Predicting short-term disability in multiple sclerosis

Predictors of long-term disability in multiple sclerosis patients using routine magnetic resonance imaging data: A 15-year retrospective study

INTRODUCTION

Early identification of patients at high risk of progression could help with a personalised treatment strategy. Magnetic resonance imaging (MRI) measures have been proposed to predict long-term disability in multiple sclerosis (MS), but a reliable predictor that can be easily implemented clinically is still needed.

AIM

Assess MRI measures during the first 5 years of the MS disease course for the ability to predict progression at 10+ years.

METHODS

Eighty-two MS patients (53 females), with ≥10 years of clinical follow-up and having two MRI scans, were included. Clinical data were obtained at baseline, follow-up and at ≥10 years. White matter lesion (WML) counts and volumes, and four linear brain sizes were measured on T2/FLAIR 'Fluid-Attenuated-Inversion-Recovery' and T1-weighted images.

RESULTS

Baseline and follow-up inter-caudate diameter (ICD) and third ventricular width (TVW) measures correlated positively with Expanded Disability Status Scale, ≥10 or more of WMLs showed a high sensitivity in predicting progression, at ≥10 years. A steeper rate of lesion volume increase was observed in subjects converting to secondary progressive MS. The sensitivity and specificity of both ICD and TVW, to predict disability at ≥10 years were 60% and 64%, respectively.

CONCLUSION

Despite advances in brain imaging and computerised volumetric analysis, ICD and TVW remain relevant as they are simple, fast and have the potential in predicting long-term disability. However, in this study, despite the statistical significance of these measures, the clinical utility is still not reliable.

Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet.

OBJECTIVES

To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS.

SEARCH METHODS

We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies.

SELECTION CRITERIA

We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression.

MAIN RESULTS

We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal.

AUTHORS' CONCLUSIONS

The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.

Approaches and challenges in the diagnosis and management of secondary progressive multiple sclerosis: A Central Eastern European perspective from healthcare professionals

Secondary progressive multiple sclerosis (SPMS) is a debilitating condition characterized by gradual worsening after an initial relapsing disease course. Despite the recent advances in our understanding of the disease, the diagnosis and treatment of SPMS continue to be challenging in routine clinical practice. The aim of this review article is to present the views of leading MS experts on the challenges in the diagnosis and management of SPMS and clinicians' perspectives in Central and Eastern Europe. This article also provides recommendations of MS experts to improve the situation with diagnosis and management of SPMS. Many countries within Central and Eastern Europe have high prevalence of MS (>100 per 100,000 population). Consistent with the global trend, in the absence of reliable tests or biomarkers, SPMS at early stage remains undiagnosed. Due to diagnostic uncertainty and lack of a universally accepted disease definition, clinicians rely more on retrospective analysis of the clinical symptoms to confirm the diagnosis. With the lack of awareness and poor understanding of the timing of the onset of SPMS, clinicians may tend to direct attention to relapses than the symptoms of progression, which leads to underestimation of SPMS. Although several predictors of progression to SPMS have been identified, their predictive value is highly variable. Therefore, defining the transitioning period as a separate stage of MS is essential. According to experts' opinion, frequent follow-up of patients and periodic assessment of progression are recommended for the timely identification of patients transitioning from RRMS to SPMS. MSProDiscuss Tool is an example of a quick assessment tool for identifying patients progressing from RRMS to SPMS. MS progression is usually assessed by changes in Expanded Disability Status Scale (EDSS) scores. As EDSS scores tend to fluctuate when measured in the short term (3-6 months), a longer period (≥12 months) may be needed to confirm the progression. Assessment of cognitive function is also important for evaluating secondary progression. Compartmentalization of inflammation within the central nervous system is an important reason behind the limited success of disease-modifying therapies (DMTs) for treating SPMS. Most of the DMTs fail to cross the blood-brain barrier; only 38% of the tested DMTs achieved their primary endpoint in SPMS. In Europe, siponimod is the first oral treatment for adults with active SPMS. Particularly, in Central and Eastern Europe, patients with SPMS are still being prescribed less efficacious DMTs and interferons. The absence of alternative treatments in SPMS supports the use of new products (siponimod and others); however the decision to initiate siponimod therapy in more severe patients (EDSS score of 7 or higher) should be individualized in consultation with the payers. The focus should be on early treatment initiation to delay disease progression.

Systematic review of prediction models in relapsing remitting multiple sclerosis

The natural history of relapsing remitting multiple sclerosis (RRMS) is variable and prediction of individual prognosis challenging. The inability to reliably predict prognosis at diagnosis has important implications for informed decision making especially in relation to disease modifying therapies. We conducted a systematic review in order to collate, describe and assess the methodological quality of published prediction models in RRMS. We searched Medline, Embase and Web of Science. Two reviewers independently screened abstracts and full text for eligibility and assessed risk of bias. Studies reporting development or validation of prediction models for RRMS in adults were included. Data collection was guided by the checklist for critical appraisal and data extraction for systematic reviews (CHARMS) and applicability and methodological quality assessment by the prediction model risk of bias assessment tool (PROBAST). 30 studies were included in the review. Applicability was assessed as high risk of concern in 27 studies. Risk of bias was assessed as high for all studies. The single most frequently included predictor was baseline EDSS (n = 11). T2 Lesion volume or number and brain atrophy were each retained in seven studies. Five studies included external validation and none included impact analysis. Although a number of prediction models for RRMS have been reported, most are at high risk of bias and lack external validation and impact analysis, restricting their application to routine clinical practice.

Classification of radiologically isolated syndrome and clinically isolated syndrome with machine-learning techniques

BACKGROUND AND PURPOSE

The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the difference between early MS [i.e. clinically isolated syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS.

METHODS

We used a multimodal 3-T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS for single-subject level classification.

RESULTS

The best proposed models to predict the diagnosis of CIS and RIS were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782].

CONCLUSIONS

A machine-learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS) with an accuracy of 78%.

Retinal measurements predict 10-year disability in multiple sclerosis

Objective

Optical coherence tomography (OCT)‐derived measures of the retina correlate with disability and cortical gray matter atrophy in multiple sclerosis (MS); however, whether such measures predict long‐term disability is unknown. We evaluated whether a single OCT and visual function assessment predict the disability status 10 years later.

