Predicting secondary progression in relapsing-remitting multiple sclerosis: a Bayesian analysis

Predicting disease severity in multiple sclerosis using multimodal data and machine learning

BACKGROUND

Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.

METHODS

We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre.

RESULTS

We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts.

CONCLUSION

Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.

Predictive factors and treatment challenges in malignant progression of relapsing-remitting multiple sclerosis

Objective

Our objective was to uncover the predictive factors that can help anticipate the malignant progression of individuals with Relapsing-Remitting Multiple Sclerosis (RRMS). Additionally, we sought to analyze and compare the response to treatment between patients with benign and malignant forms of RRMS.

Methods

This cohort study included RRMS patients categorized as benign (≥10 years since disease onset, Expanded Disability Status Scale (EDSS) ≤ 1) or malignant (≤5 years since disease onset, EDSS ≥6). Patients' data, including demographics, medical history, treatment, and MRI (Magnetic Resonance Imaging) scans, were collected and statistically analyzed.

Results

Among the 254 patients diagnosed with RRMS, 174 were found to have benign RRMS, while the remaining 80 were diagnosed with malignant RRMS. Notably, patients with malignant RRMS exhibited a significantly higher mean age of onset (32.00 ± 7.96 vs. 25.70 ± 17.19; P < 0.001) and a greater prevalence of males (40% vs. 18.4%; P = 0.014). Additionally, within the initial five years of diagnosis, patients with malignant RRMS experienced a higher number of relapses (median: 4 vs. 2; P < 0.001) and hospitalizations (median: 2 vs. 1; P = 0.006) compared to those with benign RRMS. Clinical presentations of malignant RRMS were predominantly characterized by multifocal attacks, whereas unifocal attacks were more prevalent in patients with benign RRMS. MRI scans revealed that malignant RRMS patients displayed a higher burden of plaques in the infratentorial and cord regions, as well as a greater number of black hole lesions. Conversely, benign RRMS patients exhibited a higher number of Gadolinium-enhanced lesions. Utilizing Disease-Modifying Therapies (DMTs) with an escalating approach has shown effectiveness in managing benign RRMS. However, it has proven insufficient in addressing malignant RRMS, resulting in frequent transitions to higher-line DMTs. As a result, it places a considerable burden on patients with malignant RRMS, consuming valuable time and resources, and ultimately yielding subpar outcomes.

Conclusion

Our study identifies prognostic factors for malignant progression in RRMS, including older age of onset, male gender, increased relapses and hospitalizations, multifocal attacks, higher plaque load, and black hole lesions. The current escalation strategy for DMTs is insufficient for managing malignant RRMS, requiring alternative approaches for improved outcomes. In other words, MS is a spectrum rather than a single disease, and some patients progress to a malignant phenotype of MS that is not effectively treated by the current approach.

Should autologous hematopoietic stem cell transplantation be offered as a first-line disease modifying therapy to patients with multiple sclerosis?

In multiple sclerosis (MS), progression independent of new focal inflammation may commence shortly after disease onset, and it is increasingly revealed that the risk of disability accrual is reduced by early use of high-efficacy disease-modifying therapies (HE-DMTs). People with aggressive MS may therefore benefit from early treatment with autologous haematopoietic stem cell transplantation (AHSCT), a procedure inducing maximal immunosuppression followed by immune reconstitution, demonstrated to be superior to DMTs in one randomized clinical trial. However, in current practice prior failure to HE-DMTs is typically required to establish the indication for AHSCT. In the present article, the available evidence on the potential role of AHSCT as first-line treatment in aggressive MS and the rationale for its early use will be summarized. Proposed definitions of aggressive MS that could help identifying MS patients eligible for early treatment with AHSCT will also be discussed.

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.

Improving the decision to switch from first- to second-line therapy in multiple sclerosis: A dynamic scoring system

BACKGROUND

In relapsing-remitting multiple sclerosis (RRMS), early identification of suboptimal responders can prevent disability progression.

OBJECTIVE

We aimed to develop and validate a dynamic score to guide the early decision to switch from first- to second-line therapy.

METHODS

Using time-dependent propensity scores (PS) from a French cohort of 12,823 patients with RRMS, we constructed one training and two validation PS-matched cohorts to compare the switched patients to second-line treatment and the maintained patients. We used a frailty Cox model for predicting individual hazard ratios (iHRs).

RESULTS

From the validation PS-matched cohort of 348 independent patients with iHR ⩽ 0.69, we reported the 5-year relapse-free survival at 0.14 (95% confidence interval (CI) 0.09-0.22) for the waiting group and 0.40 (95% CI 0.32-0.51) for the switched group. From the validation PS-matched cohort of 518 independent patients with iHR > 0.69, these values were 0.37 (95% CI 0.30-0.46) and 0.44 (95% CI 0.37-0.52), respectively.

