Improving quality, affordability, and equity of multiple sclerosis care.

OBJECTIVE: The prevailing approaches to selecting multiple sclerosis (MS) disease modifying therapies (DMTs) have contributed to exponential increases in societal expenditures and out-of-pocket expenses, without compelling evidence of improved outcomes. Guidance is lacking regarding when and in whom the benefits of preventing MS-related disability likely outweighs the risks of highly effective DMTs (HET) and when it is appropriate to consider DMT costs. Our objective was to develop a standardized approach to improve the quality, affordability and equity of MS care. METHODS: MS experts partnered with health plan pharmacists to develop an ethical, risk-stratified, cost-sensitive treatment algorithm. We developed a risk-stratification schema to classify patients with relapsing forms of MS as high, intermediate or low risk of disability based on the best available evidence and, when the evidence was poor or lacking, by consensus. DMTs are grouped as highly, modestly or low/uncertain effectiveness and preferentially ranked within groups by safety based on pre-specified criteria. We reviewed FDA documents and the published literature. When efficacy and safety are equivalent, the lower cost DMT is preferred. RESULTS: Assignment to the high-risk group prompts treatment with preferred HETs early in the disease course. For persons in the intermediate- or low-risk groups with cost or health care access barriers, we incorporated induction therapy with an affordable B-cell depleting agent. Based on more favorable safety profiles, our preferred approach prioritizes use of rituximab and natalizumab among HETs and interferon-betas or glatiramer acetate among modestly effective agents. INTERPRETATION: The risk-stratified treatment approach we recommend provides clear, measurable guidance in whom and when to prescribe HETs, when to prioritize lower cost DMTs and how to accommodate persons with MS with cost or other barriers to DMT use. It can be adapted to other cost structures and updated quickly as new information emerges. We recommend that physician groups partner with health insurance plans to adapt our approach to their settings, particularly in the United States. Future studies are needed to resolve the considerable uncertainty about how much variability in prognosis specific risk factors explain.

Impact of treatment on cellular immunophenotype in MS: A cross-sectional study.

OBJECTIVE: To establish cytometry profiles associated with disease stages and immunotherapy in MS. METHODS: Demographic/clinical data and peripheral blood samples were collected from 227 patients with MS and 82 sex- and age-matched healthy controls (HCs) enrolled in a cross-sectional study at 4 European MS centers (Spain, Italy, Germany, and Norway). Flow cytometry of isolated peripheral blood mononuclear cells was performed in each center using specifically prepared antibody-cocktail Lyotubes; data analysis was centralized at the Genoa center. Differences in immune cell subsets were assessed between groups of untreated patients with relapsing-remitting or progressive MS (RRMS or PMS) and HCs and between groups of patients with RRMS taking 6 commonly used disease-modifying drugs. RESULTS: In untreated patients with MS, significantly higher frequencies of Th17 cells in the RRMS population compared with HC and lower frequencies of B-memory/B-regulatory cells as well as higher percentages of B-mature cells in patients with PMS compared with HCs emerged. Overall, the greatest deviation in immunophenotype in MS was observed by treatment rather than disease course, with the strongest impact found in fingolimod-treated patients. Fingolimod induced a decrease in total CD4+ T cells and in B-mature and B-memory cells and increases in CD4+ and CD8+ T-regulatory and B-regulatory cells. CONCLUSIONS: Our highly standardized, multisite cytomics data provide further understanding of treatment impact on MS immunophenotype and could pave the way toward monitoring immune cells to help clinical management of MS individuals.

Classifying the antibody-negative NMO syndromes: Clinical, imaging, and metabolomic modeling.