Methods

Between 2006 and 2008, 172 people with MS underwent Stratus time domain‐OCT imaging [160 with measurement of total macular volume (TMV)] and high and low‐contrast letter acuity (LCLA) testing (n = 150; 87%). All participants had Expanded Disability Status Scale (EDSS) assessments at baseline and at 10‐year follow‐up. We applied generalized linear regression models to assess associations between baseline TMV, peripapillary retinal nerve fiber layer (pRNFL) thickness, and LCLA with 10‐year EDSS scores (linear) and with clinically significant EDSS worsening (binary), adjusting for age, sex, optic neuritis history, and baseline disability status.

Results

In multivariable models, lower baseline TMV was associated with higher 10‐year EDSS scores (mean increase in EDSS of 0.75 per 1 mm³ loss in TMV (mean difference = 0.75; 95% CI: 0.11–1.39; P = 0.02). In analyses using tertiles, individuals in the lowest tertile of baseline TMV had an average 0.86 higher EDSS scores at 10 years (mean difference = 0.86; 95% CI: 0.23–1.48) and had over 3.5‐fold increased odds of clinically significant EDSS worsening relative to those in the highest tertile of baseline TMV (OR: 3.58; 95% CI: 1.30–9.82; P_trend = 0.008). pRNFL and LCLA predicted the 10‐year EDSS scores only in univariate models.

Interpretation

Lower baseline TMV measured by OCT significantly predicts higher disability at 10 years, even after accounting for baseline disability status.

Diffusion tensor imaging and disability progression in multiple sclerosis: A 4-year follow-up study

OBJECTIVES

Diffusion tensor imaging (DTI) is sensitive technique to detect widespread changes in water diffusivity in the normal-appearing white matter (NAWM) that appears unaffected in conventional magnetic resonance imaging. We aimed to investigate the prognostic value and stability of DTI indices in the NAWM of the brain in an assessment of disability progression in patients with a relapsing-onset multiple sclerosis (MS).

METHODS

Forty-six MS patients were studied for DTI indices (fractional anisotropy (FA), mean diffusivity (MD), radial (RD), and axial (AD) diffusivity) in the NAWM of the corpus callosum (CC) and the internal capsule at baseline and at 1 year after. DTI analysis for 10 healthy controls was also performed at baseline. Simultaneously, focal brain lesion volume and atrophy measurements were done at baseline for MS patients. Associations between DTI indices, volumetric measurements, and disability progression over 4 years were studied by multivariate logistic regression analysis.

RESULTS

At baseline, most DTI metrics differed significantly between MS patients and healthy controls. There was tendency for associations between baseline DTI indices in the CC and disability progression (p < 0.05). Changes in DTI indices over 1 year were observed only in the CC (p < 0.008), and those changes were not found to predict clinical worsening over 4 years. Clear-cut association with disability progression was not detected for baseline volumetric measurements.

CONCLUSION

Aberrant diffusivity measures in the NAWM of the CC may provide additional information for individual disability progression over 4 years in MS with the relapsing-onset disease. CC may be a good target for DTI measurements in monitoring disease activity in MS, and more studies are needed to assess the related prognostic potential.

Estimation of annual probabilities of changing disability levels in Australians with relapsing-remitting multiple sclerosis

BACKGROUND

Transition probabilities are the engine within many health economics decision models. However, the probabilities of progression of disability due to multiple sclerosis (MS) have not previously been estimated in Australia.

OBJECTIVES

To estimate annual probabilities of changing disability levels in Australians with relapsing-remitting MS (RRMS).

METHODS

Combining data from Ausimmune/Ausimmune Longitudinal (2003-2011) and Tasmanian MS Longitudinal (2002-2005) studies (n = 330), annual transition probabilities were obtained between no/mild (Expanded Disability Status Scale (EDSS) levels 0-3.5), moderate (EDSS 4-6.0) and severe (EDSS 6.5-9.5) disability.

RESULTS

From no/mild disability, 6.4% (95% confidence interval (CI): 4.7-8.4) and 0.1% (0.0-0.2) progressed to moderate and severe disability annually, respectively. From moderate disability, 6.9% (1.0-11.4) improved (to no/mild state) and 2.6% (1.1-4.5) worsened. From severe disability, 0.0% improved to moderate and no/mild disability. Male sex, age at onset, longer disease duration, not using immunotherapies greater than 3 months and a history of relapse were related to higher probabilities of worsening.

CONCLUSION

We have estimated probabilities of changing disability levels in Australians with RRMS. Probabilities differed between various subgroups, but due to small sample sizes, results should be interpreted with caution. Our findings will be helpful in predicting long-term disease outcomes and in health economic evaluations of MS.

Modelling disease progression in relapsing-remitting onset multiple sclerosis using multilevel models applied to longitudinal data from two natural history cohorts and one treated cohort

BACKGROUND

The ability to better predict disease progression represents a major unmet need in multiple sclerosis (MS), and would help to inform therapeutic and management choices.

OBJECTIVES

To develop multilevel models using longitudinal data on disease progression in patients with relapsing-remitting MS (RRMS) or secondary-progressive MS (SPMS); and to use these models to estimate the association of disease-modifying therapy (DMT) with progression.

DESIGN

Secondary analysis of three MS cohorts.

SETTING

Two natural history cohorts: University of Wales Multiple Sclerosis (UoWMS) cohort, UK, and British Columbia Multiple Sclerosis (BCMS) cohort, Canada. One observational DMT-treated cohort: UK MS risk-sharing scheme (RSS).

PARTICIPANTS

The UoWMS database has > 2000 MS patients and the BCMS database (as of 2009) has > 5900 MS patients. All participants who had definite MS (RRMS/SPMS), who reached the criteria set out by the Association of British Neurologists (ABN) for eligibility for DMT [i.e. age ≥ 18 years, Expanded Disability Status Scale (EDSS) score of ≤ 6.5, occurrence of two or more relapses in the previous 2 years] and who had at least two repeated outcome measures were included: 404 patients for the UoWMS cohort and 978 patients for the BCMS cohort. Through the UK MS RSS scheme, 5583 DMT-treated patients were recruited, with the analysis sample being the 4137 who had RRMS and were eligible and treated at baseline, with at least one valid EDSS score post baseline.

