Heterogeneity on long-term disability trajectories in patients with secondary progressive MS: a latent class analysis from Big MS Data network

Late-onset multiple sclerosis: disability trajectories in relapsing-remitting patients of the Italian MS Registry

BACKGROUND

Generally infrequent, multiple sclerosis (MS) with late onset (LOMS) is characterized by an onset over the age of 50 and a mainly progressive course, while relapsing-remitting (RR) forms are less frequently observed and explored. This study aimed to characterize a large cohort of MS patients with RRMS at onset to assess the baseline factors related to the worst disability trajectories and explore the role of LOMS.

METHODS

The data were extracted from the Italian MS Register (IMSR). Disability trajectories, defined using at least two and up to twenty expanded disability status scale (EDSS) assessments annually performed, were implemented using group-based trajectory models (GBTMs) to identify different groups with the same trajectories over time. MS profiles were explored using multinomial logistic regression.

RESULTS

A total of 16,159 RR patients [1012 (6.26%) presented with LOMS] were analyzed. The GBTM identified four disability trajectories. The group with the most severe EDSS trend included 12.3% of the patients with a mean EDSS score > 4, which increased over time and exceeded 6 score. The group with medium severity EDSS trend comprised 21.9% of the patients and showed a change in EDSS > 3 scores over time. The largest group with 50.8% of patients reported a constant EDSS of 2 score. Finally, the benign group comprised 14.9% of the patients with a low and constant EDSS of 1 score over time. The probability of being in the worst groups increased if the patient was male; had LOMS or experienced brainstem, spinal, or supratentorial symptoms.

CONCLUSIONS

Four MS severity profiles among RRMS patients in the IMSR have been reported, with LOMS being associated with a rapid worsening of EDSS scores. These findings have important implications for recognizing and managing how older age, aging, and age-related factors interact with MS and its evolution.

Big Multiple Sclerosis Data network: an international registry research network

BACKGROUND

The Big Multiple Sclerosis Data (BMSD) network ( https://bigmsdata.org ) was initiated in 2014 and includes the national multiple sclerosis (MS) registries of the Czech Republic, Denmark, France, Italy, and Sweden as well as the international MSBase registry. BMSD has addressed the ethical, legal, technical, and governance-related challenges for data sharing and so far, published three scientific papers on pooled datasets as proof of concept for its collaborative design.

DATA COLLECTION

Although BMSD registries operate independently on different platforms, similarities in variables, definitions and data structure allow joint analysis of data. Certain coordinated modifications in how the registries collect adverse event data have been implemented after BMSD consensus decisions, showing the ability to develop together.

DATA MANAGEMENT

Scientific projects can be proposed by external sponsors via the coordinating centre and each registry decides independently on participation, respecting its governance structure. Research datasets are established in a project-to-project fashion and a project-specific data model is developed, based on a unifying core data model. To overcome challenges in data sharing, BMSD has developed procedures for federated data analysis.

FUTURE PERSPECTIVES

Presently, BMSD is seeking a qualification opinion from the European Medicines Agency (EMA) to conduct post-authorization safety studies (PASS) and aims to pursue a qualification opinion also for post-authorization effectiveness studies (PAES). BMSD aspires to promote the advancement of real-world evidence research in the MS field.

Introducing a core dataset for real-world data in multiple sclerosis registries and cohorts: Recommendations from a global task force

BACKGROUND

As of September 2022, there was no globally recommended set of core data elements for use in multiple sclerosis (MS) healthcare and research. As a result, data harmonisation across observational data sources and scientific collaboration is limited.

OBJECTIVES

To define and agree upon a core dataset for real-world data (RWD) in MS from observational registries and cohorts.

METHODS

A three-phase process approach was conducted combining a landscaping exercise with dedicated discussions within a global multi-stakeholder task force consisting of 20 experts in the field of MS and its RWD to define the Core Dataset.

RESULTS

A core dataset for MS consisting of 44 variables in eight categories was translated into a data dictionary that has been published and disseminated for emerging and existing registries and cohorts to use. Categories include variables on demographics and comorbidities (patient-specific data), disease history, disease status, relapses, magnetic resonance imaging (MRI) and treatment data (disease-specific data).

CONCLUSION

The MS Data Alliance Core Dataset guides emerging registries in their dataset definitions and speeds up and supports harmonisation across registries and initiatives. The straight-forward, time-efficient process using a dedicated global multi-stakeholder task force has proven to be effective to define a concise core dataset.

