Evaluation of cervical spinal cord atrophy using a modified SIENA approach

Spinal cord (SC) atrophy obtained from structural magnetic resonance imaging has gained relevance as an indicator of neurodegeneration in various neurological disorders. The common method to assess SC atrophy is by comparing numerical differences of the cross-sectional spinal cord area (CSA) between time points. However, this indirect approach leads to considerable variability in the obtained results. Studies showed that this limitation can be overcome by using a registration-based technique. The present study introduces the Structural Image Evaluation using Normalization of Atrophy on the Spinal Cord (SIENA-SC), which is an adapted version of the original SIENA method, designed to directly calculate the percentage of SC volume change over time from clinical brain MRI acquired with an extended field of view to cover the superior part of the cervical SC. In this work, we compared SIENA-SC with the Generalized Boundary Shift Integral (GBSI) and the CSA change. On a scan-rescan dataset, SIENA-SC was shown to have the lowest measurement error than the other two methods. When comparing a group of 190 Healthy Controls with a group of 65 Multiple Sclerosis patients, SIENA-SC provided significantly higher yearly rates of atrophy in patients than in controls and a lower sample size when measured for treatment effect sizes of 50%, 30% and 10%. Our findings indicate that SIENA-SC is a robust, reproducible, and sensitive approach for assessing longitudinal changes in spinal cord volume, providing neuroscientists with an accessible and automated tool able to reduce the need for manual intervention and minimize variability in measurements.

Magnetic resonance imaging prognostic factors for survival and relapse in dogs with meningoencephalitis of unknown origin

Introduction

Canine meningoencephalitis of unknown origin (MUO) is a debilitating disease associated with high mortality. The prognostic value of magnetic resonance imaging (MRI) findings for predicting survival at 12 months and long-term relapse remains uncertain.

Methods

This was a retrospective cohort study evaluating the prognostic value of different MRI variables using multivariable logistic regression and Cox proportional hazards analysis.

Results

In total, 138 dogs were presumptively diagnosed with MUO. The most common location for lesions identified on MRI were the white matter tracts of the corona radiata and corpus callosum, followed by the frontal, sensorimotor and temporal cortices. Lower T2 lesion load (p = 0.006, OR = 0.942, CI = 0.902-0.983) was associated with longer survival and higher T1 post-contrast lesion load (p = 0.023, OR = 1.162, CI = 1.021-1.322) was associated with relapse.

Discussion

This study has identified prognostic factors that may help identify dogs at higher risk of death and relapse and therefore guide treatment recommendations.

Is it cost-effective to request IgM oligoclonal bands against lipids in daily practice as a biomarker for poor prognosis in multiple sclerosis?

BACKGROUND

various prognostic factors of multiple sclerosis have been identified, including demographic, clinical, radiological, and laboratory factors. The aim was to analyze whether the presence of IgM oligoclonal bands against lipids is associated with disease progression.

METHODS

an individual-based, prospective, observational study was conducted at the Neurology Department of Hospital Universitari i Politècnic la Fe. Clinical, radiological, and laboratory variables were collected. Data analysis was divided into a descriptive phase and a subsequent analytical phase.

RESULTS

a total of 116 patients were included. 81.9% of them had IgM oligoclonal bands against lipids, with phosphatidylcholine being the predominant type. A higher proportion of patients with IgM oligoclonal bands against lipids required treatment with a disease-modifying drug, started treatment at an earlier stage, showed poorer results in functional tests, and exhibited a higher increase in lesion burden, although these differences were not statistically significant.

CONCLUSIONS

In our study, the presence of IgM oligoclonal bands against lipids was not found to be associated with other poor prognostic variables.

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.

Early prognosticators of later TSPO-PET-measurable microglial activation in multiple sclerosis

BACKGROUND

Factors driving increased innate immune cell activation in multiple sclerosis (MS) brain are not well understood. As higher prevalence of microglial/macrophage activation in association with chronic lesions and diffusely in the normal appearing white matter predict more rapid accumulation of clinical disability, it is of high importance to understand processes behind this. Objective of the study was to explore demographic, clinical and paraclinical variables associating with later positron emission tomography (PET)-measurable innate immune cell activation.

METHODS

PET-imaging using a TSPO-binding [¹¹C]PK11195 was performed to evaluate microglial activation in patients with relapsing-remitting MS aged 40-55 years with a minimum disease duration of five years (n = 37). Medical records and diagnostic MR images were reviewed for relevant early MS disease-related clinical and paraclinical parameters.

RESULTS

More prominent microglial activation was associated with higher number of T2 lesions in the diagnostic MRI, a higher immunoglobulin G (IgG) index in the diagnostic CSF and Expanded Disability Status Scale (EDSS) ≥ 2.0 five years after diagnosis.

CONCLUSION

The number of T2 lesions in MRI, and CSF immunoglobulin content measured by IgG index at the time of MS diagnosis associated with later TSPO-PET-measurable innate immune cell activation. This suggests that both focal and diffuse early inflammatory phenomena impact the development of later progression-related pathology.

Improving the detection of new lesions in multiple sclerosis with a cascaded 3D fully convolutional neural network approach

Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new lesions on brain MRI scans is considered a robust predictive biomarker for the disease progression. New lesions are a high-impact prognostic factor to predict evolution to MS or risk of disability accumulation over time. However, the detection of this disease activity is performed visually by comparing the follow-up and baseline scans. Due to the presence of small lesions, misregistration, and high inter-/intra-observer variability, this detection of new lesions is prone to errors. In this direction, one of the last Medical Image Computing and Computer Assisted Intervention (MICCAI) challenges was dealing with this automatic new lesion quantification. The MSSEG-2: MS new lesions segmentation challenge offers an evaluation framework for this new lesion segmentation task with a large database (100 patients, each with two-time points) compiled from the OFSEP (Observatoire français de la sclérose en plaques) cohort, the French MS registry, including 3D T2-w fluid-attenuated inversion recovery (T2-FLAIR) images from different centers and scanners. Apart from a change in centers, MRI scanners, and acquisition protocols, there are more challenges that hinder the automated detection process of new lesions such as the need for large annotated datasets, which may be not easily available, or the fact that new lesions are small areas producing a class imbalance problem that could bias trained models toward the non-lesion class. In this article, we present a novel automated method for new lesion detection of MS patient images. Our approach is based on a cascade of two 3D patch-wise fully convolutional neural networks (FCNNs). The first FCNN is trained to be more sensitive revealing possible candidate new lesion voxels, while the second FCNN is trained to reduce the number of misclassified voxels coming from the first network. 3D T2-FLAIR images from the two-time points were pre-processed and linearly co-registered. Afterward, a fully CNN, where its inputs were only the baseline and follow-up images, was trained to detect new MS lesions. Our approach obtained a mean segmentation dice similarity coefficient of 0.42 with a detection F1-score of 0.5. Compared to the challenge participants, we obtained one of the highest precision scores (PPVL = 0.52), the best PPVL rate (0.53), and a lesion detection sensitivity (SensL of 0.53).