MAIN OUTCOME MEASURES

EDSS score observations post ABN eligibility.

METHODS

We used multilevel models in the development cohort (UoWMS) to develop a model for EDSS score with time since ABN eligibility, allowing for covariates and appropriate transformation of outcome and/or time. These methods were then applied to the BCMS cohort to obtain a 'natural history' model for changes in the EDSS score with time. We then used this natural history model to predict the trajectories of EDSS score in treated patients in the UK MS RSS database. Differences between the progression predicted by the natural history model and the progression observed at 6 years' follow-up for the UK MS RSS cohort were used as indicators of the effectiveness of the DMTs. Previously developed utility scores were assigned to each EDSS score, and differences in utility also examined.

RESULTS

The model best fitting the UoWMS data showed a non-linear increase in EDSS score over time since ABN eligibility. This model fitted the BCMS cohort data well, with similar coefficients, and the BCMS model predicted EDSS score in UoWMS data with little evidence of bias. Using the natural history model predicts EDSS score in a treated cohort (UK MS RSS) higher than that observed [by 0.59 points (95% confidence interval 0.54 to 0.64 points)] at 6 years post treatment.

LIMITATIONS

Only two natural history cohorts were compared, limiting generalisability. The comparison of a treated cohort with untreated cohorts is observational, thus limiting conclusions about causality.

CONCLUSIONS

EDSS score progression in two natural history cohorts of MS patients showed a similar pattern. Progression in the natural history cohorts was slightly faster than EDSS score progression in the DMT-treated cohort, up to 6 years post treatment.

FUTURE WORK

Long-term follow-up of randomised controlled trials is needed to replicate these findings and examine duration of any treatment effect.

FUNDING DETAILS

The National Institute for Health Research Health Technology Assessment programme.

The Role of T1-Weighted Derived Measures of Neurodegeneration for Assessing Disability Progression in Multiple Sclerosis

Introduction

Multiple sclerosis (MS) is characterised by the accumulation of permanent neurological disability secondary to irreversible tissue loss (neurodegeneration) in the brain and spinal cord. MRI measures derived from T1-weighted image analysis (i.e., black holes and atrophy) are correlated with pathological measures of irreversible tissue loss. Quantifying the degree of neurodegeneration in vivo using MRI may offer a surrogate marker with which to predict disability progression and the effect of treatment. This review evaluates the literature examining the association between MRI measures of neurodegeneration derived from T1-weighted images and disability in MS patients.

Methods

A systematic PubMed search was conducted in January 2017 to identify MRI studies in MS patients investigating the relationship between “black holes” and/or atrophy in the brain and spinal cord, and disability. Results were limited to human studies published in English in the previous 10 years.

Results

A large number of studies have evaluated the association between the previous MRI measures and disability. These vary considerably in terms of study design, duration of follow-up, size, and phenotype of the patient population. Most, although not all, have shown that there is a significant correlation between disability and black holes in the brain, as well as atrophy of the whole brain and grey matter. The results for brain white matter atrophy are less consistently positive, whereas studies evaluating spinal cord atrophy consistently showed a significant correlation with disability. Newer ways of measuring atrophy, thanks to the development of segmentation and voxel-wise methods, have allowed us to assess the involvement of strategic regions of the CNS (e.g., thalamus) and to map the regional distribution of damage. This has resulted in better correlations between MRI measures and disability and in the identification of the critical role played by some CNS structures for MS clinical manifestations.

Conclusion

The evaluation of MRI measures of atrophy as predictive markers of disability in MS is a highly active area of research. At present, measurement of atrophy remains within the realm of clinical studies, but its utility in clinical practice has been recognized and barriers to its implementation are starting to be addressed.

MRI in the assessment and monitoring of multiple sclerosis: an update on best practice

Magnetic resonance imaging (MRI) has developed into the most important tool for the diagnosis and monitoring of multiple sclerosis (MS). Its high sensitivity for the evaluation of inflammatory and neurodegenerative processes in the brain and spinal cord has made it the most commonly used technique for the evaluation of patients with MS. Moreover, MRI has become a powerful tool for treatment monitoring, safety assessment as well as for the prognostication of disease progression. Clinically, the use of MRI has increased in the past couple decades as a result of improved technology and increased availability that now extends well beyond academic centers. Consequently, there are numerous studies supporting the role of MRI in the management of patients with MS. The aim of this review is to summarize the latest insights into the utility of MRI in MS.

Defining Disease Activity and Response to Therapy in MS

OPINION STATEMENT

Disease activity in multiple sclerosis (MS) has classically been defined by the occurrence of new neurological symptoms and the rate of relapses. Definition of disease activity has become more refined with the use of clinical markers, evaluating ambulation, dexterity, and cognition. Magnetic resonance imaging (MRI) has become an important tool in the investigation of disease activity. Number of lesions as well as brain atrophy have been used as surrogate outcome markers in several clinical trials, for which a reduction in these measures is appreciated in most treatment studies. With the increasing availability of new medications, the overall goal is to minimize inflammation to decrease relapse rate and ultimately prevent long-term disability. The aim of this review is to give an overview on commonly used clinical and imaging markers to monitor disease activity in MS, with emphasis on their use in clinical studies, and to give a recommendation on how to utilize these measures in clinical practice for the appropriate assessment of therapeutic response.

Exploration of machine learning techniques in predicting multiple sclerosis disease course

Objective

To explore the value of machine learning methods for predicting multiple sclerosis disease course.

Methods

1693 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.

Results

Baseline 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.

Interpretation

SVM 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.