Effects of Dietary Modification Based on Complementary and Alternative Iranian Medicine in Patients with Secondary-Progressive Multiple Sclerosis: A Randomized Controlled Clinical Trial

Objectives: To evaluate the efficacy of dietary modifications based on complementary and alternative Iranian medicine (CAIM) in patients with secondary-progressive multiple sclerosis (SPMS). Design: In this randomized controlled trial, 70 SPMS patients were randomized to receive either a moderate-nature diet based on Persian medicine (as intervention) or usual diet plus health-related diet recommendations (as control) for 2 months. Serum high-sensitivity C-reactive protein (hs-CRP), erythrocyte sedimentation rate (ESR), Expanded Disability Status Scale (EDSS), Modified Fatigue Impact Scale (MFIS), State-Trait Anxiety Inventory (STAI), Global Pain Scale (GPS), Gastrointestinal Symptom Rating Scale (GSRS), anthropometric measurements, and quality of life (QOL) were assessed at baseline and end of trial. Analysis of covariance was performed, and the results were adjusted for potential confounders using SPSS v.14. Results: All participants completed the study for 2 months. There were significant improvements across the mean changes of hs-CRP (-0.1 ± 0.2 mg/L for intervention vs. -0.01 ± 0.13 mg/L for control; padjusted = 0.012), MFIS (-11.0 ± 11.8 vs. -0.7 ± 9.9; padjusted <0.001), GSRS (-19.9 ± 16.3 to 1.2 ± 17.5; padjusted <0.001), GPS (padjusted = 0.032), and QOL (padjusted <0.05). No significant difference was observed across the ESR, EDSS, STAI, and anthropometric measurements. Conclusion: Dietary modifications based on CAIM may improve inflammation and clinical manifestations in SPMS patients. Nonetheless, further trials are required to confirm these findings. Clinical Trial Registration: IRCT20181113041641N2.

Measuring disability in multiple sclerosis: the WHODAS 2.0

INTRODUCTION

Reliable measurement of disability in multiple sclerosis (MS) using a comprehensive, patient self-reported scale, such as the World Health Organization Disability Assessment Schedule (WHODAS) 2.0, would be of clinical and research benefit.

METHODS

In the Trajectories of Outcome in Neurological Conditions-MS study, WHODAS 2.0 (WHODAS-36 items for working, WHODAS-32 items if not working, WHODAS-12 items short-form) was examined using Rasch analysis in 5809 people with MS.

RESULTS

The 36- and 32-item parallel forms, and the cognitive and physical domains, showed reliability consistent with individual or group use. The 12-item short-form is valid for group use only. Interval level measurement for parametric statistics can be derived from all three scales which showed medium to strong effect sizes for discrimination across characteristics such as age, subtype, and disease duration. Smallest detectable difference for each scale was < 6 on the standardised metric of 0-100 so < 6% of the total range. There was no substantial differential item functioning (DIF) by age, gender, education, working full/part-time, or disease duration; the finding of no DIF for time or sample supports the use of WHODAS 2.0 for longitudinal studies, with the 36- and 32-item versions and the physical and cognitive domains valid for individual patient follow-up.

CONCLUSIONS

Disability in MS can be comprehensively measured at interval level by the WHODAS 2.0, and validly monitored over time. Routine use of this self-reported measure in clinical and research practice would give valuable information on the trajectories of disability of individuals and groups.

Long-term trajectories of ambulatory impairment in multiple sclerosis

BACKGROUND

Ambulatory impairment is a common and complex manifestation of multiple sclerosis (MS), and longitudinal patterns are not well understood.

OBJECTIVE

To characterize longitudinal walking speed trajectories in a general MS patient population and in those with early disease (⩽ 5 years from onset), identify subgroups with similar patterns, and examine associations with individual attributes.

METHODS

Using a retrospective cohort study design, latent class growth analysis was applied to longitudinal timed 25-foot walk (T25-FW) data from 7683 MS patients, to determine T25-FW trajectories. Associations were evaluated between trajectory assignment and individual attributes. Analyses were repeated for 2591 patients with early disease.

RESULTS

In the general patient population, six trajectories were discerned, ranging from very minimal to very high impairment at baseline, with variability in impairment accrual. The clusters with moderate to very high walking impairment were associated with being female, older and Black American, longer symptom duration, progressive course, and depressive symptoms. In the early disease subset, eight trajectories were discerned that included two subgroups that rapidly accrued impairment.

CONCLUSION

We identified novel subgroups of MS patients will distinct long-term T25-FW trajectories. These results underscore that socially disadvantaged and economically marginalized MS patients are the most vulnerable for severe ambulatory impairment.

Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI

The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy when predicting the course of neurological conditions, including multiple sclerosis, by means of extracting image features not detectable through conventional methods. Additionally, the study of CNN-derived attention maps, which indicate the most relevant anatomical features for CNN-based decisions, has the potential to uncover key disease mechanisms leading to disability accumulation. From a cohort of patients prospectively followed up after a first demyelinating attack, we selected those with T1-weighted and T2-FLAIR brain MRI sequences available for image analysis and a clinical assessment performed within the following six months (N = 319). Patients were divided into two groups according to expanded disability status scale (EDSS) score: ≥3.0 and < 3.0. A 3D-CNN model predicted the class using whole-brain MRI scans as input. A comparison with a logistic regression (LR) model using volumetric measurements as explanatory variables and a validation of the CNN model on an independent dataset with similar characteristics (N = 440) were also performed. The layer-wise relevance propagation method was used to obtain individual attention maps. The CNN model achieved a mean accuracy of 79% and proved to be superior to the equivalent LR-model (77%). Additionally, the model was successfully validated in the independent external cohort without any re-training (accuracy = 71%). Attention-map analyses revealed the predominant role of frontotemporal cortex and cerebellum for CNN decisions, suggesting that the mechanisms leading to disability accrual exceed the mere presence of brain lesions or atrophy and probably involve how damage is distributed in the central nervous system.