥12 months on index DMT prior to discontinuation; ≥24 months of follow-up post-discontinuation; did not restart DMT for ≥6 months. Predictors of time to first relapse and 3-month CDP were analyzed using Cox proportional hazards regression adjusted for age, gender, baseline EDSS, EDSS stability and relapse-free period for ≥1 year prior to discontinuation, calendar epoch, index DMT and reason for discontinuation.
4842 patients (74.2% female) from 20 MSBase Centers met our inclusion criteria. 3556 (73%) discontinued one of IFNβ preparations, 849 (18%) - glatiramer acetate, 308 (6%) - natalizumab and 129 (3%) – fingolimod; other DMTs were excluded because too few records were available. Overall post-discontinuation annualized relapse rate (95% CI) was 0.224 (0.219, 0.229) and CDP rate was 8.23 (7.72, 8.76) per 100 person-years. Risk of post-DMT relapse was higher in younger patients, female patients, those with moderate disability and a relapse within 1 year of discontinuation. Hazard of CDP increased with increasing disability at baseline and disease progression within 3 years prior to stopping DMT. Of all the DMTs, only natalizumab was associated with increased risk of both post-DMT relapse and CDP.
Knowledge of post-DMT relapse and disability progression rates and predictors of post-DMT disease activity allows for a more informed discussion of DMT discontinuation in those patients who are considering this option.
•4842 patients in MSBase Registry stopped disease modifying therapy (DMT) for >6 months•During post-DMT period, the annualized relapse rate was 0.22 and confirmed disability (CDP) progression rate was 8.23 per 100 person-years•Younger and moderately disabled patients were at highest risk of post-DMT relapse•Patients who entered the progressive phase or were severely impaired at baseline were at highest risk for CDP during post-DMT follow up•Former natalizumab users had elevated relapse and CDP rates during follow up compared to other DMTs
After multiple sclerosis (MS) relapse while a patient is receiving an injectable disease-modifying drug, many physicians advocate therapy switch, but the relative effectiveness of different switch ...decisions is often uncertain.
To compare the effect of the oral immunomodulator fingolimod with that of all injectable immunomodulators (interferons or glatiramer acetate) on relapse rate, disability, and treatment persistence in patients with active MS.
Matched retrospective analysis of data collected prospectively from MSBase, an international, observational cohort study. The MSBase cohort represents a population of patients with MS monitored at large MS centers. The analyzed data were collected between July 1996 and April 2014. Participants included patients with relapsing-remitting MS who were switching therapy to fingolimod or injectable immunomodulators up to 12 months after on-treatment clinical disease activity (relapse or progression of disability), matched on demographic and clinical variables. Median follow-up duration was 13.1 months (range, 3-80). Indication and attrition bias were controlled with propensity score matching and pairwise censoring, respectively. Head-to-head analyses of relapse and disability outcomes used paired, weighted, negative binomial models or frailty proportional hazards models adjusted for magnetic resonance imaging variables. Sensitivity analyses were conducted.
Patients had received fingolimod, interferon beta, or glatiramer acetate for a minimum of 3 months following a switch of immunomodulatory therapy.
Annualized relapse rate and proportion of relapse-free patients, as well as the proportion of patients without sustained disability progression.
Overall, 379 patients in the injectable group were matched to 148 patients in the fingolimod group. The fingolimod group had a lower mean annualized relapse rate (0.31 vs 0.42; 95% CI, 0.02-0.19; P=.009), lower hazard of first on-treatment relapse (hazard ratio HR, 0.74; 95% CI, 0.56-0.98; P=.04), lower hazard of disability progression (HR, 0.53; 95% CI, 0.31-0.91; P=.02), higher rate of disability regression (HR, 2.0; 95% CI, 1.2-3.3; P=.005), and lower hazard of treatment discontinuation (HR, 0.55; P=.04) compared with the injectable group.
Switching from injectable immunomodulators to fingolimod is associated with fewer relapses, more favorable disability outcomes, and greater treatment persistence compared with switching to another injectable preparation following on-treatment activity of MS.
Age at onset (AAO) in multiple sclerosis (MS) is an important marker of disease severity and may have prognostic significance. Understanding what factors can influence AAO may shed light on the ...aetiology of this complex disease, and have applications in the diagnostic process.
The study cohort of 22 162 eligible patients from 21 countries was extracted from the MSBase registry. Only patients with MS aged ≥16 years were included. To reduce heterogeneity, only centres of largely European descent were included for analysis. AAO was defined as the year of the first symptom suggestive of inflammatory central nervous system demyelination. Predictors of AAO were evaluated by linear regression.