Brain MRI atrophy quantification in MS: From methods to clinical application

Patients with the main clinical phenotypes of multiple sclerosis (MS) manifest varying degrees of brain atrophy beyond that of normal aging. Assessment of atrophy helps to distinguish clinically and cognitively deteriorating patients and predicts those who will have a less-favorable clinical outcome over the long term. Atrophy can be measured from brain MRI scans, and many technological improvements have been made over the last few years. Several software tools, with differing requirements on technical ability and levels of operator intervention, are currently available and have already been applied in research or clinical trial settings. Despite this, the measurement of atrophy in routine clinical practice remains an unmet need. After a short summary of the pathologic substrates of brain atrophy in MS, this review attempts to guide the clinician towards a better understanding of the methods currently used for quantifying brain atrophy in this condition. Important physiologic factors that affect brain volume measures are also considered. Finally, the most recent research on brain atrophy in MS is summarized, including whole brain and various compartments thereof (i.e., white matter, gray matter, selected CNS structures). Current methods provide sufficient precision for cohort studies, but are not adequate for confidently assessing changes in individual patients over the scale of months or a few years.

Structural Brain Network Characteristics Can Differentiate CIS from Early RRMS

Focal demyelinated lesions, diffuse white matter (WM) damage, and gray matter (GM) atrophy influence directly the disease progression in patients with multiple sclerosis. The aim of this study was to identify specific characteristics of GM and WM structural networks in subjects with clinically isolated syndrome (CIS) in comparison to patients with early relapsing-remitting multiple sclerosis (RRMS). Twenty patients with CIS, 33 with RRMS, and 40 healthy subjects were investigated using 3 T-MRI. Diffusion tensor imaging was applied, together with probabilistic tractography and fractional anisotropy (FA) maps for WM and cortical thickness correlation analysis for GM, to determine the structural connectivity patterns. A network topology analysis with the aid of graph theoretical approaches was used to characterize the network at different community levels (modularity, clustering coefficient, global, and local efficiencies). Finally, we applied support vector machines (SVM) to automatically discriminate the two groups. In comparison to CIS subjects, patients with RRMS were found to have increased modular connectivity and higher local clustering, highlighting increased local processing in both GM and WM. Both groups presented increased modularity and clustering coefficients in comparison to healthy controls. SVM algorithms achieved 97% accuracy using the clustering coefficient as classifier derived from GM and 65% using WM from probabilistic tractography and 67% from modularity of FA maps to differentiate between CIS and RRMS patients. We demonstrate a clear increase of modular and local connectivity in patients with early RRMS in comparison to CIS and healthy subjects. Based only on a single anatomic scan and without a priori information, we developed an automated and investigator-independent paradigm that can accurately discriminate between patients with these clinically similar disease entities, and could thus complement the current dissemination-in-time criteria for clinical diagnosis.

A pharmacogenetic study implicates SLC9a9 in multiple sclerosis disease activity

OBJECTIVE

A proportion of multiple sclerosis (MS) patients experience disease activity despite treatment. The early identification of the most effective drug is critical to impact long-term outcome and to move toward a personalized approach. The aim of the present study is to identify biomarkers for further clinical development and to yield insights into the pathophysiology of disease activity.

METHODS

We performed a genome-wide association study in interferon-β (IFNβ)-treated MS patients followed by validation in 3 independent cohorts. The role of the validated variant was examined in several RNA data sets, and the function of the presumed target gene was explored using an RNA interference approach in primary T cells in vitro.

RESULTS

We found an association between rs9828519(G) and nonresponse to IFNβ (pdiscovery = 4.43 × 10(-8)) and confirmed it in a meta-analysis across 3 replication data sets (preplication = 7.78 × 10(-4)). Only 1 gene is found in the linkage disequilibrium block containing rs9828519: SLC9A9. Exploring the function of this gene, we see that SLC9A9 mRNA expression is diminished in MS subjects who are more likely to have relapses. Moreover, SLC9A9 knockdown in T cells in vitro leads an increase in expression of IFNγ, which is a proinflammatory molecule.

INTERPRETATION

This study identifies and validates the role of rs9828519, an intronic variant in SLC9A9, in IFNβ-treated subjects, demonstrating a successful pharmacogenetic screen in MS. Functional characterization suggests that SLC9A9, an Na(+) -H(+) exchanger found in endosomes, appears to influence the differentiation of T cells to a proinflammatory fate and may have a broader role in MS disease activity, outside of IFNβ treatment.

A longitudinal model for disease progression was developed and applied to multiple sclerosis

Objectives

To develop a model of disease progression using multiple sclerosis (MS) as an exemplar.

Study Design and Settings

Two observational cohorts, the University of Wales MS (UoWMS), UK (1976), and British Columbia MS (BCMS) database, Canada (1980), with longitudinal disability data [the Expanded Disability Status Scale (EDSS)] were used; individuals potentially eligible for MS disease-modifying drugs treatments, but who were unexposed, were selected. Multilevel modeling was used to estimate the EDSS trajectory over time in one data set and validated in the other; challenges addressed included the choice and function of time axis, complex observation-level variation, adjustments for MS relapses, and autocorrelation.

Results

The best-fitting model for the UoWMS cohort (404 individuals, and 2,290 EDSS observations) included a nonlinear function of time since onset. Measurement error decreased over time and ad hoc methods reduced autocorrelation and the effect of relapse. Replication within the BCMS cohort (978 individuals and 7,335 EDSS observations) led to a model with similar time (years) coefficients, time [0.22 (95% confidence interval {CI}: 0.19, 0.26), 0.16 (95% CI: 0.10, 0.22)] and log time [−0.13 (95% CI: −0.39, 0.14), −0.15 (95% CI: −0.70, 0.40)] for BCMS and UoWMS, respectively.

Conclusion

It is possible to develop robust models of disability progression for chronic disease. However, explicit validation is important given the complex methodological challenges faced.

Joint assessment of dependent discrete disease state processes

In multiple sclerosis, the primary clinical measure of disability level is an ordinal score, the expanded disability severity scale score. In relapsing-remitting multiple sclerosis, measures of relapse are additionally of interest. Multiple sclerosis patients are typically assessed with regard to both the expanded disability severity scale and relapse state at each follow-up visit. As both are discrete measures, the two can be viewed as jointly dependent Markov processes. One of the main goals of multiple sclerosis research is to accurately model, over time, both transitions between expanded disability severity scale states and change in relapse state. This objective requires a number of significant modeling decisions, including decisions about whether or not the combination of specific disease states is warranted and assessment of the dependence structure between the two disease processes. Historically, such decisions are often made in an ad hoc manner and are not formally justified. We propose novel use of Bayes factors and Bayesian variable selection in the assessment of jointly dependent Markovian processes in multiple sclerosis. Methods are assessed using both simulated data and data collected from the Partners Multiple Sclerosis Center in Boston, MA.