Using personalized prognosis in the treatment of relapsing multiple sclerosis: A practical guide

The clinical course of multiple sclerosis (MS) is highly variable among patients, thus creating important challenges for the neurologist to appropriately treat and monitor patient progress. Despite some patients having apparently similar symptom severity at MS disease onset, their prognoses may differ greatly. To this end, we believe that a proactive disposition on the part of the neurologist to identify prognostic “red flags” early in the disease course can lead to much better long-term outcomes for the patient in terms of reduced disability and improved quality of life. Here, we present a prognosis tool in the form of a checklist of clinical, imaging and biomarker parameters which, based on consensus in the literature and on our own clinical experiences, we have established to be associated with poorer or improved clinical outcomes. The neurologist is encouraged to use this tool to identify the presence or absence of specific variables in individual patients at disease onset and thereby implement sufficiently effective treatment strategies that appropriately address the likely prognosis for each patient.

Reproducibility of Lesion Count in Various Subregions on MRI Scans in Multiple Sclerosis

Purpose

Lesion number and burden can predict the long-term outcome of multiple sclerosis, while the localization of the lesions is also a good predictive marker of disease progression. These biomarkers are used in studies and in clinical practice, but the reproducibility of lesion count is not well-known.

Methods

In total, five raters evaluated T2 hyperintense lesions in 140 patients with multiple sclerosis in six localizations: periventricular, juxtacortical, deep white matter, infratentorial, spinal cord, and optic nerve. Black holes on T1-weighted images and brain atrophy were subjectively measured on a binary scale. Reproducibility was measured using the intraclass correlation coefficient (ICC). ICCs were also calculated for the four most accurate raters to see how one outlier can influence the results.

Results

Overall, moderate reproducibility (ICC 0.5-0.75) was shown, which did not improve considerably when the most divergent rater was excluded. The areas that produced the worst results were the optic nerve region (ICC: 0.118) and atrophy judgment (ICC: 0.364). Comparing high- and low-lesion burdens in each region revealed that the ICC is higher when the lesion count is in the mid-range. In the periventricular and deep white matter area, where lesions are common, higher ICC was found in patients who had a lower lesion count. On the other hand, juxtacortical lesions and black holes that are less common showed higher ICC when the subjects had more lesions. This difference was significant in the juxtacortical region when the most accurate raters compared patients with low (ICC: 0.406 CI: 0.273-0.546) and high (0.702 CI: 0.603-0.785) lesion loads.

Conclusion

Lesion classification showed high variability by location and overall moderate reproducibility. The excellent range was not achieved, owing to the fact that some areas showed poor performance. Hence, putting effort toward the development of artificial intelligence for the evaluation of lesion burden should be considered.

The impairment of the functional system and fatigue at the onset of the disease predict reaching disability milestones in relapsing-remitting multiple sclerosis differently in female and male patients

Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system with variable types of disability progression (DP). Previous studies, defining different disability milestones (DMs), have reported symptoms at MS onset to be the predictors of DP and sex as a risk factor. Meanwhile, accounting for sex differences in MS, predictors in female and male patients might differ. To investigate whether the symptoms at MS onset predict reaching DMs in patients with relapsing-remitting (RR) MS and whether the predictors vary between different DMs and female and male patients. Data from 128 RR MS patients (84 females, 44 males) was retrospectively studied. EDSS scores 4 and 6 (associated with impaired ambulation) were taken as DMs. Association between symptoms at MS onset and time to reach DMs was assessed with Cox multiple regression model. Pyramidal symptoms and fatigue at MS onset predicted the progression to EDSS 4 in the whole study population (HR 1.84, 95% CI 1.07-3.2, p = 0.028 and HR 2.01, 95% CI 1.12-3.4, p = 0.011, correspondingly). The same symptoms predicted reaching DM in female, but not male patients. Bowel/bladder symptoms predicted reaching EDSS 6 in the whole study population (HR 4.31, 95% CI 1.47-12.6, p = 0.008) and female patients only (HR 3.93, 95% CI 1.04-14.8, p = 0.043). In female patients, fatigue was also the predictor of reaching EDSS 6 (HR 3.54, 95% CI 1.16-10.8, p = 0.026). Impairment of functional symptoms at MS onset can predict reaching DMs in patients with RR-MS, but the predictors for EDSS 4 and EDSS 6 differ in female and male patients.

Prognostic utility of the IgM oligoclonal bands against myelin lipids in multiple sclerosis

IgM oligoclonal bands (OCMBs) against myelin-specific lipids have been identified as a marker for poor prognosis in multiple sclerosis (MS). The aim is to examine the relation between lipid-specific OCMBs (LS-OCMBs) and the evolution of MS. An analytical, ambispective and individual-based study was conducted. We selected 116 patients, out of whom 95 had LS-OCMBs. The predominant lipid recognized was phosphatidylcholine. The positive gangliosides OCMB group reached better scores in the 9HPT, and the phosphatidylcholine, sphingolipids and phosphatidylethanolamine OCMB groups showed statistical differences in the magnetic resonance parameters. In conclusion: some LS-OCMBs showed statistically significant differences with functional or imaging tests.

