Defining the clinical course of multiple sclerosis: the 2013 revisions.
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ABSTRACT:
SUBMITTER: Lublin FD
PROVIDER: S-EPMC4117366 | biostudies-literature | 2014 Jul
REPOSITORIES: biostudies-literature
Publications
Defining the clinical course of multiple sclerosis: the 2013 revisions.
Lublin Fred D FD Reingold Stephen C SC Cohen Jeffrey A JA Cutter Gary R GR Sørensen Per Soelberg PS Thompson Alan J AJ Wolinsky Jerry S JS Balcer Laura J LJ Banwell Brenda B Barkhof Frederik F Bebo Bruce B Calabresi Peter A PA Clanet Michel M Comi Giancarlo G Fox Robert J RJ Freedman Mark S MS Goodman Andrew D AD Inglese Matilde M Kappos Ludwig L Kieseier Bernd C BC Lincoln John A JA Lubetzki Catherine C Miller Aaron E AE Montalban Xavier X O'Connor Paul W PW Petkau John J Pozzilli Carlo C Rudick Richard A RA Sormani Maria Pia MP Stüve Olaf O Waubant Emmanuelle E Polman Chris H CH
Neurology 20140528 3
Accurate clinical course descriptions (phenotypes) of multiple sclerosis (MS) are important for communication, prognostication, design and recruitment of clinical trials, and treatment decision-making. Standardized descriptions published in 1996 based on a survey of international MS experts provided purely clinical phenotypes based on data and consensus at that time, but imaging and biological correlates were lacking. Increased understanding of MS and its pathology, coupled with general concern ...[more]
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Project description:BackgroundLow vitamin D and/or sun exposure have been associated with increased risk of multiple sclerosis (MS) onset. However, comparatively, few studies have prospectively examined associations between these factors and clinical course.ObjectivesTo evaluate the association of sun exposure parameters and vitamin D levels with conversion to MS and relapse risk in a prospectively monitored cohort of 145 participants followed after a first demyelinating event up to 5-year review (AusLong Study).MethodsSun exposure prior to and after onset measured by annual questionnaire; ultraviolet radiation (UVR) "load" estimated by location of residence over the life course and ambient UVR levels. Serum 25-hydroxyvitamin D [25(OH)D] concentrations measured at baseline, 2/3-year, and 5-year review. MS conversion and relapse assessed by neurologist assessment and medical record review.ResultsOver two-thirds (69%) of those followed to 5-year review (100/145) converted to MS, with a total of 252 relapses. Higher pre-MS onset sun exposure was associated with reduced risk of MS conversion, with internal consistency between measures and dose-response relationships. Analogous associations were also seen with risk of relapse, albeit less strong. No consistent associations were observed between postonset sun exposure and clinical course, however. Notably, those who increased their sun exposure during follow-up had significantly reduced hazards of MS conversion and relapse. Serum 25(OH)D levels and vitamin D supplementation were not associated with conversion to MS or relapse hazard.ConclusionWe found that preonset sun exposure was protective against subsequent conversion to MS and relapses. While consistent associations between postonset sun exposure or serum 25(OH)D level and clinical course were not evident, possibly masked by behavior change, those participants who markedly increased their sun exposure demonstrated a reduced MS conversion and relapse hazard, suggesting beneficial effects of sun exposure on clinical course.