CONCLUSIONS

By using the proposed dynamic score, we estimated that at least one-third of patients could benefit from an earlier switch to prevent relapse.

Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review

(1) Background: Multiple sclerosis (MS) is an immune system disease in which myelin in the nervous system is affected. This abnormal immune system mechanism causes physical disabilities and cognitive impairment. Functional magnetic resonance imaging (fMRI) is a common neuroimaging technique used in studying MS. Computational methods have recently been applied for disease detection, notably graph theory, which helps researchers understand the entire brain network and functional connectivity. (2) Methods: Relevant databases were searched to identify articles published since 2000 that applied graph theory to study functional brain connectivity in patients with MS based on fMRI. (3) Results: A total of 24 articles were included in the review. In recent years, the application of graph theory in the MS field received increased attention from computational scientists. The graph-theoretical approach was frequently combined with fMRI in studies of functional brain connectivity in MS. Lower EDSSs of MS stage were the criteria for most of the studies (4) Conclusions: This review provides insights into the role of graph theory as a computational method for studying functional brain connectivity in MS. Graph theory is useful in the detection and prediction of MS and can play a significant role in identifying cognitive impairment associated with MS.

Towards a validated definition of the clinical transition to secondary progressive multiple sclerosis: A study from the Italian MS Register

BACKGROUND

Definitions for reliable identification of transition from relapsing-remitting multiple sclerosis (MS) to secondary progressive (SP)MS in clinical cohorts are not available.

OBJECTIVES

To compare diagnostic performances of two different data-driven SPMS definitions.

METHODS

Data-driven SPMS definitions based on a version of Lorscheider's algorithm (DDA) and on the EXPAND trial inclusion criteria were compared, using the neurologist's definition (ND) as gold standard, in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Akaike information criterion (AIC) and area under the curve (AUC).

RESULTS

A cohort of 10,240 MS patients with ⩾5 years of follow-up was extracted from the Italian MS Registry; 880 (8.5%) patients were classified as SPMS according to the neurologist definition, 1806 (17.6%) applying the DDA and 1134 (11.0%) with the EXPAND definition. The DDA showed greater discrimination power (AUC: 0.8 vs 0.6) and a higher sensitivity (77.1% vs 38.0%) than the EXPAND definition, with similar specificity (88.0% vs 91.5%). PPV and NPV were higher using the DDA than considering EXPAND definition (37.5% vs 29.5%; 97.6% vs 94.0%).

CONCLUSION

Data-driven definitions demonstrated greater ability to capture SP transition than neurologist's definition and the global accuracy of DDA seems to be higher than the EXPAND definition.

Treatment response scoring systems to assess long-term prognosis in self-injectable DMTs relapsing-remitting multiple sclerosis patients

BACKGROUND AND OBJECTIVES

Different treatment response scoring systems in treated MS patients exist. The objective was to assess the long-term predictive value of these systems in RRMS patients treated with self-injectable DMTs.

METHODS

RRMS-treated patients underwent brain MRI before the onset of therapy and 12 months thereafter, and neurological assessments every 6 months. Clinical and demographic characteristics were collected at baseline. After the first year of treatment, several scoring systems [Rio score (RS), modified Rio score (MRS), MAGNIMS score (MS), and ROAD score (RoS)] were calculated. Cox-Regression and survival analyses were performed to identify scores predicting long-term disability.

RESULTS

We included 319 RRMS patients. Survival analyses showed that patients with RS > 1 and RoS > 3 had a significant risk of reaching an EDSS of 4.0 and 6.0 The score with the best sensitivity (61%) was the RoS, while the MRS showed the best specificity (88%). The RS showed the best positive predictive value (42%) and the best accuracy (81%).

CONCLUSIONS

The combined measures integrated into different scores have an acceptable prognostic value for identifying patients with long-term disability. Thus, these data reinforce the concept of early treatment optimization to minimize the risk of long-term disability.

Early predictors of conversion to secondary progressive multiple sclerosis

BACKGROUND

We conducted this study to estimated the time of conversion from relapsing-remitting MS (RRMS) to SPMS and its early predictor factors.

METHODS

In this retrospective study, demographic, clinical, and imaging data from MS patients at diagnosis were extracted. Cox proportional hazards model was used to assess the association between various baseline characteristics and conversion to SPMS. We also assessed the association brtween escalation and early intensive therapy approaches with transition to progressive phase.