Prediction in treatment outcomes in multiple sclerosis: challenges and recent advances

INTRODUCTION

Multiple Sclerosis (MS) is a chronic autoimmune and neurodegenerative disease of the central nervous system with a course dependent on early treatment response. Increasing evidence also suggests that despite eliminating disease activity (relapses and lesions), many patients continue to accrue disability, highlighting the need for a more comprehensive definition of treatment success. Optimizing disability outcome measures, as well as continuously improving our understanding of neuroinflammatory and neurodegenerative biomarkers is required.

AREAS COVERED

This review describes the challenges inherent in classifying and monitoring disease phenotype in MS. The review also provides an assessment of clinical, radiological, and blood biomarker tools for current and future practice.

EXPERT OPINION

Emerging MRI techniques and standardized patient outcome assessments will increase the accuracy of initial diagnosis and understanding of disease progression.

The clinical and paraclinical correlates of employment status in multiple sclerosis

PURPOSE

To identify the clinical and paraclinical markers of employment status in multiple sclerosis (MS).

METHODS

This was a cross-sectional sub-study investigating 1226 MS patients. To minimalized confounding effect, two groups of patients, matched by sex, age, and education, were selected: 307 patients with full time employment and 153 unemployed patients receiving disability pension. We explored associations between employment status and Expanded Disability Status Scale (EDSS), 25 Foot Walk Test (25FWT), Nine Hole Peg Test (9HPT), Brief International Cognitive Assessment for MS (BICAMS), Paced Auditory Serial Addition Test (PASAT), Beck Depression Inventory (BDI), SLOAN charts (SLOAN), and brain volumetric MRI measures.

RESULTS

Both groups differed significantly on all variables of interest (p < 0.001). In the univariate analyses, EDSS, SDMT (Symbol Digit Modalities Test) adjusted for BDI, 25FWT, and 9HPT best explained variability in vocational status. In multivariate analyses, the combination of EDSS, 25FWT, SDMT, BDI, and corpus callosum fraction (CCF) explained the greatest variability. As a next step, after patients were matched by EDSS, differences in SDMT, 25FWT (both p < 0.001), 9HPT, CCF, and T2 lesion volume were still present (all p < 0.005) between both groups. The best multivariate model consisted of SDMT, BDI, and T2 lesion volume.

CONCLUSIONS

EDSS, walking ability, cognitive performance, and MRI volumetric parameters are independently associated with employment status.

Quantifying disease pathology and predicting disease progression in multiple sclerosis with only clinical routine T2-FLAIR MRI

•

We explored five brain pathology measures from clinical-quality T2-FLAIR MRI in MS.

•

These included LVV, thalamus volume, MOV, SCLV and network efficiency.

•

T2-FLAIR measures predicted a majority of the variance in research-quality MRI.

•

T2-FLAIR measures correlated with neurologic disability and cognitive function.

•

T2-FLAIR measures predicted disability progression over five-years.

•

T2-FLAIR measures can be used in legacy clinical datasets.

Background

Although quantitative measures from research-quality MRI provide a means to study multiple sclerosis (MS) pathology in vivo, these metrics are often unavailable in legacy clinical datasets.

Objective

To determine how well an automatically-generated quantitative snapshot of brain pathology, measured only on clinical routine T2-FLAIR MRI, can substitute for more conventional measures on research MRI in terms of capturing multi-factorial disease pathology and providing similar clinical relevance.

Methods

MRI with both research-quality sequences and conventional clinical T2-FLAIR was acquired for 172 MS patients at baseline, and neurologic disability was assessed at baseline and five-years later. Five measures (thalamus volume, lateral ventricle volume, medulla oblongata volume, lesion volume, and network efficiency) for quantifying disparate aspects of neuropathology from low-resolution T2-FLAIR were applied to predict standard research-quality MRI measures. They were compared in regard to association with future neurologic disability and disease progression over five years.

Results

The combination of the five T2-FLAIR measures explained most of the variance in standard research-quality MRI. T2-FLAIR measures were associated with neurologic disability and cognitive function five-years later (R² = 0.279, p < 0.001; R² = 0.382, p < 0.001), similar to standard research-quality MRI (R² = 0.279, p < 0.001; R² = 0.366, p < 0.001). They also similarly predicted disability progression over five years (%-correctly-classified = 69.8, p = 0.034), compared to standard research-quality MRI (%-correctly-classified = 72.4%, p = 0.022) in relapsing-remitting MS.

Conclusion

A set of five T2-FLAIR-only measures can substitute for standard research-quality MRI, especially in relapsing-remitting MS. When only clinical T2-FLAIR is available, it can be used to obtain substantially more quantitative information about brain pathology and disability than is currently standard practice.

Cortical fractal dimension predicts disability worsening in Multiple Sclerosis patients

BACKGROUND

Fractal geometry measures the morphology of the brain and detects CNS damage. We aimed to assess the longitudinal changes on brain's fractal geometry and its predictive value for disease worsening in patients with Multiple Sclerosis (MS).

METHODS

We prospectively analyzed 146 consecutive patients with relapsing-remitting MS with up to 5 years of clinical and brain MRI (3 T) assessments. The fractal dimension and lacunarity were calculated for brain regions using box-counting methods. Longitudinal changes were analyzed in mixed-effect models and the risk of disability accumulation were assessed using Cox Proportional Hazard regression analysis.

RESULTS

There was a significant decrease in the fractal dimension and increases of lacunarity in different brain regions over the 5-year follow-up. Lower cortical fractal dimension increased the risk of disability accumulation for the Expanded Disability Status Scale [HR 0.9734, CI 0.8420-0.9125; Harrell C 0.59; Wald p 0.038], 9-hole peg test [HR 0.9734, CI 0.8420-0.9125; Harrell C 0.59; Wald p 0.0083], 2.5% low contrast vision [HR 0.4311, CI 0.2035-0.9133; Harrell C 0.58; Wald p 0.0403], symbol digit modality test [HR 2.215, CI 1.043-4.705; Harrell C 0.65; Wald p 0.0384] and MS Functional Composite-4 [HR 0.55, CI 0.317-0.955; Harrell C 0.59; Wald p 0.0029].

CONCLUSIONS

Fractal geometry analysis of brain MRI identified patients at risk of increasing their disability in the next five years.