Compared with those living in lower latitudes (19.0-39.9°), onset of symptoms was 1.9 years earlier for those at higher latitudes (50.0-56.0°) (p=3.83×10
). A reciprocal relationship was seen for ambient ultraviolet radiation (UVR), with a significantly increasing AAO for patients with MS per each quartile increment of ambient UVR (p=1.56×10
). We found that the AAO of female patients was ∼5 months earlier than male patients (p=0.002). AAO of progressive-onset patients with MS were ∼9 years later than relapsing-onset patients (p=1.40×10
).
An earlier AAO in higher latitude regions was found in this worldwide European-descent cohort and correlated inversely with variation in latitudinal UVR. These results suggest that environmental factors which act at the population level may significantly influence disease severity characteristics in genetically susceptible populations.
Background
Patients with highly active relapsing-remitting multiple sclerosis inadequately responding to first-line therapies (interferon-based therapies, glatiramer acetate, dimethyl fumarate, and ...teriflunomide, known collectively as “BRACETD”) often switch to natalizumab or fingolimod.
Objective
The aim was to estimate the comparative effectiveness of switching to natalizumab or fingolimod or within BRACETD using real-world data and to evaluate the cost-effectiveness of switching to natalizumab versus fingolimod using a United Kingdom (UK) third-party payer perspective.
Methods
Real-world data were obtained from MSBase for patients relapsing on BRACETD in the year before switching to natalizumab or fingolimod or within BRACETD. Three-way-multinomial-propensity-score–matched cohorts were identified, and comparisons between treatment groups were conducted for annualised relapse rate (ARR) and 6-month–confirmed disability worsening (CDW6M) and improvement (CDI6M). Results were applied in a cost-effectiveness model over a lifetime horizon using a published Markov structure with health states based on the Expanded Disability Status Scale. Other model parameters were obtained from the UK MS Survey 2015, published literature, and publicly available UK sources.
Results
The MSBase analysis found a significant reduction in ARR (rate ratio RR = 0.64; 95% confidence interval CI 0.57–0.72;
p
< 0.001) and an increase in CDI6M (hazard ratio HR = 1.67; 95% CI 1.30–2.15;
p
< 0.001) for switching to natalizumab compared with BRACETD. For switching to fingolimod, the reduction in ARR (RR = 0.91; 95% CI 0.81–1.03;
p
= 0.133) and increase in CDI6M (HR = 1.30; 95% CI 0.99–1.72;
p
= 0.058) compared with BRACETD were not significant. Switching to natalizumab was associated with a significant reduction in ARR (RR = 0.70; 95% CI 0.62–0.79;
p
< 0.001) and an increase in CDI6M (HR = 1.28; 95% CI 1.01–1.62;
p
= 0.040) compared to switching to fingolimod. No evidence of difference in CDW6M was found between treatment groups. Natalizumab dominated (higher quality-adjusted life-years QALYs and lower costs) fingolimod in the base-case cost-effectiveness analysis (0.453 higher QALYs and £20,843 lower costs per patient). Results were consistent across sensitivity analyses.
Conclusions
This novel real-world analysis suggests a clinical benefit for therapy escalation to natalizumab versus fingolimod based on comparative effectiveness results, translating to higher QALYs and lower costs for UK patients inadequately responding to BRACETD.
To compare the effectiveness of glatiramer acetate (GA) vs intramuscular interferon beta-1a (IFN-β-1a), we applied a previously published statistical method aimed at identifying patients' profiles ...associated with efficacy of treatments.
Data from 2 independent multiple sclerosis datasets, a randomized study (the Combination Therapy in Patients With Relapsing-Remitting Multiple Sclerosis CombiRx trial, evaluating GA vs IFN-β-1a) and an observational cohort extracted from MSBase, were used to build and validate a treatment response score, regressing annualized relapse rates (ARRs) on a set of baseline predictors.
The overall ARR ratio of GA to IFN-β-1a in the CombiRx trial was 0.72. The response score (made up of a linear combination of age, sex, relapses in the previous year, disease duration, and Expanded Disability Status Scale score) detected differential response of GA vs IFN-β-1a: in the trial, patients with the largest benefit from GA vs IFN-β-1a (lower score quartile) had an ARR ratio of 0.40 (95% confidence interval CI 0.25-0.63), those in the 2 middle quartiles of 0.90 (95% CI 0.61-1.34), and those in the upper quartile of 1.14 (95% CI 0.59-2.18) (heterogeneity
= 0.012); this result was validated on MSBase, with the corresponding ARR ratios of 0.58 (95% CI 0.46-0.72), 0.92 (95% CI 0.77-1.09,) and 1.29 (95% CI 0.97-1.71); heterogeneity
< 0.0001).