RESULTS

Out of 1903 patients with RRMS at baseline, 293 (15.4%) patients progressed to SPMS during follow-up. The estimated number of patients converted to SPMS was 10% at 10-years, 50% at 20-years, and 93% at 30-years. On multivariate Cox regression analysis older age at onset (HR: 1.067, 95%CI: 1.048-1.085, p < 0.001), smoking (HR: 2.120, 95%CI: 1.203-3.736, p = 0.009), higher EDSS at onset (HR: 1.199, 95%CI: 1.109-1.295, p < 0.001), motor dysfunction (HR: 2.470, 95%CI: 1.605-3.800, p < 0.001), cerebellar dysfunction (HR: 3.096, 95%CI: 1.840-5.211, p < 0.001), and presence of lesions in spinal cord (HR: 0.573, 95%CI: 0.297-0.989, p = 0.042) increased the risk of conversion from RRMS to SPMS. No significant difference between escalation and EIT groups in the risk of transition to progressive phase (weighted HR = 1.438; 95% CI: 0.963, 2.147; p = 0.076) was found.

CONCLUSION

Our data support previous observations that smoking is a modifiable risk factor for secondary progressive MS and confirms that spinal cord involvement, age, and more severe disease at onset are prognostic factors for converting to secondary progressive MS.

Determinants of disability development in patients with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is one of the most common chronic neurological diseases affecting the central nervous system in young adults.

OBJECTIVE

To investigate demographic and clinical factors that are effective in the development of irreversible disability from the onset of MS, and to identify factors that affect the transformation from the relapse-remitting MS (RRMS) phase to the progressive MS (PMS) phase.

METHODS

Retrospective study on 741 patients who were diagnosed with RRMS and PMS according to the McDonald criteria, and were enrolled into the Turkish MS database of the Department of Neurology MS Polyclinic, at the Faculty of Medicine, Karadeniz Technical University, in Trabzon, Turkey. Kaplan-Meier analysis was used to evaluate the time taken to reach EDSS 4 and EDSS 6 from the onset of disease, and the time taken between EDSS 4 and EDSS 6.

RESULTS

Age of onset >40 years; having polysymptomatic-type onset, pyramidal or bladder-intestinal system-related first episode; ≥7 episodes in the first 5 years; and <2 years between the first two episodes were found to be effective for MS patients to reach EDSS 4 and EDSS 6. The demographic and clinical parameters that were effective for progression from EDSS 4 to EDSS 6 were: pyramidal or bladder-intestinal system-related first episode; 4‒6 episodes in the first 5 years; >2 years until start of first treatment; and smoking.

CONCLUSIONS

Our findings reveal important characteristics of MS patients in our region. However, the associations between these parameters and MS pathophysiology remain to be elucidated.

Scoring the 10-year risk of ambulatory disability in multiple sclerosis: the RoAD score

BACKGROUND AND PURPOSE

Both baseline prognostic factors and short-term predictors of treatment response can influence the long-term risk of disability accumulation in patients with relapsing-remitting multiple sclerosis (RRMS). The objective was to develop and validate a scoring system combining baseline prognostic factors and 1-year variables of treatment response into a single numeric score predicting the long-term risk of disability.

METHODS

We analysed two independent datasets of patients with RRMS who started interferon beta or glatiramer acetate, had an Expanded Disability Status Scale (EDSS) score <4.0 at treatment start and were followed for at least 10 years. The first dataset ('training set') included patients attending three MS centres in Italy and served as a framework to create the so-called RoAD score (Risk of Ambulatory Disability). The second ('validation set') included a cohort of patients followed in Barcelona, Spain, to explore the performance of the RoAD score in predicting the risk of reaching an EDSS score ≥6.0.

RESULTS

The RoAD score (ranging from 0 to 8) derived from the training set (n = 1225), was based on demographic (age), clinical baseline prognostic factors (disease duration, EDSS) and 1-year predictors of treatment response (number of relapses, presence of gadolinium enhancement and new T2 lesions). The best cut-off score for discriminating patients at higher risk of reaching the disability milestone was ≥4. When applied to the validation set (n = 296), patients with a RoAD score ≥4 had an approximately 4-fold increased risk for reaching the disability milestone (p < 0.001).

DISCUSSION

The RoAD score is proposed as an useful tool to predict individual prognosis and optimize treatment strategy of patients with RRMS.

Proportion of alemtuzumab-treated patients converting from relapsing-remitting multiple sclerosis to secondary progressive multiple sclerosis over 6 years

Background

Few data exist concerning conversion to secondary progressive MS in patients treated with disease-modifying therapies.