Factors influencing daily treatment choices in multiple sclerosis: practice guidelines, biomarkers and burden of disease

At two meetings of a Central European board of multiple sclerosis (MS) experts in 2018 and 2019 factors influencing daily treatment choices in MS, especially practice guidelines, biomarkers and burden of disease, were discussed. The heterogeneity of MS and the complexity of the available treatment options call for informed treatment choices. However, evidence from clinical trials is generally lacking, particularly regarding sequencing, switches and escalation of drugs. Also, there is a need to identify patients who require highly efficacious treatment from the onset of their disease to prevent deterioration. The recently published European Committee for the Treatment and Research in Multiple Sclerosis/European Academy of Neurology clinical practice guidelines on pharmacological management of MS cover aspects such as treatment efficacy, response criteria, strategies to address suboptimal response and safety concerns and are based on expert consensus statements. However, the recommendations constitute an excellent framework that should be adapted to local regulations, MS center capacities and infrastructure. Further, available and emerging biomarkers for treatment guidance were discussed. Magnetic resonance imaging parameters are deemed most reliable at present, even though complex assessment including clinical evaluation and laboratory parameters besides imaging is necessary in clinical routine. Neurofilament-light chain levels appear to represent the current most promising non-imaging biomarker. Other immunological data, including issues of immunosenescence, will play an increasingly important role for future treatment algorithms. Cognitive impairment has been recognized as a major contribution to MS disease burden. Regular evaluation of cognitive function is recommended in MS patients, although no specific disease-modifying treatment has been defined to date. Finally, systematic documentation of real-life data is recognized as a great opportunity to tackle unresolved daily routine challenges, such as use of sequential therapies, but requires joint efforts across clinics, governments and pharmaceutical companies.

Magnetic resonance spectroscopy of the normal appearing grey matter in the posterior cingulate gyrus in the prognosis and monitoring of disease activity in MS patients treated with interferon-β in a 3-year follow-up

Several predictors of non-response to interferon-β (IFN-β) treatment have been proposed. The aim of the study was to identify metabolite changes in the normal-appearing cortex of the posterior cingulate gyrus (PCG) using MRS (magnetic resonance spectroscopy) and to investigate their usefulness in prognosis of NEDA (no evidence of disease activity) in the 3-year follow-up and in monitoring treatment effects during IFN-β therapy in the parallel period of time in multiple sclerosis (MS) patients. Forty-one relapsing-remitting MS patients and 41 sex- and age-matched healthy subjects underwent routine MRI protocol with MRS sequence with the use of a 1.5 T magnet. A single voxel size of 2x2x2cm was inserted in the cortex of PCG region. Associations between baseline metabolic ratios, conventional MRI findings, demographic and clinical factors, and NEDA status were evaluated using logistic, Cox, and multinomial logistic regression models. MS patients in the initial scan showed a statistically significant decline in NAA/Cr ratio (p < 0.0001) and an increase in Cho/Cr ratio (p = 0.016) compared to the control group. None of the MRS parameters predicted NEDA maintenance or the time to loss of NEDA. In treatment monitoring only an improvement in the combination of NAA/Cr + Cho/Cr ratio between the 1st and 2nd year of treatment was connected with a 6.27-fold chance (p = 0.025) of having simultaneous NEDA maintenance. To conclude, metabolite alterations in the PCG region did not predict NEDA maintenance, but they seem to be useful in treatment monitoring.

Assessment of Racial/Ethnic Disparities in Volumetric MRI Correlates of Clinical Disability in Multiple Sclerosis: A Preliminary Study

BACKGROUND AND PURPOSE

Although global and regional brain volume has been established as a relevant measure to define and predict multiple sclerosis (MS) severity, characterization of specific trends by race/ethnicity is currently lacking. We aim to (1) characterize racial disparities in disability-specific patterns of brain MRI volumetric measures between Hispanic and Caucasian individuals with MS and (2) explore the relevance of these measures as predictors of clinical disability progression.

METHODS

Brain MRI scans from 94 Hispanic and 94 age- and gender-matched Caucasian MS patients were analyzed using automatic and manual segmentation techniques. Select global and regional volume measures were correlated to Expanded Disability Status Scale (EDSS) scores at baseline and subsequent follow-up visits.

RESULTS

Hispanic patients had a higher baseline median EDSS score (interquartile range [IQR], 2.0; [1.0-3.5]) compared to Caucasians (median [IQR], 1.0 [.0-2.0]) and an increased risk of requiring ambulatory assistance (hazard ratio [HR], 9.7; 95% confidence interval [CI], 2.8-32.5). Normalized thalamic volume was moderately associated with EDSS scores (r_s   = -.42,  P < .001 in Hispanics; r_s   = -.32, P  = .002 in Caucasians) and was the best predictor of sustained disability worsening in both racial groups in a time-to-event analysis.

CONCLUSIONS

The confounding impact of race on quantitative brain volume measures may affect the interpretation of outcome measures in MS clinical trials.

The role of ADC values within the normal-appearing brain in the prognosis of multiple sclerosis activity during interferon-β therapy in the 3-year follow-up: a preliminary report

Predictors of multiple sclerosis (MS) activity during disease-modifying treatment are being extensively investigated. The aim of this study was to assess the prognosis of NEDA (no evidence of disease activity) status during IFN-β (interferon-β) treatment, using apparent diffusion coefficient (ADC) measurements obtained at initial MRI (magnetic resonance imaging). In 87 MS patients treated with IFN-β, ADC values were calculated for 13 regions of normal-appearing white and grey matter (NAWM, NAGM) based on MRI performed with a 1.5 T magnet before (MS0, n = 45) or after one year of therapy (MS1, n = 42). Associations were evaluated between ADC, conventional MRI findings, demographic and clinical factors and NEDA status within the following 3 years using logistic, Cox and multinomial logistic regression models. NEDA rates in the MS0 group were 64.4%, 46.5% and 33.3% after the 1st, 2nd and 3rd year of treatment, respectively and in MS1 patients 71.4% and 48.7% for the periods 1st–2nd and 1st–3rd years of treatment, respectively. ADC values in the NAWM regions contributed to loss of NEDA and its clinical and radiological components, with a 1–3% increase in the risk of NEDA loss (p = 0.0001–0.0489) in both groups. ADC measurements may have an additional prognostic value with regard to NEDA status.