OBJECTIVE: To determine whether unsupervised principal component analysis (PCA) of comprehensive clinico-radiologic data can identify phenotypic subgroups within antibody-negative patients with overlapping features of multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSDs), and to validate the phenotypic classifications using high-resolution nuclear magnetic resonance (NMR) plasma metabolomics with inference to underlying pathologies. METHODS: Forty-one antibody-negative patients were recruited from the Oxford NMO Service. Thirty-six clinico-radiologic parameters, focusing on features known to distinguish NMOSD and MS, were collected to build an unbiased PCA model identifying phenotypic subgroups within antibody-negative patients. Metabolomics data from patients with relapsing-remitting MS (RRMS) (n = 34) and antibody-positive NMOSD (Ab-NMOSD) (aquaporin-4 antibody n = 54, myelin oligodendrocyte glycoprotein antibody n = 20) were used to identify discriminatory plasma metabolites separating RRMS and Ab-NMOSD. RESULTS: PCA of the 36 clinico-radiologic parameters revealed 3 phenotypic subgroups within antibody-negative patients: an MS-like subgroup, an NMOSD-like subgroup, and a low brain lesion subgroup. Supervised multivariate analysis of metabolomics data from patients with RRMS and Ab-NMOSD identified myoinositol and formate as the most discriminatory metabolites (both higher in RRMS). Within antibody-negative patients, myoinositol and formate were significantly higher in the MS-like vs NMOSD-like subgroup; myoinositol (mean [SD], 0.0023 [0.0002] vs 0.0019 [0.0003] arbitrary units [AU]; p = 0.041); formate (0.0027 [0.0006] vs 0.0019 [0.0006] AU; p = 0.010) (AU). CONCLUSIONS: PCA identifies 3 phenotypic subgroups within antibody-negative patients and that the metabolite discriminators of RRMS and Ab-NMOSD suggest that these groupings have some pathogenic meaning. Thus, the identified clinico-radiologic discriminators may provide useful diagnostic clues when seeing antibody-negative patients in the clinic.

Early Clinical Features, Time to Secondary Progression, and Disability Milestones in Polish Multiple Sclerosis Patients.

Background and Objectives: Determining the clinical course of multiple sclerosis (MS) and prediction of long-term disability can be a big challenge. To determine early clinical features of MS, their influence on long-term disability progression, and time to transition from relapsing-remitting MS (RRMS) to secondary progressive MS (SPMS), a cohort of Polish patients was studied. Materials and Methods: We retrospectively evaluated 375 Polish MS patients based on data from available medical records. We assessed early clinical MS features and the relationship between demographics and time from disease onset to attainment of 4 and 6 points on the Expanded Disability Status Scale (EDSS), as well as time to conversion from RRMS to SPMS. Results: The differences between initial MS variants were significantly associated with gender, age at disease onset, number and type of the first symptoms, and rate of the disability accrual. Mean times from disease onset to attainment of EDSS 4 and 6 were significantly influenced by the disease variant, age at onset, gender, degree of recovery from the initial symptoms, and first inter-bouts interval. The mean time to secondary progression was significantly influenced by the number and type of the first symptoms of RRMS. Conclusions: Early clinical features of MS are important in determining the disease variant, the time to transition from RRMS to SPMS, as well as predicting the disability accumulation of patients. Despite the small differences regarding the first MS symptoms, the disability outcomes in the cohort of Polish patients are similar to other regions of the world.

Ocrelizumab efficacy in subgroups of patients with relapsing multiple sclerosis.

OBJECTIVE: The efficacy and safety of ocrelizumab, versus interferon (IFN) ß-1a, for the treatment of relapsing multiple sclerosis (RMS) from the identically designed OPERA I (NCT01247324) and OPERA II (NCT01412333) phase III studies has been reported; here we present subgroup analyses of efficacy endpoints from the pooled OPERA I and OPERA II populations. METHODS: Patients with RMS were randomized to either ocrelizumab 600 mg administered by intravenous infusion every 24 weeks or subcutaneous IFN ß-1a 44 µg three times per week throughout the 96-week treatment period. Relapse, disability, and MRI outcomes were analyzed for predefined and post hoc subgroups based on demographic and disease characteristics along with prior treatment using appropriate statistical tests to determine the treatment effect in subgroups and treatment-by-subgroup interactions. RESULTS: The significant treatment benefit of ocrelizumab, versus IFN ß-1a, observed in the overall OPERA I and OPERA II pooled populations was maintained across most subgroup strata for all endpoints, including annualized relapse rate, disability progression, and MRI outputs. CONCLUSIONS: The treatment effect of ocrelizumab versus IFN ß-1a, measured by clinical and MRI outcomes, was maintained across most of the subgroups and strata of interest, and the pattern of treatment benefit across all subgroups was consistent with that from the pooled OPERA studies.