Clinical, MRI, and CSF markers of disability progression in multiple sclerosis

Multiple sclerosis (MS) is a chronic disorder of the central nervous system (CNS) in which the complex interplay between inflammation and neurodegeneration determines varying degrees of neurological disability. For this reason, it is very difficult to express an accurate prognosis based on purely clinical information in the individual patient at an early disease stage. Magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) biomarkers are promising sources of prognostic information with a good potential of quantitative measure, sensitivity, and reliability. However, a comprehensive MS outcome prediction model combining multiple parameters is still lacking. Current relevant literature addressing the topic of clinical, MRI, and CSF markers as predictors of MS disability progression is reviewed here.

Estimating time to disease progression comparing transition models and survival methods--an analysis of multiple sclerosis data

This article reports an analysis that aims to quantify the effect of fingolimod, an oral treatment for relapsing remitting multiple sclerosis (MS), on disability progression. The standard approach utilizes survival analysis methods, which may be problematic for MS studies that assess disability at only a few time points and include as a cardinal feature both relapses and remissions. Instead, a Markov transition model, originally developed in the framework of longitudinal data, is fit, and its special probabilistic properties are used to estimate survival curves for time to disability progression. The transition approach models the whole disability process and uses all available transition data for inference, while survival methods concentrate on a single event of interest and use only time to event data. This article compares the transition model approach to survival analysis methods, and discusses the differences in the interpretations of the estimated parameters. It applies both models to data obtained from two phase 3 clinical trials and finds that both yield positive effects for the new treatment compared to placebo, and provide similar estimates for the probability of disability progression over time. The transition model enables calculation of covariate-specific transition matrices that describe the short-term effect of treatment and other covariates on the disability process.

MRI outcomes with cladribine tablets for multiple sclerosis in the CLARITY study

We herein provide a comprehensive assessment of magnetic resonance imaging (MRI) outcomes from CLARITY, a 96-week, double-blind study demonstrating significant clinical and MRI improvements in patients with relapsing-remitting multiple sclerosis (RRMS) treated with cladribine tablets. Patients with RRMS were randomized 1:1:1 to annual short-course therapy with cladribine tablets cumulative dose 3.5 or 5.25 mg/kg or placebo. MRI endpoints included mean number of T1 gadolinium-enhancing (Gd+), active T2 and combined unique (CU) lesions/patient/scan. MRI-measured disease activity was significantly reduced in both cladribine tablets groups versus placebo. The proportion of patients with no active lesions at study end was: T1 Gd+ lesions: 86.8 and 91.0 versus 48.3 % (p < 0.001); active T2 lesions: 61.7 and 62.5 versus 28.4 % (p < 0.001); CU lesions: 59.6 and 60.7 versus 26.1 % (p < 0.001). Clinically meaningful and significant reductions in active lesion counts and increases in proportions of active lesion-free patients were achieved consistently in cladribine tablet groups when data were stratified by baseline disease characteristics. For example, the percentage of patients who remained lesion-free over the study was significantly greater in cladribine tablet groups than in the placebo group for all lesion types regardless of relapse category at baseline (p < 0.001 for all analyses of patients with ≤1 or 2 relapses; p ≤ 0.022 for analyses of patients with ≥3 relapses). MRI-measured disease activity was greatly reduced by both doses of cladribine tablets, with consistent effect across clinically relevant patient populations. These findings add to our scientific understanding of the neurological impact of this therapeutic modality in patients with RRMS.

Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci

OBJECTIVE

To perform a 1-stage meta-analysis of genome-wide association studies (GWAS) of multiple sclerosis (MS) susceptibility and to explore functional consequences of new susceptibility loci.

METHODS

We synthesized 7 MS GWAS. Each data set was imputed using HapMap phase II, and a per single nucleotide polymorphism (SNP) meta-analysis was performed across the 7 data sets. We explored RNA expression data using a quantitative trait analysis in peripheral blood mononuclear cells (PBMCs) of 228 subjects with demyelinating disease.

RESULTS

We meta-analyzed 2,529,394 unique SNPs in 5,545 cases and 12,153 controls. We identified 3 novel susceptibility alleles: rs170934(T) at 3p24.1 (odds ratio [OR], 1.17; p = 1.6 × 10(-8)) near EOMES, rs2150702(G) in the second intron of MLANA on chromosome 9p24.1 (OR, 1.16; p = 3.3 × 10(-8)), and rs6718520(A) in an intergenic region on chromosome 2p21, with THADA as the nearest flanking gene (OR, 1.17; p = 3.4 × 10(-8)). The 3 new loci do not have a strong cis effect on RNA expression in PBMCs. Ten other susceptibility loci had a suggestive p < 1 × 10(-6) , some of these loci have evidence of association in other inflammatory diseases (ie, IL12B, TAGAP, PLEK, and ZMIZ1).

INTERPRETATION

We have performed a meta-analysis of GWAS in MS that more than doubles the size of previous gene discovery efforts and highlights 3 novel MS susceptibility loci. These and additional loci with suggestive evidence of association are excellent candidates for further investigations to refine and validate their role in the genetic architecture of MS.

Cladribine tablets for relapsing-remitting multiple sclerosis: Efficacy across patient subgroups from the phase III CLARITY study

BACKGROUND

In the phase III CLARITY study, treatment with cladribine tablets at cumulative doses of 3.5 or 5.25mg/kg over 96 weeks led to significant reductions in annualized relapse rates (ARR) versus placebo in patients with relapsing-remitting multiple sclerosis. Further post hoc analyses of CLARITY study data were conducted to determine the efficacy of cladribine tablets across patient subgroups stratified by baseline characteristics.

METHODS

Relapse rates over the 96-week CLARITY study were analyzed in cohorts stratified by demographics; disease duration; treatment history and disease activity at baseline.