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.

Early clinical markers of aggressive multiple sclerosis

Patients with the ‘aggressive’ form of multiple sclerosis accrue disability at an accelerated rate, typically reaching Expanded Disability Status Score (EDSS) ≥ 6 within 10 years of symptom onset. Several clinicodemographic factors have been associated with aggressive multiple sclerosis, but less research has focused on clinical markers that are present in the first year of disease. The development of early predictive models of aggressive multiple sclerosis is essential to optimize treatment in this multiple sclerosis subtype. We evaluated whether patients who will develop aggressive multiple sclerosis can be identified based on early clinical markers. We then replicated this analysis in an independent cohort. Patient data were obtained from the MSBase observational study. Inclusion criteria were (i) first recorded disability score (EDSS) within 12 months of symptom onset; (ii) at least two recorded EDSS scores; and (iii) at least 10 years of observation time, based on time of last recorded EDSS score. Patients were classified as having ‘aggressive multiple sclerosis’ if all of the following criteria were met: (i) EDSS ≥ 6 reached within 10 years of symptom onset; (ii) EDSS ≥ 6 confirmed and sustained over ≥6 months; and (iii) EDSS ≥ 6 sustained until the end of follow-up. Clinical predictors included patient variables (sex, age at onset, baseline EDSS, disease duration at first visit) and recorded relapses in the first 12 months since disease onset (count, pyramidal signs, bowel-bladder symptoms, cerebellar signs, incomplete relapse recovery, steroid administration, hospitalization). Predictors were evaluated using Bayesian model averaging. Independent validation was performed using data from the Swedish Multiple Sclerosis Registry. Of the 2403 patients identified, 145 were classified as having aggressive multiple sclerosis (6%). Bayesian model averaging identified three statistical predictors: age &gt; 35 at symptom onset, EDSS ≥ 3 in the first year, and the presence of pyramidal signs in the first year. This model significantly predicted aggressive multiple sclerosis [area under the curve (AUC) = 0.80, 95% confidence intervals (CIs): 0.75, 0.84, positive predictive value = 0.15, negative predictive value = 0.98]. The presence of all three signs was strongly predictive, with 32% of such patients meeting aggressive disease criteria. The absence of all three signs was associated with a 1.4% risk. Of the 556 eligible patients in the Swedish Multiple Sclerosis Registry cohort, 34 (6%) met criteria for aggressive multiple sclerosis. The combination of all three signs was also predictive in this cohort (AUC = 0.75, 95% CIs: 0.66, 0.84, positive predictive value = 0.15, negative predictive value = 0.97). Taken together, these findings suggest that older age at symptom onset, greater disability during the first year, and pyramidal signs in the first year are early indicators of aggressive multiple sclerosis.

Brain MRI Predicts Worsening Multiple Sclerosis Disability over 5 Years in the SUMMIT Study

BACKGROUND AND PURPOSE

Brain MRI-derived lesions and atrophy are related to multiple sclerosis (MS) disability. In the Serially Unified Multicenter MS Investigation (SUMMIT), from Brigham and Women's Hospital (BWH) and University of California, San Francisco (UCSF), we assessed whether MRI methodologic heterogeneity may limit the ability to pool multisite data sets to assess 5-year clinical-MRI associations.

METHODS

Patients with relapsing-remitting (RR) MS (n = 100 from each site) underwent baseline brain MRI and baseline and 5-year clinical evaluations. Patients were matched on sex (74 women each), age, disease duration, and Expanded Disability Status Scale (EDSS) score. MRI was performed with differences between sites in both acquisition (field strength, voxel size, pulse sequences), and postprocessing pipeline to assess brain parenchymal fraction (BPF) and T2 lesion volume (T2LV).

RESULTS

The UCSF cohort showed higher correlation than the BWH cohort between T2LV and disease duration. UCSF showed a higher inverse correlation between BPF and age than BWH. UCSF showed a higher inverse correlation than BWH between BPF and 5-year EDSS score. Both cohorts showed inverse correlations between BPF and T2LV, with no between-site difference. The pooled but not individual cohort data showed a link between a lower baseline BPF and the subsequent 5-year worsening in disability in addition to other stronger relationships in the data.

CONCLUSIONS

MRI acquisition and processing differences may result in some degree of heterogeneity in assessing brain lesion and atrophy measures in patients with MS. Pooling of data across sites is beneficial to correct for potential biases in individual data sets.

Practical Priors for Bayesian Inference of Latent Biomarkers

Latent biomarkers are quantities that strongly relate to patient's disease diagnosis and prognosis, but are difficult to measure or even not directly observable. The objective of this study was to develop, analyze and validate new priors for Bayesian inference of such biomarkers. Theoretical analysis revealed a relationship between the estimates inferred from the model and the true values of measured quantities, and the impact of the priors. This led to a new prior encoding scheme that incorporates objectively measurable domain knowledge, i.e. by performing two measurements with a reference method, which imply scale of the prior distribution. Second, priors on parameters of systematic error are non-informative, which enables biomarker estimation from a set of different quantities. Analysis showed that the volume of nucleus basalis of Meynert, which is reduced in early stages of Alzheimer's dementia and Parkinson's disease, is inter-related and could be inferred from compartmental brain volume measurements performed on routine clinical MR scans. Another experiment showed that total lesion load, associated to future disability progression in multiple sclerosis patients, could be inferred from lesion volume measurements based on multiple automated MR scan segmentations. Besides, figures of merit derived from the estimates could, without comparing against reference gold standard segmentations, identify the best performing lesion segmentation method. The proposed new priors substantially simplify the application of Bayesian inference for latent biomarkers and thus open an avenue for clinical implementation of new biomarkers, which may ultimately advance the evidence-based medicine.

Does CSF pleocytosis have a predictive value for disease course in MS?

Objective

MS is a demyelinating CNS disorder with a spectrum of clinical patterns regarding course and prognosis. Although several prognostic factors are considered in the initial evaluation of patients, biological markers defining the disease course and guiding treatments are currently lacking. It is unknown whether patients with CSF pleocytosis differ in regard to symptoms, disease course, and prognosis from those without. The aim of this study was to evaluate whether CSF pleocytosis during the initial presentation has an impact on the clinical course and progression of MS.