Objective

Determine the proportion of alemtuzumab-treated patients converting from relapsing-remitting to secondary progressive MS during the CARE-MS core and extension studies.

Methods

Patients (N = 811) were analyzed post hoc for secondary progressive MS conversion. Optimal conversion definition: Expanded Disability Status Scale (EDSS) score ≥4, pyramidal functional system score ≥2, and confirmed progression over ≥3 months including confirmation within the functional system leading to progression, independent of relapse.

Results

Over 6.2 years median follow-up, 20 alemtuzumab-treated patients converted (Kaplan-Meier estimate, 2.7%; 95% confidence interval, 1.8%-4.2%). Sensitivity analysis accounting for dropouts showed similar results (3%), as did analyses using alternative definitions with different EDSS thresholds and/or confirmation periods, and analysis of core study subcutaneous interferon beta-1a-treated patients who received alemtuzumab in the extension. Patients converting to secondary progressive MS were older, and had higher EDSS scores and greater brain lesion volumes at baseline, but did not need additional alemtuzumab or other therapies.

Conclusions

The 6-year conversion rate to secondary progressive MS was low for alemtuzumab-treated patients, supporting further study of the role alemtuzumab may play in reducing risk of secondary progression.ClinicalTrials.gov identifiers: NCT00530348, NCT00548405, NCT00930553.

Transition to secondary progression in relapsing-onset multiple sclerosis: Definitions and risk factors

BACKGROUND

No uniform criteria for a sensitive identification of the transition from relapsing-remitting multiple sclerosis (MS) to secondary-progressive multiple sclerosis (SPMS) are available.

OBJECTIVE

To compare risk factors of SPMS using two definitions: one based on the neurologist judgment (ND) and an objective data-driven algorithm (DDA).

METHODS

Relapsing-onset MS patients (n = 19,318) were extracted from the Italian MS Registry. Risk factors for SPMS and for reaching irreversible Expanded Disability Status Scale (EDSS) 6.0, after SP transition, were estimated using multivariable Cox regression models.

RESULTS

SPMS identified by the DDA (n = 2343, 12.1%) were older, more disabled and with a faster progression to severe disability (p < 0.0001), than those identified by the ND (n = 3868, 20.0%). In both groups, the most consistent risk factors (p < 0.05) for SPMS were a multifocal onset, an age at onset >40 years, higher baseline EDSS score and a higher number of relapses; the most consistent protective factor was the disease-modifying therapy (DMT) exposure. DMT exposure during SP did not impact the risk of reaching irreversible EDSS 6.0.

CONCLUSION

A DDA definition of SPMS identifies more aggressive progressive patients. DMT exposure reduces the risk of SPMS conversion, but it does not prevent the disability accumulation after the SP transition.

Clinical Parameters to Predict Future Clinical Disease Activity After Treatment Change to Higher-Dose Subcutaneous Interferon Beta-1a From Other Platform Injectables in Patients With Relapsing-Remitting Multiple Sclerosis

Objective: To identify predictors of clinical disease activity after treatment change to higher-dose interferon beta-1a in relapsing-remitting multiple sclerosis (MS).

Methods: This was a retrospective-prospective observational multicenter study. We enrolled patients with at least one relapse on platform injectable therapy who were changed to 44 μg interferon beta-1a. Our primary endpoint was the clinical disease activity-free (cDAF) status at 6, 12, 18, and 24 months. Secondary endponts included relapse-free status and disability progression-free status at different timepoints. The primary predictor of interest was the monosymptomatic vs. polysymptomatic index relapse, based on the number of affected functional systems from the Kurtzke scale during the last relapse prior to baseline. Other secondary predictors of clinical disease activity were analyzed based on different demographic and relapse characteristics. Kaplan-Meier estimates of the cumulative probability of remaining in cDAF status were performed. The time to clinical disease activity was compared between groups using univariate Kaplan-Meier analysis and multivariate Cox regression. Multivariate analyses were processed in the form of CART (Classification & Regression Trees).

Results: A total of 300 patients entered the study; 233 (77.7%) of them completed the 24-month study period and 67 patients (22.3%) terminated early. The proportion of patients in cDAF status was 84.7, 69.5, 57.5, and 54.2% at 6, 12, 18, and 24 months. After 2 years of follow-up, 55.9% of patients remained relapse-free and 87.8% of patients remained disability progression-free. At all timepoints, the polysymptomatic index relapse was the most significant predictor of clinical disease activity of all studied variables. Hazard ratio of cDAF status for patients with monosymptomatic vs. polysymptomatic index relapse was 1.94 (95% CI 1.38–2.73). CART analyses also confirmed the polysymptomatic index relapse being the strongest predictor of clinical disease activity, followed by higher number of pre-baseline relapses with the most significant effect in the monosymptomatic index relapse group. The next strongest predictors of clinical disease activity were cerebellar syndrome as the most disabled Kurtzke functional system for the monosymptomatic relapse group, and age at first MS symptom ≥ 45 for the polysymptomatic relapse group.