RESULTS

In the intent-to-treat population (n=437, 433 and 456 in the placebo, cladribine 3.5 and 5.25mg/kg groups, respectively), treatment with cladribine tablets 3.5 and 5.25mg/kg led to consistent improvements in ARR versus placebo in patients stratified by gender; age (≤40/>40 years); disease duration (<3/3-10/>10 years); prior disease-modifying drug treatment (treated/naïve); relapses in the prior year (≤1/2/≥3); Expanded Disability Status Scale score (<3.5/≥3.5); T1 gadolinium-enhancing lesions (presence, absence); and T2 lesion volume (≤median/>median) at baseline (all P≤0.05 for reduction in the relative risk of relapse [cladribine tablets versus placebo]). Significant effects were also observed in patients who had only one relapse in the year prior to study entry (n=306, 303 and 323 in the placebo, cladribine 3.5 and 5.25mg/kg groups, respectively) and who were further stratified according to other measures of disease activity at baseline.

CONCLUSIONS

Treatment with cladribine tablets provides consistent reductions in ARR compared with placebo across the spectrum of baseline demographics and disease characteristics represented in the CLARITY study.

Age at onset as a determinant of presenting phenotype and initial relapse recovery in multiple sclerosis

BACKGROUND

Age at onset modifies prognosis in multiple sclerosis (MS) and may also exert an effect on the characteristics of disease ignition. Understanding how age influences presentation informs disease management and may allow differentiation of distinct clinical sub-groups.

OBJECTIVES

To determine the nature of age-specific presentations of relapsing-remitting MS (RRMS) with respect to onset symptoms, gender ratios and index event outcomes.

METHODS

In a prospective, population-based sample of 1424 patients in South-East Wales we examined associations between age at onset, clinical features and outcome of the onset event, making specific comparisons between paediatric, adolescent and late-onset MS.

RESULTS

Age at onset varied significantly between sexes (Male 31.2, Female 29.3, p = 0.002), 0.7% had paediatric onset, 2.7% adolescent onset and 2.8% late-onset MS (>50 years). Optic neuritis was common in younger patients and declined after age 30. Lower limb motor, facial sensory, sexual and sphincteric symptoms rose with age independent of sex and disease course. F:M ratios were highest <16 years of age and declined with increasing age, with a male excess in those over 50. Probability of complete recovery from index event declined with age from 87.4% in the youngest group to 68% in the eldest (p = 0.009).

CONCLUSIONS

Age at disease onset in RRMS exerts a significant effect on gender ratios and presenting phenotype, and allows identification of specific clinical sub-groups. In addition, ability to recover from initial relapse declines with age, suggesting accumulation of disability in MS is an age-dependent response to relapse.

Computational classifiers for predicting the short-term course of Multiple sclerosis

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.

Improving power to detect disease progression in multiple sclerosis through alternative analysis strategies

In patients with multiple sclerosis, investigation of a treatment effect on disease progression in clinical trials and observational studies often uses sustained progression on the expanded disability status scale (EDSS) as an outcome. It is not clear whether this outcome is the most powerful to detect a treatment effect on clinical disease progression. Assessment of EDSS modeling choice on the detection of treatment effect was of interest. This assessment was separately conducted under three potential treatment effects: treatment reducing the chance of higher future EDSS, treatment increasing the chance of lower future EDSS, and treatment leading to both effects. To assess the effect of modeling choice, nine modeling strategies were applied to the data to determine the most powerful approach. EDSS measurements were simulated at 6 month intervals for 24 months. Each patient's initial EDSS value ranged between 0 and 3, and probabilities of transitioning from one EDSS state to another were based on the empirical probabilities of transition obtained from available clinical data. Modeling approaches based on sustained progression had less power than approaches which modeled the EDSS score directly, regardless of treatment effect. This difference was especially pronounced when the treatment effect corresponded to an increase in the probability of improvement. Sustained progression on the EDSS is a less powerful outcome measure for clinical progression than approaches based on the actual EDSS values.

A putative Alzheimer's disease risk allele in PCK1 influences brain atrophy in multiple sclerosis

Background

Brain atrophy and cognitive dysfunction are neurodegenerative features of Multiple Sclerosis (MS). We used a candidate gene approach to address whether genetic variants implicated in susceptibility to late onset Alzheimer's Disease (AD) influence brain volume and cognition in MS patients.

Methods/Principal Findings

MS subjects were genotyped for five single nucleotide polymorphisms (SNPs) associated with susceptibility to AD: PICALM, CR1, CLU, PCK1, and ZNF224. We assessed brain volume using Brain Parenchymal Fraction (BPF) measurements obtained from Magnetic Resonance Imaging (MRI) data and cognitive function using the Symbol Digit Modalities Test (SDMT). Genotypes were correlated with cross-sectional BPF and SDMT scores using linear regression after adjusting for sex, age at symptom onset, and disease duration. 722 MS patients with a mean (±SD) age at enrollment of 41 (±10) years were followed for 44 (±28) months. The AD risk-associated allele of a non-synonymous SNP in the PCK1 locus (rs8192708^G) is associated with a smaller average brain volume (P = 0.0047) at the baseline MRI, but it does not impact our baseline estimate of cognition. PCK1 is additionally associated with higher baseline T2-hyperintense lesion volume (P = 0.0088). Finally, we provide technical validation of our observation in a subset of 641 subjects that have more than one MRI study, demonstrating the same association between PCK1 and smaller average brain volume (P = 0.0089) at the last MRI visit.

Conclusion/Significance

Our study provides suggestive evidence for greater brain atrophy in MS patients bearing the PCK1 allele associated with AD-susceptibility, yielding new insights into potentially shared neurodegenerative process between MS and late onset AD.

HLA B*44: protective effects in MS susceptibility and MRI outcome measures

OBJECTIVE

In addition to the main multiple sclerosis (MS) major histocompatibility complex (MHC) risk allele (HLA DRB1*1501), investigations of the MHC have implicated several class I MHC loci (HLA A, HLA B, and HLA C) as potential independent MS susceptibility loci. Here, we evaluate the role of 3 putative protective alleles in MS: HLA A*02, HLA B*44, and HLA C*05.