Methods

We retrospectively evaluated patients attending the MS Clinic at Rabin Medical Center between January 1999 and January 2016 who underwent lumbar puncture (LP) at disease presentation, considering CSF cell count, clinical diagnosis (clinically isolated syndrome [CIS] and relapsing-remitting MS [RRMS]), annualized relapse rate (ARR), paraclinical findings (imaging, CSF oligoclonal bands, and evoked potentials), and disease progression, expressed by the Expanded Disability Status Scale (EDSS).

Results

One hundred fourteen patients (72 females) underwent LP at disease presentation (RRMS: n = 100, CIS: n = 14). Age at diagnosis was 32.4 ± 12.2 years, and the follow-up time was 9.4 ± 3.8 years. Forty-six patients showed a pleocytic CSF (≥5 cells per μL). Compared with patients with <4 cells per μL, patients with pleocytosis had a higher ARR (0.60 ± 0.09 vs 0.48 ± 0.04; p = 0.0267) and a steeper increase (slope) in the EDSS score throughout the follow-up period (correlation coefficient: r2 = 0.04; p = 0.0251).

Conclusions

CSF pleocytosis may be considered a biological unfavorable predictive factor regarding disease course and progression in MS.

A Serial 10-Year Follow-Up Study of Atrophied Brain Lesion Volume and Disability Progression in Patients with Relapsing-Remitting MS

BACKGROUND AND PURPOSE

Disappearance of T2 lesions into CSF spaces is frequently observed in patients with MS. Our aim was to investigate temporal changes of cumulative atrophied brain T2 lesion volume and 10-year confirmed disability progression.

MATERIALS AND METHODS

We studied 176 patients with relapsing-remitting MS who underwent MR imaging at baseline, 6 months, and then yearly for 10 years. Occurrence of new/enlarging T2 lesions, changes in T2 lesion volume, and whole-brain, cortical and ventricle volumes were assessed yearly between baseline and 10 years. Atrophied T2 lesion volume was calculated by combining baseline lesion masks with follow-up CSF partial volume maps. Ten-year confirmed disability progression was confirmed after 48 weeks. ANCOVA detected MR imaging outcome differences in stable (n = 76) and confirmed disability progression (n = 100) groups at different time points; hierarchic regression determined the unique additive variance explained by atrophied T2 lesion volume regarding the association with confirmed disability progression, in addition to other MR imaging metrics. Cox regression investigated the association of early MR imaging outcome changes and time to development of confirmed disability progression.

RESULTS

The separation of stable-versus-confirmed disability progression groups became significant even in the first 6 months for atrophied T2 lesion volume (140% difference, Cohen d = 0.54, P = .004) and remained significant across all time points (P ≤ .007). The hierarchic model, including all other MR imaging outcomes during 10 years predicting confirmed disability progression, improved significantly after adding atrophied T2 lesion volume (R ² = 0.27, R ² change 0.11, P = .009). In Cox regression, atrophied T2 lesion volume in 0-6 months (hazard ratio = 4.23, P = .04) and 0-12 months (hazard ratio = 2.41, P = .022) was the only significant MR imaging predictor of time to confirmed disability progression.

CONCLUSIONS

Atrophied T2 lesion volume is a robust and early marker of disability progression in relapsing-remitting MS.

Relation between functional connectivity and disability in multiple sclerosis: a non-linear model

OBJECTIVE

To characterize the relation between brain functional connectivity and disability in patients with multiple sclerosis; to investigate the existence of critical values of both disability and functional connectivity corresponding to exhaustion of functional adaptive mechanisms.

METHODS

Hundred-and-nineteen patients with no-to-severe disability and 42 healthy subjects were studied via 3T resting state functional MRI. Out of 116 regions extracted from Automated Anatomical Labeling atlas, pairs of regions whose functional connectivity correlated with Expanded Disability Status Score were identified. In patients, mathematical modeling was applied to find the best models describing Expanded-Disability-Status-Score vs structural or functional measures. Functional vs structural models intersecting points were identified.

RESULTS

Disability had direct linear relation with lesion load (r = 0.40, p < 5E-6), inverse of thalamic volume (r = 0.31 p < 1E-3) and functional connectivity in bi-frontal pairs of regions (r > 0.40, p < 0.04), while being non-linearly associated with functional connectivity in cerebello-temporal and cerebello-frontal pairs of regions (F > 1.73, p < 0.02). Structural vs functional models intersecting points corresponded to Expanded Disability Status Score of 3.0. 85% of patients scoring more than 3.0 showed functional connectivity in cerebello-temporal and cerebello-frontal pairs of regions below confidence intervals (z = [2.28-2.88] 95% CI) measured in healthy subjects.

CONCLUSIONS

Functional brain connectivity changes may represent mechanisms of adaptation to structural damage and inflammation and may be not always clinically beneficial. Functional connectivity decreases in comparison with structural measure at Expanded Disability Status Score greater than 3.0, which may be critical and indicate exhaustion of compensatory mechanisms.

Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year employment status in multiple sclerosis patients

BACKGROUND

Multiple sclerosis (MS) is frequently diagnosed in the most productive years of adulthood and is often associated with worsening employment status. However, reliable predictors of employment status change are lacking.

OBJECTIVE

To identify early clinical and brain magnetic resonance imaging (MRI) markers of employment status worsening in MS patients at 12-year follow-up.

METHODS

A total of 145 patients with early relapsing-remitting MS from the original Avonex-Steroids-Azathioprine (ASA) study were included in this prospective, longitudinal, observational cohort study. Cox models were conducted to identify MRI and clinical predictors (at baseline and during the first 12 months) of worsening employment status (patients either (1) working full-time or part-time with no limitations due to MS and retaining this status during the course of the study, or (2) patients working full-time or part-time with no limitations due to MS and switching to being unemployed or working part-time due to MS).

RESULTS

In univariate analysis, brain parenchymal fraction, T1 and T2 lesion volume were the best MRI predictors of worsening employment status over the 12-year follow-up period. MS duration at baseline (hazard ratio (HR) = 1.10, 95% confidence interval (CI) 1.03-1.18; p = 0.040) was the only significant clinical predictor. Having one extra milliliter of T1 lesion volume was associated with a 53% greater risk of worsening employment status (HR = 1.53, 95% CI 1.16-2.02; p = 0.018). A brain parenchymal fraction decrease of 1% increased the risk of worsening employment status by 22% (HR = 0.78, 95% CI 0.65-0.95; p = 0.034).