We demonstrate the possibility of a criterion, based on patients' characteristics, to choose whether to treat with GA or IFN-β-1a. This result, replicated on an independent real-life cohort, may have implications for clinical decisions in everyday clinical practice.
Objectives:
The aim was to analyse risk of relapse phenotype recurrence in multiple sclerosis and to characterise the effect of demographic and clinical features on this phenotype.
Methods:
...Information about relapses was collected using MSBase, an international observational registry. Associations between relapse phenotypes and history of similar relapses or patient characteristics were tested with multivariable logistic regression models. Tendency of relapse phenotypes to recur sequentially was assessed with principal component analysis.
Results:
Among 14,969 eligible patients (89,949 patient-years), 49,279 phenotypically characterised relapses were recorded. Visual and brainstem relapses occurred more frequently in early disease and in younger patients. Sensory relapses were more frequent in early or non-progressive disease. Pyramidal, sphincter and cerebellar relapses were more common in older patients and in progressive disease. Women presented more often with sensory or visual symptoms. Men were more prone to pyramidal, brainstem and cerebellar relapses. Importantly, relapse phenotype was predicted by the phenotypes of previous relapses. (OR = 1.8–5, p = 10-14). Sensory, visual and brainstem relapses showed better recovery than other relapse phenotypes. Relapse severity increased and the ability to recover decreased with age or more advanced disease.
Conclusion:
Relapse phenotype was associated with demographic and clinical characteristics, with phenotypic recurrence significantly more common than expected by chance.
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:
Several natural history studies on primary progressive multiple sclerosis (PPMS) patients detected a consistent heterogeneity in the rate of disability accumulation.
Objectives:
To ...identify subgroups of PPMS patients with similar longitudinal trajectories of Expanded Disability Status Scale (EDSS) over time.
Methods:
All PPMS patients collected within the MSBase registry, who had their first EDSS assessment within 5 years from onset, were included in the analysis. Longitudinal EDSS scores were modeled by a latent class mixed model (LCMM), using a nonlinear function of time from onset. LCMM is an advanced statistical approach that models heterogeneity between patients by classifying them into unobserved groups showing similar characteristics.
Results:
A total of 853 PPMS (51.7% females) from 24 countries with a mean age at onset of 42.4 years (standard deviation (SD): 10.8 years), a median baseline EDSS of 4 (interquartile range (IQR): 2.5–5.5), and 2.4 years of disease duration (SD: 1.5 years) were included. LCMM detected three different subgroups of patients with a mild (n = 143; 16.8%), moderate (n = 378; 44.3%), or severe (n = 332; 38.9%) disability trajectory. The probability of reaching EDSS 6 at 10 years was 0%, 46.4%, and 81.9% respectively.
Conclusion:
Applying an LCMM modeling approach to long-term EDSS data, it is possible to identify groups of PPMS patients with different prognosis.
Background:
The MSBase prediction model of treatment response leverages multiple demographic and clinical characteristics to estimate hazards of relapses, confirmed disability accumulation (CDA), and ...confirmed disability improvement (CDI). The model did not include Multiple Sclerosis Severity Score (MSSS), a disease duration-adjusted ranked score of disability.
Objective:
To incorporate MSSS into the MSBase prediction model and compare model accuracy with and without MSSS.
Methods:
The associations between MSSS and relapse, CDA, and CDI were evaluated with marginal proportional hazards models adjusted for three principal components representative of patients’ demographic and clinical characteristics. The model fit with and without MSSS was assessed with penalized r2 and Harrell C.
Results:
A total of 5866 MS patients were started on disease-modifying therapy during prospective follow-up (age 38.4 ± 10.6 years; 72% female; disease duration 8.5 ± 7.7 years). Including MSSS into the model improved the accuracy of individual prediction of relapses by 31%, of CDA by 23%, and of CDI by 24% (Harrell C) and increased the amount of variance explained for relapses by 49%, for CDI by 11%, and for CDA by 10% as compared with the original model.
Conclusion:
Addition of a single, readily available metric, MSSS, to the comprehensive MSBase prediction model considerably improved the individual accuracy of prognostics in MS.
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