Silent lesions on MRI imaging - Shifting goal posts for treatment decisions in multiple sclerosis.

The current best practice suggests yearly magnetic resonance imaging (MRI) to monitor treatment response in multiple sclerosis (MS) patients. To evaluate the current practice of clinicians changing MS treatment based on subclinical new MRI lesions alone. Using MSBase, an international MS patient registry with MRI data, we analysed the probability of treatment change among patients with clinically silent new MRI lesions. A total of 8311 MRI brain scans of 4232 patients were identified. Around 26.9% (336/1247) MRIs with one new T2 lesion were followed by disease-modifying therapy (DMT) change, increasing to 50.2% (129/257) with six new T2 lesions. DMT change was twice as likely with new T1 contrast enhancing compared to new T2 lesions odds ratio (OR): 2.43, 95% confidence interval (CI): 2.00-2.96 vs OR: 1.26 (95% CI: 1.22-1.29). DMT change with new MRI lesions occurred most frequently with 'injectable' DMTs. The probability of switching therapy was greater only after high-efficacy therapies became available in 2007 (after, OR: 1.43, 95% CI: 1.28-1.59 vs before, OR: 0.98, 95% CI: 0.520-1.88). MS clinicians rely increasingly on MRI alone in their treatment decisions, utilizing low thresholds (1 new T2 lesion) for optimizing MS therapy. This signals a shift towards no evidence of disease activity (NEDA)-3 since high-efficacy therapies became available

Prediction of on-treatment disability worsening in RRMS with the MAGNIMS score

Background: The magnetic resonance imaging in multiple sclerosis (MAGNIMS) score combines relapses and magnetic resonance imaging (MRI) lesions to predict disability outcomes in relapsing-remitting multiple sclerosis (RRMS) treated with interferon-beta. Objective: To validate the MAGNIMS score and extend to other disease-modifying therapies (DMTs). To examine the prognostic value of gadolinium contrast-enhancing (Gd+) lesions. Methods: This RRMS MSBase cohort study (n = 2293) used a Cox model to examine the prognostic value of relapses, MRI activity and the MAGNIMS score for disability worsening during treatment with interferon-beta and three other DMTs. Results: Three new T2 lesions (hazard ratio (HR) = 1.60,p = 0.028) or two relapses (HR = 2.24,p = 0.002) on interferon-beta (for 12 months) were predictive of disability worsening over 4 years. MAGNIMS score = 2 (1 relapse and > 3 T2 lesions or > 2 relapses) was associated with a greater risk of disability worsening on interferon-beta (HR = 2.0,p = 0.001). In pooled cohort of four DMTs, similar associations were seen (MAGNIMS score = 2: HR = 1.72,p = 0.001). Secondary analyses demonstrated that the addition of Gd+ to the MAGNIMS did not materially improve its prediction of disability worsening. Conclusion: We have validated the MAGNIMS score in RRMS and extended its application to three other DMTs: 1 relapse and > 3 T2 lesions or > 2 relapses predicted worsening of disability. Contrast-enhancing lesions did not substantially improve the prognostic score

Comparative effectiveness of cladribine tablets versus other oral disease-modifying treatments for multiple sclerosis: Results from MSBase registry

Background: Effectiveness of cladribine tablets, an oral disease-modifying treatment (DMT) for multiple sclerosis (MS), was established in clinical trials and confirmed with real-world experience. Objectives: Use real-world data to compare treatment patterns and clinical outcomes in people with MS (pwMS) treated with cladribine tablets versus other oral DMTs. Methods: Retrospective treatment comparisons were based on data from the international MSBase registry. Eligible pwMS started treatment with cladribine, fingolimod, dimethyl fumarate, or teriflunomide tablets from 2018 to mid-2021 and were censored at treatment discontinuation/switch, death, loss to follow-up, pregnancy, or study period end. Treatment persistence was evaluated as time to discontinuation/switch; relapse outcomes included time to first relapse and annualized relapse rate (ARR). Results: Cohorts included 633 pwMS receiving cladribine tablets, 1195 receiving fingolimod, 912 receiving dimethyl fumarate, and 735 receiving teriflunomide. Individuals treated with fingolimod, dimethyl fumarate, or teriflunomide switched treatment significantly more quickly than matched cladribine tablet cohorts (adjusted hazard ratio (95% confidence interval): 4.00 (2.54-6.32), 7.04 (4.16-11.93), and 6.52 (3.79-11.22), respectively). Cladribine tablet cohorts had significantly longer time-to-treatment discontinuation, time to first relapse, and lower ARR, compared with other oral DMT cohorts. Conclusion: Cladribine tablets were associated with a significantly greater real-world treatment persistence and more favorable relapse outcomes than all oral DMT comparators

A plain language summary on the effectiveness of cladribine tablets compared with other oral treatments for multiple sclerosis: results from the MSBase registry

What is this summary about? Patient registries contain anonymous data from people who share the same medical condition. The MSBase registry contains information from over 80,000 people living with multiple sclerosis (MS) across 41 countries. Using information from the MSBase registry, the GLIMPSE (Generating Learnings In MultiPle SclErosis) study looked at real-life outcomes in 3475 people living with MS who were treated with cladribine tablets (Mavenclad®) compared with other oral treatments. What were the results? Results showed that people treated with cladribine tablets stayed on treatment for longer than other treatments given by mouth. They also had fewer relapses (also called flare ups of symptoms) than people who received a different oral treatment for their MS. What do the results mean? The results provide evidence that, compared with other oral treatments for MS, cladribine tablets are an effective medicine for people living with MS. </sec

Long-term disability trajectories in primary progressive MS patients: A latent class growth analysis

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

Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180].

The authors regret an error in the pre-processing of the dataset that went unnoticed in the extensive code used for pre-processing of the study data. As a result of this error, the last EDSS overall for each patient was included as the last EDSS in the observation period. We corrected the error and repeated all analyses. [...

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression.

Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem. We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used. We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS

Disability outcomes of early cerebellar and brainstem symptoms in multiple sclerosis.

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

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response.

Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of disability regression was predominantly associated with pre-baseline disability, therapy and relapse activity. Relapse incidence was associated with pretreatment relapse activity, age and relapsing disease course, with the strength of these associations varying among therapies. Accuracy and internal validity (n = 1196) of the resulting predictive models was high (>80%) for relapse incidence during the first year and for disability outcomes, moderate for relapse incidence in Years 2-4 and for the change in the cumulative disease burden, and low for conversion to secondary progressive disease and treatment discontinuation. External validation showed similar results, demonstrating high external validity for disability and relapse outcomes, moderate external validity for cumulative disease burden and low external validity for conversion to secondary progressive disease and treatment discontinuation. We conclude that demographic, clinical and paraclinical information helps predict individual response to disease-modifying therapies at the time of their commencement