Identifying Patients With Relapsing-Remitting Multiple Sclerosis Using Algorithms Applied to US Integrated Delivery Network Healthcare Data.

BACKGROUND: Relapsing-remitting multiple sclerosis (RRMS) has a major impact on affected patients; therefore, improved understanding of RRMS is important, particularly in the context of real-world evidence. OBJECTIVES: To develop and validate algorithms for identifying patients with RRMS in both unstructured clinical notes found in electronic health records (EHRs) and structured/coded health care claims data. METHODS: US Integrated Delivery Network data (2010-2014) were queried for study inclusion criteria (possible multiple sclerosis [MS] base cohort): one or more MS diagnosis code, patients aged 18 years or older, 1 year or more baseline history, and no other demyelinating diseases. Sets of algorithms were developed to search narrative text of unstructured clinical notes (EHR clinical notes-based algorithms) and structured/coded data (claims-based algorithms) to identify adult patients with RRMS, excluding patients with evidence of progressive MS. Medical records were reviewed manually for algorithm validation. Positive predictive value was calculated for both EHR clinical notes-based and claims-based algorithms. RESULTS: From a sample of 5308 patients with possible MS, 837 patients with RRMS were identified using only the EHR clinical notes-based algorithms and 2271 patients were identified using only the claims-based algorithms; 779 patients were identified using both algorithms. The positive predictive value was 99.1% (95% confidence interval [CI], 94.2%-100%) for the EHR clinical notes-based algorithms and 94.6% (95% CI, 89.1%-97.8%) to 94.9% (95% CI, 89.8%-97.9%) for the claims-based algorithms. CONCLUSIONS: The algorithms evaluated in this study identified a real-world cohort of patients with RRMS without evidence of progressive MS that can be studied in clinical research with confidence.

Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis.

BACKGROUND: Deep sequencing of lymphocyte receptor repertoires has made it possible to comprehensively profile the clonal composition of lymphocyte populations. This opens the door for novel approaches to diagnose and prognosticate diseases with a driving immune component by identifying repertoire sequence patterns associated with clinical phenotypes. Indeed, recent studies support the feasibility of this, demonstrating an association between repertoire-level summary statistics (e.g., diversity) and patient outcomes for several diseases. In our own prior work, we have shown that six codons in VH4-containing genes in B cells from the cerebrospinal fluid of patients with relapsing remitting multiple sclerosis (RRMS) have higher replacement mutation frequencies than observed in healthy controls or patients with other neurological diseases. However, prior methods to date have been limited to focusing on repertoire-level summary statistics, ignoring the vast amounts of information in the millions of individual immune receptors comprising a repertoire. We have developed a novel method that addresses this limitation by using innovative approaches for accommodating the extraordinary sequence diversity of immune receptors and widely used machine learning approaches. We applied our method to RRMS, an autoimmune disease that is notoriously difficult to diagnose. RESULTS: We use the biochemical features encoded by the complementarity determining region 3 of each B cell receptor heavy chain in every patient repertoire as input to a detector function, which is fit to give the correct diagnosis for each patient using maximum likelihood optimization methods. The resulting statistical classifier assigns patients to one of two diagnosis categories, RRMS or other neurological disease, with 87% accuracy by leave-one-out cross-validation on training data (N = 23) and 72% accuracy on unused data from a separate study (N = 102). CONCLUSIONS: Our method is the first to apply statistical learning to immune repertoires to aid disease diagnosis, learning repertoire-level labels from the set of individual immune repertoire sequences. This method produced a repertoire-based statistical classifier for diagnosing RRMS that provides a high degree of diagnostic capability, rivaling the accuracy of diagnosis by a clinical expert. Additionally, this method points to a diagnostic biochemical motif in the antibodies of RRMS patients, which may offer insight into the disease process.

Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest.

OBJECTIVE: We tested whether brain gray matter (GM) imaging measures can differentiate between multiple sclerosis (MS) and neuromyelitis optica (NMO) using random-forest classification. METHODS: Ninety participants (25 patients with MS, 30 patients with NMO, and 35 healthy controls [HCs]) were studied in Tehran, Iran, and 54 (24 patients with MS, 20 patients with NMO, and 10 HCs) in Padua, Italy. Participants underwent brain T1 and T2/fluid-attenuated inversion recovery MRI. Volume, thickness, and surface of 50 cortical GM regions and volumes of the deep GM nuclei were calculated and used to construct 3 random-forest models to classify patients as either NMO or MS, and separate each patient group from HCs. Clinical diagnosis was the gold standard against which the accuracy was calculated. RESULTS: The classifier distinguished patients with MS, who showed greater atrophy especially in deep GM, from those with NMO with an average accuracy of 74% (sensitivity/specificity: 77/72; p < 0.01). When we used thalamic volume (the most discriminating GM measure) together with the white matter lesion volume, the accuracy of the classification of MS vs NMO was 80%. The classifications of MS vs HCs and NMO vs HCs achieved higher accuracies (92% and 88%). CONCLUSIONS: GM imaging biomarkers, automatically obtained from clinical scans, can be used to distinguish NMO from MS, even in a 2-center setting, and may facilitate the differential diagnosis in clinical practice. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that GM imaging biomarkers can distinguish patients with NMO from those with MS.

A Novel Approach to Discriminate Subgroups in Multiple Sclerosis.

Multiple sclerosis (MS) is an autoimmune disease of central nervous system. Since different types of immune cells are involved in MS pathogenesis, in this study we aimed to evaluate serum levels of several immunological components including soluble CD4 (sCD4), sCD8, sCD163, and immunoglobulins as markers of activity of T-cells, macrophages, and B-cells in different types of MS. Serum levels of sCD4, sCD8, and sCD163 of patients with relapsing-remitting MS (RRMS, n=61), primary progressive MS (PRMS, n=31), secondary progressive MS (SPMS, n=31), clinical isolated syndrome (CIS, n=31) and neuromyelitis optica (NMO, n=31), and healthy controls (n=49) were measured using enzyme-linked immunosorbent assay (ELISA). Serum levels of Ig-G, Ig-M, and Ig-A were determined using nephelometric technique. Serum levels of sCD4, sCD8, sCD163, Ig-G, Ig-M, and Ig-A were significantly different in five groups of cases (p<0.05). Furthermore, application of stepwise method of discriminant analysis yielded 4 significant discriminant functions of classification due to the presence of six levels of categorical variables in the analysis. The most important function explained 85.5% of the total variance with the correlation value of 0.79. Taken together, our preliminary analysis suggests that although we found some functions to discriminate most of the patients, further studies will be required to individuate immunological markers characterizing the different type of MS including RRMS, PPMS, SPMS, CIS and NMO as proved by the data on sCD4, sCD163, Ig-M, and Ig-G in blood.

Association of nonmyeloablative hematopoietic stem cell transplantation with neurological disability in patients with relapsing-remitting multiple sclerosis.