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Project description:ImportanceBiomarkers to estimate long-term outcomes in patients with multiple sclerosis (MS) and to assign patients to individual treatment regimens are urgently needed.ObjectiveTo assess whether retinal layer volumes are correlated with immune cell subsets and immunoglobulin indices in the cerebrospinal fluid and whether retinal layer volumes alone or in combination with intrathecal variables are associated with worsening of disease in patients with relapsing-remitting MS.Design, setting, and participantsThis observational cohort study included 312 patients with relapsing-remitting MS in 2 independent cohorts (72 patients with short disease duration [cohort 1] and 240 patients with longer disease duration [cohort 2]) treated at a single German university hospital from April 15, 2013, through November 11, 2015.Main outcomes and measuresThe common ganglion cell and inner plexiform layer (GCIPL) and inner nuclear layer (INL) volumes were tested for association with the immunoglobulin indices and the frequencies of immune cells in the cerebrospinal fluid (including B cells, T cells, and natural killer cells) (cohort 1). Volumes of GCIPL alone (cohorts 1 and 2) or GCIPL corrected for intrathecal B-cell frequencies (cohort 1) were tested for their association with worsening disability.ResultsA total of 312 patients (212 women [67.9%] and 100 men [32.1%]; median age, 34.0 years [interquartile range (IQR), 28.0-42.0 years]) were available for analysis. In cohort 1 (50 women [69.4%] and 22 men [30.6%]; median age, 31.0 years [IQR, 26.3-38.3 years]), with short disease durations (median, 1.0 months [IQR, 1.0-2.0 months]), low GCIPL volumes were associated with increased intrathecal B-cell frequencies (median, 1.96% [IQR, 1.45%-4.20%]) and intrathecal IgG synthesis (median cerebrospinal fluid/serum IgG index, 0.78 [IQR, 0.53-1.07]). The INL volumes correlated with the frequencies of intrathecal CD56bright natural killer cells (r = 0.28; P = .007). Individuals with low GCIPL volumes (<1.99 mm3) had a 6.4-fold risk for worsening disability during follow-up compared with patients with higher GCIPL values (95% CI, 1.7-24.2; P = .007). This finding was reproduced in cohort 2 (162 women [67.5%] and 78 men [32.5%]; median age, 34.0 years [IQR, 29.0-42.0 years]) consisting of patients with longer disease durations (median, 36.0 months [IQR, 21.0-60.0 months]) (hazard ratio, 2.4; 95% CI, 1.2-4.8; P = .02). In both cohorts, INL volumes correlated with the prospective increase in T2 lesion load and the number of gadolinium-enhancing lesions.Conclusions and relevanceRetinal layers reflect different aspects of disease activity during MS. Loss of GCIPL is associated with intrathecal B-cell immunity and constitutes an independent risk factor for worsening disability, whereas high INL volumes are associated with activity on magnetic resonance imaging in the brain parenchyma. Thus, retinal optical coherence tomography might be a means to support stratification of patients with MS for different therapeutic regimens.
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Project description:Multiple sclerosis (MS), an inflammatory disease affecting the central nervous system, is considered to exhibit an important neurodegenerative component as well. Laquinimod is an orally administered quinoline-3-carboxamide under development for the treatment of MS. In vitro and animal studies have revealed various mechanisms by which laquinimod may exert its effects on the immune and nervous systems. These include effects on the innate immune system that promote the differentiation of anti-inflammatory/regulatory T cells, the activation of microglia cells, an increase in the expression of brain-derived neurotrophic factor, as well as the prevention of inflammation-induced excitotoxicity. Two phase III studies revealed the clinical benefits of laquinimod in patients with relapsing-remitting MS and exhibited a benign safety profile for this drug. Ongoing clinical trials will help to define the optimal dose and indication for laquinimod in MS. This article reviews current experimental and clinical evidence on the role of laquinimod in patients with this disabling disease.
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Project description:Background and purposeDelayed-release dimethyl fumarate (DMF, also known as gastro-resistant DMF), demonstrated efficacy and safety in relapsing-remitting multiple sclerosis in the 2-year, randomized, placebo-controlled, phase 3 DEFINE and CONFIRM trials. A post hoc analysis of integrated data from DEFINE and CONFIRM was conducted to determine the temporal profile of the clinical and neuroradiological effects of DMF.MethodsEligible patients were randomized to receive placebo, DMF 240 mg twice (BID) or three times (TID) daily or glatiramer acetate (GA; reference comparator; CONFIRM only) for up to 96 weeks. Patients in the GA group were excluded from this analysis.ResultsA total of 2301 patients were randomized and received treatment with placebo (n = 771) or DMF BID (n = 769) or TID (n = 761). DMF significantly reduced the annualized relapse rate beginning in weeks 0-12 (BID, P = 0.0159; TID, P = 0.0314); the proportion of patients relapsed beginning at week 10 (BID, P = 0.0427) and week 12 (TID, P = 0.0451); and the proportion of patients with 12-week confirmed disability progression beginning at week 62 (BID, P = 0.0454) and week 72 (TID, P = 0.0399), compared with placebo. These effects were sustained throughout the 2-year study period. DMF significantly reduced the odds of having a higher number of gadolinium-enhancing lesions by 88% (BID) and 75% (TID) and the mean number of new or enlarging T2 lesions by 72% (BID) and 67% (TID), from the first post-baseline magnetic resonance imaging assessment at 24 weeks (all P < 0.0001 versus placebo).ConclusionsIn phase 3 clinical trials, DMF demonstrated rapid and sustained clinical and neuroradiological efficacy in relapsing-remitting multiple sclerosis.