METHODS

Subjects include a clinic-based patient sample with a diagnosis of either MS or a clinically isolated syndrome (n = 532), compared to subjects in a bone marrow donor registry (n = 776). All subjects have 2-digit HLA data. Logistic regression was used to determine the independence of each allele's effect. We used linear regression and an additive model to test for correlation between an allele and MRI and clinical measures of disease course.

RESULTS

After accounting for the effect of HLA DRB1*1501, both HLA A*02 and HLA B*44 are validated as susceptibility alleles (p(A*02) 0.00039 and p(B*44) 0.00092) and remain significantly associated with MS susceptibility in the presence of the other allele. Although A*02 is not associated with MS outcome measures, HLA B*44 demonstrates association with a better radiologic outcome both in terms of brain parenchymal fraction and T2 hyperintense lesion volume (p = 0.03 for each outcome).

CONCLUSION

The MHC class I alleles HLA A*02 and HLA B*44 independently reduce susceptibility to MS, but only HLA B*44 appears to influence disease course, preserving brain volume and reducing the burden of T2 hyperintense lesions in subjects with MS.

Possible clinical outcome measures for clinical trials in patients with multiple sclerosis

Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease with both clinical and pathological heterogeneity. The complexity of the MS population has offered challenges to the measurement of MS disease progression in therapeutic trials. The current standard clinical outcome measures are relapse rate, Expanded Disability Severity Scale (EDSS), and the MS Functional Composite (MSFC). These measures each have strengths and some weakness. Two additional measures, the six-minute walk and accelerometry, show promise in augmenting current measures. MS therapeutics is a quickly advancing field which requires sensitive clinical outcome measures that can detect small changes in disability that reliably reflect long-term changes in sustained disease progression in a complex population. A single clinical outcome measure of sustained disease progression may remain elusive. Rather, an integration of current and new outcome measures may be most appropriate and utilization of different measures depending on the MS population and stage of the disease may be preferred.

IL12A, MPHOSPH9/CDK2AP1 and RGS1 are novel multiple sclerosis susceptibility loci

A recent meta-analysis identified seven single-nucleotide polymorphisms (SNPs) with suggestive evidence of association with multiple sclerosis (MS). We report an analysis of these polymorphisms in a replication study that includes 8,085 cases and 7,777 controls. A meta-analysis across the replication collections and a joint analysis with the discovery data set were performed. The possible functional consequences of the validated susceptibility loci were explored using RNA expression data. For all of the tested SNPs, the effect observed in the replication phase involved the same allele and the same direction of effect observed in the discovery phase. Three loci exceeded genome-wide significance in the joint analysis: RGS1 (P value=3.55 x 10(-9)), IL12A (P=3.08 x 10(-8)) and MPHOSPH9/CDK2AP1 (P=3.96 x 10(-8)). The RGS1 risk allele is shared with celiac disease (CD), and the IL12A risk allele seems to be protective for celiac disease. Within the MPHOSPH9/CDK2AP1 locus, the risk allele correlates with diminished RNA expression of the cell cycle regulator CDK2AP1; this effect is seen in both lymphoblastic cell lines (P=1.18 x 10(-5)) and in peripheral blood mononuclear cells from subjects with MS (P=0.01). Thus, we report three new MS susceptibility loci, including a novel inflammatory disease locus that could affect autoreactive cell proliferation.

Modeling disease-state transition heterogeneity through Bayesian variable selection

In many diseases, Markov transition models are useful in describing transitions between discrete disease states. Often the probability of transitioning from one state to another varies widely across subjects. This heterogeneity is driven, in part, by a possibly unknown number of previous disease states and by potentially complex relationships between clinical data and these states. We propose use of Bayesian variable selection in Markov transition models to allow estimation of subject-specific transition probabilities. Our approach simultaneously estimates the order of the Markov process and the transition-specific covariate effects. The methods are assessed using simulation studies and applied to model disease-state transition on the expanded disability status scale (EDSS) in multiple sclerosis (MS) patients from the Partners MS Center in Boston, MA. The proposed methodology is shown to accurately identify complex covariate-transition relationships in simulations and identifies a clinically significant interaction between relapse history and EDSS history in MS patients.

Sensorimotor dysfunction in multiple sclerosis and column-specific magnetization transfer-imaging abnormalities in the spinal cord

The human spinal cord contains segregated sensory and motor pathways that have been difficult to quantify using conventional magnetic resonance imaging (MRI) techniques. Multiple sclerosis is characterized by both focal and spatially diffuse spinal cord lesions with heterogeneous pathologies that have limited attempts at linking MRI and behaviour. We used a novel magnetization-transfer-weighted imaging approach to quantify damage to spinal white matter columns and tested its association with sensorimotor impairment. We studied 42 participants with multiple sclerosis who each underwent MRI at 3 Tesla and quantitative tests of sensorimotor function. We measured cerebrospinal-fluid-normalized magnetization-transfer signals in the dorsal and lateral columns and grey matter of the cervical cord. We also measured brain lesion volume, cervical spinal cord lesion number and cross-sectional area, vibration sensation, strength, walking velocity and standing balance. We used linear regression to assess the relationship between sensorimotor impairment and MRI abnormalities. We found that the dorsal column cerebrospinal-fluid-normalized magnetization-transfer signal specifically correlated with vibration sensation (R = 0.58, P < 0.001) and the lateral column signal with strength (R = -0.45, P = 0.003). Spinal cord signal measures also correlated with walking and balance dysfunction. A stepwise multiple regression showed that the dorsal column signal and diagnosis subtype alone explained a significant portion of the variance in sensation (R(2) = 0.54, P < 0.001), whereas the lateral column signal and diagnosis subtype explained a significant portion of the variance in strength (R(2) = 0.30, P < 0.001). These results help to understand the anatomic basis of sensorimotor disability in multiple sclerosis and have implications for testing the effects of neuroprotective and reparative interventions.

Deep gray matter involvement on brain MRI scans is associated with clinical progression in multiple sclerosis

BACKGROUND

Conventional brain MRI lesion measures have unreliable associations with clinical progression in multiple sclerosis (MS). Gray matter imaging may improve clinical-MRI correlations.