IMPORTANCE: No current therapy for relapsing-remitting multiple sclerosis (MS) results in significant reversal of disability. OBJECTIVE: To determine the association of nonmyeloablative hematopoietic stem cell transplantation with neurological disability and other clinical outcomes in patients with MS. DESIGN, SETTING, AND PARTICIPANTS: Case series of patients with relapsing-remitting MS (n = 123) or secondary-progressive MS (n = 28) (mean age, 36 years; range, 18-60 years; 85 women) treated at a single US institution between 2003 and 2014 and followed up for 5 years. Final follow-up was completed in June 2014. INTERVENTIONS: Treatment with cyclophosphamide and alemtuzumab (22 patients) or cyclophosphamide and thymoglobulin (129 patients) followed by infusion of unmanipulated peripheral blood stem cells. MAIN OUTCOMES AND MEASURES: Primary end point was reversal or progression of disability measured by change in the Expanded Disability Status Scale (EDSS) score of 1.0 or greater (score range, 0-10). Secondary outcomes included changes in the Neurologic Rating Scale (NRS) score of 10 or greater (score range, 0-100), Multiple Sclerosis Functional Composite (MSFC) score, quality-of-life Short Form 36 questionnaire scores, and T2 lesion volume on brain magnetic resonance imaging scan. RESULTS: Outcome analysis was available for 145 patients with a median follow-up of 2 years and a mean of 2.5 years. Scores from the EDSS improved significantly from a pretransplant median of 4.0 to 3.0 (interquartile range [IQR], 1.5 to 4.0; n = 82) at 2 years and to 2.5 (IQR, 1.9 to 4.5; n = 36) at 4 years (P < .001 at each assessment). There was significant improvement in disability (decrease in EDSS score of ≥1.0) in 41 patients (50%; 95% CI, 39% to 61%) at 2 years and in 23 patients (64%; 95% CI, 46% to 79%) at 4 years. Four-year relapse-free survival was 80% and progression-free survival was 87%. The NRS scores improved significantly from a pretransplant median of 74 to 88.0 (IQR, 77.3 to 93.0; n = 78) at 2 years and to 87.5 (IQR, 75.0 to 93.8; n = 34) at 4 years (P < .001 at each assessment). The median MSFC scores were 0.38 (IQR, -0.01 to 0.64) at 2 years (P < .001) and 0.45 (0.04 to 0.60) at 4 years (P = .02). Total quality-of-life scores improved from a mean of 46 (95% CI, 43 to 49) pretransplant to 64 (95% CI, 61 to 68) at a median follow-up of 2 years posttransplant (n = 132) (P < .001). There was a decrease in T2 lesion volume from a pretransplant median of 8.57 cm3 (IQR, 2.78 to 22.08 cm3) to 5.74 cm3 (IQR, 1.88 to 14.45 cm3) (P < .001) at the last posttransplant assessment (mean follow-up, 27 months; n = 128). CONCLUSIONS AND RELEVANCE: Among patients with relapsing-remitting MS, nonmyeloablative hematopoietic stem cell transplantation was associated with improvement in neurological disability and other clinical outcomes. These preliminary findings from this uncontrolled study require confirmation in randomized trials.

Relapsing multiple sclerosis patients treated with disease modifying therapy exhibit highly variable disease progression: a predictive model.

OBJECTIVE: To describe a "new natural history" of multiple sclerosis (MS), characterizing three patterns of progression in Relapsing MS (RMS) patients during the "treatment era," using newly developed definitions. By utilizing our simple model we intend to predict which patients are most likely to reach an EDSS of 6.0. METHODS: We stratified MS progression into three distinct patterns: aggressive MS (AMS), intermediate MS (IMS) and mild MS (MMS), based on Expanded Disability Status Scale (EDSS) score rate of change. These groups were compared for progression of EDSS before and after reaching these definitions. RESULTS: The three groups remained significantly different in terms of disability throughout their disease courses p ≤ 0.001; 98% of the patients used disease modifying treatments (DMTs). AMS patients represent a significantly more disabling and aggressive form of MS than the IMS group. CONCLUSIONS: Transition from relatively mild MS to aggressive course may begin at any time in the first 15 years, despite DMTs. Our definition for AMS is unique and identifies a group of patients who become permanently disabled within two years after a variable amount of time in a benign phase, despite treatment with modern DMTs.

The diagnosis of multiple sclerosis and the various related demyelinating syndromes: a critical review.