CONCLUSION

Brain atrophy and lesion load were significant predictors of worsening employment status in MS patients. Using a combination of clinical and MRI markers may improve the early prediction of an employment status change over long-term follow-up.

The Role of High-Frequency MRI Monitoring in the Detection of Brain Atrophy in Multiple Sclerosis

BACKGROUND AND PURPOSE

A relatively high intraindividual variability of longitudinal magnetic resonance imaging (MRI) of brain volume loss (BVL) measurements over time renders challenging its application to individual multiple sclerosis (MS) patients. Objective of this study was to investigate if high-frequency brain MRI monitoring affects identification of pathological BVL in an individual patient.

METHODS

One hundred fifty-seven relapsing-remitting MS patients had seven MRI scans over 12 months follow-up. All 1,585 MRI scans were performed on the same 1.5T scanner using an identical scanning protocol. Volumetric analysis was performed by ScanView and SIENA software. Linear regression analysis was used for estimation of annualized BVL, with a cutoff greater than .4% defined as pathological. We compared proportions of patients with pathological BVL obtained by analysis of different number of MRI time-points.

RESULTS

An analysis of seven MRI scans (months 0, 2, 4, 6, 8, 10, and 12) showed pathological BVL in 105 (65%) of patients. When three MRI scans were included (months 0, 6, and 12), we found 10 (6.4%) false negative and 9 (5.7%) false positive results compared with the analysis of seven MRI scans, used as a reference for assessment of pathological BVL. Analysis of two MRI time-points (months 0 and 12) showed 10 (6.4%) false negative and 13 (8.3%) false positive results compared with analysis of seven MRI time-points. Change in the accuracy of pathological BVL between results obtained by analysis of seven and two time-points was 14.7%.

CONCLUSIONS

High-frequency MRI monitoring may have a considerable effect on improving the precision of precisely identifying pathological BVL in individual patients. However, limitations in translation to clinical practice remain.

Cognitive clinico-radiological paradox in early stages of multiple sclerosis

Objective

To investigate whether the strength of the association between magnetic resonance imaging (MRI) metrics and cognitive outcomes differs between various multiple sclerosis subpopulations.

Methods

A total of 1052 patients were included in this large cross‐sectional study. Brain MRI (T1 and T2 lesion volume and brain parenchymal fraction) and neuropsychological assessment (Brief International Cognitive Assessment for Multiple Sclerosis and Paced Auditory Serial Addition Test) were performed.

Results

Weak correlations between cognitive domains and MRI measures were observed in younger patients (age≤30 years; absolute Spearman's rho = 0.05–0.21), with short disease duration (<2 years; rho = 0.01–0.21), low Expanded Disability Status Scale [EDSS] (≤1.5; rho = 0.08–0.18), low T2 lesion volume (lowest quartile; <0.59 mL; rho = 0.01–0.20), and high brain parenchymal fraction (highest quartile; >86.66; rho = 0.01–0.16). Stronger correlations between cognitive domains and MRI measures were observed in older patients (age>50 years; rho = 0.24–0.50), with longer disease duration (>15 years; rho = 0.26–0.53), higher EDSS (≥5.0; rho = 0.23–0.39), greater T2 lesion volume (highest quartile; >5.33 mL; rho = 0.16–0.32), and lower brain parenchymal fraction (lowest quartile; <83.71; rho = 0.13–0.46). The majority of these observed results were confirmed by significant interactions (P ≤ 0.01) using continuous variables.

Interpretation

The association between structural brain damage and functional cognitive impairment is substantially weaker in multiple sclerosis patients with a low disease burden. Therefore, disease stage should be taken into consideration when interpreting associations between structural and cognitive measures in clinical trials, research studies, and clinical practice.

Pathological cut-offs of global and regional brain volume loss in multiple sclerosis

BACKGROUND

Volumetric MRI surrogate markers of disease progression are lacking.

OBJECTIVE

To establish cut-off values of brain volume loss able to discriminate between healthy controls and MS patients.

METHODS

In total, 386 patients after first demyelinating event suggestive of MS (CIS), 964 relapsing-remitting MS (RRMS) patients, 63 secondary-progressive MS (SPMS) patients and 58 healthy controls were included in this longitudinal study. A total of 11,438 MRI scans performed on the same MRI scanner with the same protocol were analysed. Annualised percentage changes of whole brain, grey matter, thalamus and corpus callosum volumes were estimated. We investigated cut-offs able to discriminate between healthy controls and MS patients.

RESULTS

At a predefined specificity of 90%, the annualised percentage change cut-off of corpus callosum volume (-0.57%) was able to distinguish between healthy controls and patients with the highest sensitivity (51% in CIS, 48% in RRMS and 42% in SPMS patients). Lower sensitivities (22%-49%) were found for cut-offs of whole brain, grey matter and thalamic volume loss. Among CIS and RRMS patients, cut-offs were associated with greater accumulation of disability.

CONCLUSION

We identified cut-offs of annualised global and regional brain volume loss rates able to discriminate between healthy controls and MS patients.

CD133-Positive Membrane Particles in Cerebrospinal Fluid of Patients with Inflammatory and Degenerative Neurological Diseases

Background: Analysis of cerebrospinal fluid (CSF) is a frequently used diagnostic tool in a variety of neurological diseases. Recent studies suggested that investigating membrane particles enriched with the stem cell marker CD133 may offer new avenues for studying neurological disease. In this study, we evaluated the amount of membrane particle-associated CD133 in human CSF in neuroinflammatory and degenerative diseases.

Methods: We compared the amount of membrane particle-associated CD133 in CSF samples collected from 45 patients with normal pressure hydrocephalus, parkinsonism, dementia, and cognitive impairment, chronic inflammatory diseases and 10 healthy adult individuals as controls. After ultracentrifugation of CSF, gel electrophoresis and immunoblotting using anti-CD133 monoclonal antibody 80B258 were performed. Antigen-antibody complexes were detected using chemiluminescence.