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Project description:Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only "real world" data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given "confidence threshold". For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how "real world" data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values.
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Project description:BackgroundDigital technologies have afforded people living with multiple sclerosis (MS) access to telehealth consultations, diagnostic tools, and monitoring. Although health care professionals remain the most trusted source of information, the internet has emerged as a valuable resource for providing MS-related information, particularly during the COVID-19 pandemic. Notably, people living with MS are increasingly seeking educational content for a range of topics related to the self-management of MS; however, web-based information seeking remains largely underevaluated. To address this gap and ensure that web-based health-related information is accessible and engaging, this study used qualitative methods to analyze the reflections from participants of web-based educational programs for people living with MS.ObjectiveThis study aimed to explore the motivations, behaviors, and expectations of web-based health information seeking for people living with MS.MethodsWe conducted semistructured interviews for 38 people living with MS 1 month after they completed the novel MS Online Course, which provided information on modifiable lifestyle-related risk factors for people living with MS. Of the 38 participants, 22 (58%) completed the intervention course and 16 (42%) completed the standard care course. Inductive thematic analysis was used within a qualitative paradigm, and 2 authors coded each interview separately and arrived at themes with consensus.ResultsWe identified 2 themes: motivation to learn and MS information on the web. The diagnosis of MS was described as a pivotal moment for precipitating web-based information seeking. People living with MS sought lifestyle-related information to facilitate self-management and increase control of their MS. Although social media sites and MS websites were considered useful for providing both support and information, discretion was needed to critically appraise information. Recognizable institutions were frequently accessed because of their trustworthiness.ConclusionsThis study provided novel insights into the motivations of people living with MS for seeking web-based health information. Furthermore, their preferences for the content and format of the web-based information accessed and their experiences and reactions to this information were explored. These findings may guide educators, researchers, and clinicians involved in MS care to optimize the engagement and processing of web-based health information seeking by people living with MS.
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Project description:Little is known about the interplay between the autonomic nervous system and disease activity in multiple sclerosis (MS). We examined the relationship between heart rate variability (HRV), a reliable measure of vagal nerve function, and disease characteristics in a prospective MS cohort. Standard deviation of each normal-to-normal inter-beat interval (SDNN) and root mean square of successive differences (RMSSD), global indices of HRV, were measured in 114 MS patients, which included four predefined subgroups, and 30 age and sex-matched healthy controls (HC). We assessed group differences at baseline, HRV reproducibility at month 3, and used logistic regression modeling to relate baseline HRV with relapse occurrence. No significant HRV differences were found between MS and HC and between MS subgroups. In MS patients, both HRV indices correlated with age (r = -0.278, p = 0.018 and r = -0.319, p < 0.001, respectively) and with month 3 assessments (r = 0.695 and r = 0.760, p < 0.001). Higher SDNN and RMSSD at baseline were associated with self-reported relapses at month 3 (OR = 1.053, 95% CI (1.013-1.095), p = 0.009 and OR = 1.065, 95% CI (1.016-1.117), p = 0.009), and SDNN at baseline with relapses at month 12 (OR = 1.034, 95% CI (1.009-1.059), p = 0.008; ROC, AUC = 0.733, p = 0.002). There were no baseline HRV differences between MS and HC or between subgroups. Post-hoc analysis showed an association with an increased relapse risk.
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Project description:BACKGROUND: The aim of this study was to assess the diagnostic accuracy (sensitivity and specificity) of clinical, imaging and motor evoked potentials (MEP) for predicting the short-term prognosis of multiple sclerosis (MS). METHODS: We obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1) expanded disability status scale (EDSS), 2) disability progression, and 3) new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks) and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center. RESULTS: We found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS), MRI lesion load and central motor conduction time (CMCT), were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT) we developed a neural network (NNet) that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80%) for predicting the change in the EDSS two years later. CONCLUSIONS: The usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability.
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Project description:ObjectiveTo explore the value of machine learning methods for predicting multiple sclerosis disease course.Methods1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.ResultsBaseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%). Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.InterpretationSVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.
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Project description:The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women's Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients' clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A "threshold" was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course.
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