Multiple sclerosis (MS), is a chronic disease of the central nervous system (CNS) characterized by loss of motor and sensory function, that results from immune-mediated inflammation, demyelination and subsequent axonal damage. MS is one of the most common causes of neurological disability in young adults. Several variants of MS (and CNS demyelinating syndromes in general) have been nowadays defined in an effort to increase the diagnostic accuracy, to identify the unique immunopathogenic profile and to tailor treatment in each individual patient. These include the initial events of demyelination defined as clinically or radiologically isolated syndromes (CIS and RIS respectively), acute disseminated encephalomyelitis (ADEM) and its variants (acute hemorrhagic leukoencephalitis-AHL, Marburg variant, and Balo's concentric sclerosis), Schilder's sclerosis, transverse myelitis, neuromyelitis optica (NMO and NMO spectrum of diseases), recurrent isolated optic neuritis and tumefactive demyelination. The differentiation between them is not only a terminological matter but has important implications on their management. For instance, certain patients with MS and prominent immunopathogenetic involvement of B cells and autoantibodies, or with the neuromyelitic variants of demyelination, may not only not respond well but even deteriorate under some of the first-line treatments for MS. The unique clinical and neuroradiological features, along with the immunological biomarkers help to distinguish these cases from classical MS. The use of such immunological and imaging biomarkers, will not only improve the accuracy of diagnosis but also contribute to the identification of the patients with CIS or RIS who, are at greater risk for disability progression (worse prognosis) or, on the contrary, will have a more benign course. This review summarizes in a critical way, the diagnostic criteria (historical and updated) and the definitions/characteristics of MS of the various variants/subtypes of CNS demyelinating syndromes.

Serum uric acid level in patients with relapsing-remitting multiple sclerosis.

Uric acid (UA) is a hydrophilic antioxidant product associated with multiple sclerosis (MS). We conducted a randomized case-control study to evaluate the serum level of UA in different phases of MS in comparison with levels in a healthy control population. Serum UA was checked in 130 patients with relapsing-remitting MS (85 patients in remitting and 45 patients in relapsing phase) and 50 age-matched controls using a quantitative enzyme-linked immunosorbent assay (ELISA). The mean concentrations of UA in serum was 6.41(±3.18)mg/dL in patients with remitting MS, 4.76(±1.66)mg/dL in patients with relapsing MS and 6.33(±2.94)mg/dL in controls. There was a significant difference between mean UA concentration in relapsing MS and remitting MS (p<0.001), and between patients with relapsing MS and controls (p=0.002); however, the difference between levels for patients in the remitting phase of MS and the control group was not significant (p=0.87). It seems probable that UA has a role in the prevention of disease activity in MS.

'Clinically definite benign multiple sclerosis', an unwarranted conceptual hodgepodge: evidence from a 30-year observational study.

BACKGROUND: Benign multiple sclerosis (BMS) is a controversial concept which is still debated. However identification of this kind of patients is crucial to prevent them from unnecessary exposure to aggressive and/or long term medical treatments. OBJECTIVES: To assess two definitions of 'clinically definite benign multiple sclerosis' (CDBMS) using long-term follow-up data, and to look for prognostic factors of CDBMS. METHODS: In 874 patients with definite relapsing-remitting MS, followed up for at least 10 years, disability was assessed using the Disability Status Scale (DSS). CDBMS was defined by either DSS score≤2 (CDBMS1 group) or DSS score≤ 3 (CDBMS2 group) at 10 years. We estimated the proportion of patients who were still benign at 20 and 30 years after clinical onset. RESULTS: CDBMS frequency estimates were 57.7% and 73.9% when using CDBMS1 and CDBMS2 definitions, respectively. In the CDBMS1 group, only 41.7% (105/252) of cases were still benign 10 years later, and 41.1% (23/56) after an additional decade, while there were 53.8% (162/301) and 59.5% (44/74) respectively in the CDBMS2 group. CONCLUSIONS: This 30-year observational study, which is one of the largest published series, indicates that favourable 10-year disability scores of DSS 2 or 3 fail to ensure a long-term benign course of multiple sclerosis. After every decade almost half of the CDBMS were no longer benign. CDBMS, as currently defined, is an unwarranted conceptual hodgepodge. Other criteria using new biomarkers (genetic, biologic or MRI) should be found to detect benign cases of MS.

ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple sclerosis lesion heterogeneity, as demonstrated by quantitative MRI.

BACKGROUND: There are modest correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes, as measured by T2-weighted (T2w) magnetic resonance imaging (MRI) scans (T2-WML). This may partly reflect pathological heterogeneity in WMLs, which is not apparent on T2w scans. OBJECTIVE: To determine if ADvanced IMage Algebra (ADIMA), a novel MRI post-processing method, can reveal WML heterogeneity from proton-density weighted (PDw) and T2w images. METHODS: We obtained conventional PDw and T2w images from 10 patients with relapsing-remitting MS (RRMS) and ADIMA images were calculated from these. We classified all WML into bright (ADIMA-b) and dark (ADIMA-d) sub-regions, which were segmented. We obtained conventional T2-WML and T1-WML volumes for comparison, as well as the following quantitative magnetic resonance parameters: magnetisation transfer ratio (MTR), T1 and T2. Also, we assessed the reproducibility of the segmentation for ADIMA-b, ADIMA-d and T2-WML. RESULTS: Our study's ADIMA-derived volumes correlated with conventional lesion volumes (p < 0.05). ADIMA-b exhibited higher T1 and T2, and lower MTR than the T2-WML (p < 0.001). Despite the similarity in T1 values between ADIMA-b and T1-WML, these regions were only partly overlapping with each other. ADIMA-d exhibited quantitative characteristics similar to T2-WML; however, they were only partly overlapping. Mean intra- and inter-observer coefficients of variation for ADIMA-b, ADIMA-d and T2-WML volumes were all < 6 % and < 10 %, respectively. CONCLUSION: ADIMA enabled the simple classification of WML into two groups having different quantitative magnetic resonance properties, which can be reproducibly distinguished.

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity.

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11 Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

The multiple sclerosis rating scale, revised (MSRS-R): development, refinement, and psychometric validation using an online community.

BACKGROUND: In developing the PatientsLikeMe online platform for patients with Multiple Sclerosis (MS), we required a patient-reported assessment of functional status that was easy to complete and identified disability in domains other than walking. Existing measures of functional status were inadequate, clinician-reported, focused on walking, and burdensome to complete. In response, we developed the Multiple Sclerosis Rating Scale (MSRS). METHODS: We adapted a clinician-rated measure, the Guy's Neurological Disability Scale, to a self-report scale and deployed it to an online community. As part of our validation process we reviewed discussions between patients, conducted patient cognitive debriefing, and made minor improvements to form a revised scale (MSRS-R) before deploying a cross-sectional survey to patients with relapsing-remitting MS (RRMS) on the PatientsLikeMe platform. The survey included MSRS-R and comparator measures: MSIS-29, PDDS, NARCOMS Performance Scales, PRIMUS, and MSWS-12. RESULTS: In total, 816 RRMS patients responded (19% response rate). The MSRS-R exhibited high internal consistency (Cronbach's alpha = .86). The MSRS-R walking item was highly correlated with alternative walking measures (PDDS, ρ = .84; MSWS-12, ρ = .83; NARCOMS mobility question, ρ = .86). MSRS-R correlated well with comparison instruments and differentiated between known groups by PDDS disease stage and relapse burden in the past two years. Factor analysis suggested a single factor accounting for 51.5% of variance. CONCLUSIONS: The MSRS-R is a concise measure of MS-related functional disability, and may have advantages for disease measurement over longer and more burdensome instruments that are restricted to a smaller number of domains or measure quality of life. Studies are underway describing the use of the instrument in contexts outside our online platform such as clinical practice or trials. The MSRS-R is released for use under creative commons license.

Classification of individuals based on ex-vivo glatiramer acetate-induced interferon-γ and interleukin-4 response.