METHODS

We tested if gray matter MRI measures and conventional measures of lesions/atrophy predicted clinical progression in a 4-year longitudinal study of 97 patients with MS. Baseline and follow-up brain MRI were analyzed for basal ganglia and thalamic normalized T2 signal intensity, whole brain T2-hyperintense lesion volume, and whole brain atrophy. Logistic regression tested the ability of baseline or on-study change in MRI to predict disability progression, as reported by area under the receiver operator characteristics curve (AUC).

RESULTS

Lower caudate T2-intensity at baseline (P= .04; AUC = .69) and on-study decreasing T2-intensity in the putamen (P= .03; AUC = .70) and thalamus (P= .01; AUC = .71) were the MRI variables associated with clinical progression when regression modeling was adjusted for length of follow-up interval, baseline EDSS, disease duration, age, and sex.

CONCLUSIONS

Gray matter T2-hypointensity, suggestive of excessive iron deposition is associated with worsening disability in patients with MS. Gray matter MRI assessment may be able to capture neurodegenerative aspects of the disease, with more clinical relevance than derived from conventional MRI measures. J Neuroimaging 2009;19:3-8.

Quantitative risk-benefit analysis of natalizumab

OBJECTIVE

To model the long-term risks and benefits of natalizumab in individuals with relapsing multiple sclerosis (MS).

METHODS

We created a Markov model to evaluate treatment effects on reducing relapses and slowing disease progression using published natural history data and clinical trial results. Health changes, measured in quality-adjusted life-years (QALYs), were based on patient health preferences. Patient cohorts treated with no disease-modifying treatment, natalizumab, subcutaneous interferon beta-1a, and a theoretical "perfect" MS treatment were modeled. Sensitivity analysis was used to explore model uncertainty, including varying risks of developing progressive multifocal leukoencephalopathy (PML).

RESULTS

Treatment with natalizumab resulted in 9.50 QALYs over a 20-year time horizon, a gain of 0.80 QALYs over the untreated cohort and 0.38 QALYs over interferon beta-1a. The health loss due to PML was small (-0.06 QALYs). To offset natalizumab's incremental health gain over interferon beta-1a, the risk had to increase from 1 to 7.6 PML per 1,000 patients treated over 17.9 months. The "perfect" MS treatment accumulated 10.59 QALYs over the 20-year time horizon, 1.89 QALYs above the untreated cohort. Interferon beta-1a resulted in greater QALY gains compared with natalizumab if natalizumab's relative relapse reduction was reduced from 68% to 35% or if interferon beta-1a's relative reduction was increased from 32% to 65%.

CONCLUSIONS

A more than sevenfold increase in actual risk of progressive multifocal leukoencephalopathy was required to decrease natalizumab's health gain below that of interferon beta-1a, and there remains considerable room for additional gains in health (>50%) beyond those already achieved with current therapies.

Clinical and conventional MRI predictors of disability and brain atrophy accumulation in RRMS. A large scale, short-term follow-up study

To assess the value of clinical and MRI variables in predicting short-term brain atrophy accumulation and clinical evolution in a large cohort of patients with RRMS, we studied a cohort of 548 patients, previously enrolled as a placebo arm of a 14-month, double-blind trial of oral glatiramer acetate (GA). A logistic regression model with EDSS progression as the dependent variable was built to assess baseline clinical and MRI variables associated with clinical worsening during follow-up. In 466 patients with complete central brain atrophy assessment, another linear regression model with percentage central brain volume change (PCBVC) as the dependent variable was built to assess baseline clinical and MRI variables associated with atrophy development.A total of 80 patients (15%) had EDSS progression over the follow-up period. Factors independently predicting the probability to have a clinical progression were lower EDSS (OR = 0.78, 95% CI = 0.62-0.97 p = 0.02) and higher T2 LL (OR = 1.022, 95% CI = 1.006-1.038, p = 0.007) at baseline. In the 466 patients with atrophy assessment, PCBVC declined, on average, by -2.0% (SD = 2.8) (p < 0.001) over the follow-up. The multivariate PCBVC analysis revealed that the PCBVC decrease was independently correlated with higher EDSS (p = 0.03) and T2 LL (p = 0.005) at baseline. The squared correlation coefficients of the composite scores made up of EDSS and T2 LL considered together were able to explain only 3 % of the variance in disability progression and only 4 % of the variance of PCBVC.In RRMS patients, clinical and conventional MRI findings at baseline only modestly predict shortterm accumulation of brain atrophy and disability. These data confirm the need to develop clinical and MRI measures more sensitive towards the more disabling aspects of the disease.

Simultaneous Confidence Intervals Based on the Percentile Bootstrap Approach

This note concerns the construction of bootstrap simultaneous confidence intervals (SCI) for m parameters. Given B bootstrap samples, we suggest an algorithm with complexity of O(mB log(B)). We apply our algorithm to construct a confidence region for time dependent probabilities of progression in multiple sclerosis and for coefficients in a logistic regression analysis. Alternative normal based simultaneous confidence intervals are presented and compared to the bootstrap intervals.

MRI in multiple sclerosis: what's inside the toolbox?

Magnetic resonance imaging (MRI) has played a central role in the diagnosis and management of multiple sclerosis (MS). In addition, MRI metrics have become key supportive outcome measures to explore drug efficacy in clinical trials. Conventional MRI measures have contributed to the understanding of MS pathophysiology at the macroscopic level yet have failed to provide a complete picture of underlying MS pathology. They also show relatively weak relationships to clinical status such as predictive strength for clinical progression. Advanced quantitative MRI measures such as magnetization transfer, spectroscopy, diffusion imaging, and relaxometry techniques are somewhat more specific and sensitive for underlying pathology. These measures are particularly useful in revealing diffuse damage in cerebral white and gray matter and therefore may help resolve the dissociation between clinical and conventional MRI findings. In this article, we provide an overview of the array of tools available with brain and spinal cord MRI technology as it is applied to MS. We review the most recent data regarding the role of conventional and advanced MRI techniques in the assessment of MS. We focus on the most relevant pathologic and clinical correlation studies relevant to these measures.