Results: The amount of membrane particle-associated CD133 was significantly increased in patients with normal pressure hydrocephalus (p < 0.001), parkinsonism (p = 0.011) as well as in patients with chronic inflammatory disease (p = 0.008). Analysis of CSF of patients with dementia and cognitive impairment revealed no significant change compared with healthy individuals. Furthermore, subgroup analysis of patients with chronic inflammatory diseases demonstrated significantly elevated levels in individuals with relapsing-remitting multiple sclerosis (p = 0.023) and secondary progressive multiple sclerosis (SPMS; p = 0.010).

Conclusion: Collectively, our study revealed elevated levels of membrane particle-associated CD133 in patients with normal pressure hydrocephalus, parkinsonism as well as relapsing-remitting and SPMS. Membrane glycoprotein CD133 may be of clinical value for several neurological diseases.

Disability progression markers over 6-12 years in interferon-β-treated multiple sclerosis patients

OBJECTIVE

To investigate the association between activity during interferon-beta (IFNβ) therapy and disability outcomes in patients with relapsing-remitting multiple sclerosis (RRMS).

METHODS

A longitudinal study based on two previously described cohorts of IFNβ-treated RRMS patients was conducted. Patients were classified according to clinical activity after 2 years (clinical cohort) or to clinical and radiological activity after 1 year (magnetic resonance imaging (MRI) cohort). Multivariate Cox models were calculated for early disease activity predicting long-term disability.

RESULTS

A total of 516 patients from two different cohorts were included in the analyses. Persistent clinical disease activity during the first 2 years of therapy predicted severe long-term disability (clinical cohort). In the MRI cohort, modified Rio score and no or minimal evidence of disease activity (NEDA/MEDA) did not identify patients with risk of Expanded Disability Status Scale (EDSS) worsening. However, a Rio score ≥ 2 (hazard ratio (HR): 3.3, 95% confidence interval (CI): 1.7-6.4); ≥3 new T2 lesions (HR: 2.9, 95% CI: 1.5-5.6); or ≥2 Gd-enhancing lesions (HR: 2.1, 95% CI: 1.1-4) were able to identify patients with EDSS worsening.

CONCLUSION

Although early activity during IFNβ therapy is associated with poor long-term outcomes, minimal degree of activity does not seem to be predictive of EDSS worsening over 6.7-year mean follow-up.

Identification of multiple sclerosis patients at highest risk of cognitive impairment using an integrated brain magnetic resonance imaging assessment approach

BACKGROUND AND PURPOSE

While impaired cognitive performance is common in multiple sclerosis (MS), it has been largely underdiagnosed. Here a magnetic resonance imaging (MRI) screening algorithm is proposed to identify patients at highest risk of cognitive impairment. The objective was to examine whether assessment of lesion burden together with whole brain atrophy on MRI improves our ability to identify cognitively impaired MS patients.

METHODS

Of the 1253 patients enrolled in the study, 1052 patients with all cognitive, volumetric MRI and clinical data available were included in the analysis. Brain MRI and neuropsychological assessment with the Brief International Cognitive Assessment for Multiple Sclerosis were performed. Multivariable logistic regression and individual prediction analysis were used to investigate the associations between MRI markers and cognitive impairment. The results of the primary analysis were validated at two subsequent time points (months 12 and 24).

RESULTS

The prevalence of cognitive impairment was greater in patients with low brain parenchymal fraction (BPF) (<0.85) and high T2 lesion volume (T2-LV) (>3.5 ml) than in patients with high BPF (>0.85) and low T2-LV (<3.5 ml), with an odds ratio (OR) of 6.5 (95% CI 4.4-9.5). Low BPF together with high T2-LV identified in 270 (25.7%) patients predicted cognitive impairment with 83% specificity, 82% negative predictive value, 51% sensitivity and 75% overall accuracy. The risk of confirmed cognitive decline over the follow-up was greater in patients with high T2-LV (OR 2.1; 95% CI 1.1-3.8) and low BPF (OR 2.6; 95% CI 1.4-4.7).

CONCLUSIONS

The integrated MRI assessment of lesion burden and brain atrophy may improve the stratification of MS patients who may benefit from cognitive assessment.

The Efficacy of Natalizumab versus Fingolimod for Patients with Relapsing-Remitting Multiple Sclerosis: A Systematic Review, Indirect Evidence from Randomized Placebo-Controlled Trials and Meta-Analysis of Observational Head-to-Head Trials

Background

Although Fingolimod (FGD) and Natalizumab (NTZ) appear to be effective in relapsing-remitting multiple sclerosis (RRMS), they have never been directly compared in a randomized clinical trial (RCT).

Methods and Findings

We evaluated the comparative efficacy of FGD vs. NTZ using a meta-analytical approach. Data from placebo-controlled RCTs was used for indirect comparisons and observational data was utilized for head-to-head comparisons. We identified 3 RCTs (2498 patients) and 5 observational studies (2576 patients). NTZ was associated with a greater reduction in the 2-year annualized relapse rate (ARR; SMD_indirect = -0.24;95% CI: from -0.44 to -0.04; p = 0.005) and with the probability of no disease activity at 2 years (OR_indirect:1.82, 95% CI: from 1.05 to 3.15) compared to FGD, while no differences between the two therapies were found in the proportion of patients who remained relapse-free (OR_indirect = 1.20;95% CI: from 0.84 to 1.71) and those with disability progression (OR_indirect = 0.76;95% CI: from 0.48 to 1.21) at 2 years. In the analysis of observational data, we found no significant differences between NTZ and FGD in the 2-year ARR (SMD = -0.05; 95% CI: from -0.26 to 0.16), and 2-year disability progression (OR:1.08;95% CI: from 0.77 to 1.52). However, NTZ-treated patients were more likely to remain relapse-free at 2-years compared to FGD (OR: 2.19;95% CI: from 1.15 to 4.18; p = z0.020).

Conclusions

Indirect analyses of RCT data and head-to-head comparisons of observational findings indicate that NTZ may be more effective than FGD in terms of disease activity reduction in patients with RRMS. However, head-to-head RCTs are required to independently confirm this preliminary observation.