BACKGROUND: Glatiramer acetate (GA) in multiple sclerosis acts through the induction of GA-specific T-helper 2 cells. Nevertheless, the phenomenon is not universal in patients, explaining individual differences in clinical response. OBJECTIVE: The objective of this article was to categorize GA-treated patients. METHOD: An enhanced quantitative PCR assay was used for measuring ex-vivo GA-induced IFNγ and IL4 mRNA responses in mononuclear cells from 23 healthy donors, 27 untreated patients, 33 short-term (≤6 months) and 77 long-term (>6 months) GA-treated patients. Thresholds for IFNγ and IL4 transcriptional response were calculated by ROC analysis and long-term treated patients were compared in terms of prognostic stratification. RESULTS: Thresholds for IFNγ and IL4 transcriptional response were calculated at 5.36 and 1.41 relative expression (RE). Finally, 67% of long-term treated patients scored above both response thresholds. These patients had a higher proportion of relapse-free subjects (74% vs 40% when compare to patients who scored below both thresholds) and a significantly better relapse-free survival rate (p=0.006; CI 0.29-0.75). The negative predictive value to predict adverse clinical outcome was 79% (CI 0.63-0.90), meaning that by a positive response, there is a 79% chance that the patient will not experience a negative outcome at 3 years. CONCLUSIONS: Our enhanced quantitative PCR assay produced clinically significant results for GA-treated patients. As such, if patients have a positive response, it means they have less chance of a relapse, while patients with a negative response have a greater probability of a worse outcome.

The psychosocial and cognitive impact of longstanding 'benign' multiple sclerosis.

BACKGROUND: Benign multiple sclerosis (BMS) is typically defined using the Expanded Disability Status Scale (EDSS), which relies heavily on ambulation. We set out to examine important psychosocial and cognitive outcomes in patients with longstanding BMS compared with patients who had recently progressed to 'no longer benign' (NLB). METHODS: A previously reported cohort of BMS (EDSS ≤3 at 20 years disease duration) were re-assessed 25-30 years post-onset. Patients remaining benign (EDSS ≤3 at re-assessment) were compared with those NLB for: depression (Beck Depression Inventory), fatigue (Modified Fatigue Impact Scale), health-related quality of life (MSQoL-54), cognition (Rao's Neuropsychological Screening Battery), and employment status. RESULTS: A total of 75% (66/88) of the original cohort were located. A total of 61 patients were re-assessed. Twenty-five patients (41%) had progressed in EDSS and were NLB. Compared with benign patients, those NLB were more likely to have: significant fatigue (15/36 [42%] vs. 18/25 [72%], p = 0.019); poorer physical functioning (mean MSQoL-54 = 67.30 vs. 50.89, p = 0.002); an MS-related negative change in employment status (13/36 [36%] vs. 21/25 [84%], p < 0.0001) and cognitive impairment (3/28 [11%] vs. 5/19 [26%]; trend only, p = 0.317). Depression and mental health quality if life differed little between the benign and NLB patients (p > 0.6). CONCLUSIONS: Despite remaining benign for 20 years, a significant proportion of patients progressed with further follow up. While neither depression nor patient-reported mental health quality of life was associated with EDSS progression, patients with longstanding 'benign' MS (EDSS ≤3 for 25+ years) had less fatigue, better physical quality of life and employment outcomes and infrequent cognitive impairment. Remaining benign over the long term, as defined by the EDSS, carried some advantages beyond ambulation.

Symptom cluster and physical activity in relapsing-remitting multiple sclerosis.

We compared the explanatory power of two symptom clusters that consisted of either three or five symptoms as correlates of physical activity in individuals with relapsing-remitting multiple sclerosis (RRMS; N = 218). The data were primarily analyzed using covariance modeling in Mplus 3.0. A symptom cluster of fatigue, depression, and pain had a moderate, negative relationship with physical activity, and this relationship was comparable in magnitude with a symptom cluster of fatigue, depression, pain, perceived cognitive dysfunction, and poor sleep quality. The relationships were attenuated after controlling for exercise history and neurological impairment. Such findings further support the consideration of a narrowly defined cluster of three symptoms as an independent correlate of physical activity in persons with RRMS.