Conclusions: Patients with a polysymptomatic index relapse and/or higher number of relapses within 2 years prior to baseline are at high risk of clinical disease activity, despite treatment change to higher-dose interferon beta-1a from other platform injectable therapy.

Trial registration: State Institute of Drug Control (SUKL), URL: http://www.sukl.eu/modules/nps/index.php?h=study&a=detail&id=958&lang=2, registration number 1205090000.

Aggressive multiple sclerosis (1): Towards a definition of the phenotype

While the major phenotypes of multiple sclerosis (MS) and relapsing-remitting, primary and secondary progressive MS have been well characterized, a subgroup of patients with an active, aggressive disease course and rapid disability accumulation remains difficult to define and there is no consensus about their management and treatment. The current lack of an accepted definition and treatment guidelines for aggressive MS triggered a 2018 focused workshop of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) on aggressive MS. The aim of the workshop was to discuss approaches on how to describe and define the disease phenotype and its treatments. Unfortunately, it was not possible to come to consensus on a definition because of unavailable data correlating severe disease with imaging and molecular biomarkers. However, the workshop highlighted the need for future research needed to define this disease subtype while also focusing on its treatment and management. Here, we review previous attempts to define aggressive MS and present characteristics that might, with additional research, eventually help characterize it. A companion paper summarizes data regarding treatment and management.

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.

Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

OBJECTIVE

For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter.

METHODS

106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler.

RESULTS

The follow-up diameters were predicted satisfactorily (i.e. the true follow-up diameter was in the 95% prediction interval) for 79% of the scans using the population-based growth model, and 83% of the scans using the patient-specific growth model. Among the evaluated geometric measurements, centerline tortuosity was a significant (p = 0.0002) predictor of growth for AAAs with accelerated and stable expansion rates. Using the enhanced prediction model, 86% of follow-up scans were predicted satisfactorily. The average prediction errors of population-based, patient-specific, and enhanced models were ±2.67, ±2.61 and ± 2.79 mm, respectively.

CONCLUSION

A computational framework using patient-oriented growth models provides useful tools for per-patient basis treatment and enables better prediction of AAA growth.

Risk of secondary progressive multiple sclerosis: A longitudinal study

Background:

The risk factors for conversion from relapsing-remitting to secondary progressive multiple sclerosis remain highly contested.

Objective:

The aim of this study was to determine the demographic, clinical and paraclinical features that influence the risk of conversion to secondary progressive multiple sclerosis.

Methods:

Patients with adult-onset relapsing–remitting multiple sclerosis and at least four recorded disability scores were selected from MSBase, a global observational cohort. The risk of conversion to objectively defined secondary progressive multiple sclerosis was evaluated at multiple time points per patient using multivariable marginal Cox regression models. Sensitivity analyses were performed.

Results:

A total of 15,717 patients were included in the primary analysis. Older age (hazard ratio (HR) = 1.02, p < 0.001), longer disease duration (HR = 1.01, p = 0.038), a higher Expanded Disability Status Scale score (HR = 1.30, p < 0.001), more rapid disability trajectory (HR = 2.82, p < 0.001) and greater number of relapses in the previous year (HR = 1.07, p = 0.010) were independently associated with an increased risk of secondary progressive multiple sclerosis. Improving disability (HR = 0.62, p = 0.039) and disease-modifying therapy exposure (HR = 0.71, p = 0.007) were associated with a lower risk. Recent cerebral magnetic resonance imaging activity, evidence of spinal cord lesions and oligoclonal bands in the cerebrospinal fluid were not associated with the risk of conversion.

Conclusion:

Risk of secondary progressive multiple sclerosis increases with age, duration of illness and worsening disability and decreases with improving disability. Therapy may delay the onset of secondary progression.

Association between suicide and multiple sclerosis: An updated meta-analysis

BACKGROUND

Whether multiple sclerosis is associated with a higher rate of suicide remains controversial. Therefore, we aimed to evaluate the risk of suicide in multiple sclerosis patients based on meta-analysis of previously published data.

METHODS

We searched for studies that measured the suicide risk in multiple sclerosis patients compared with general population that were published up to 1 December 2018 in PubMed, EMBASE, and Web of Science databases. Sixteen studies fulfilled the eligibility criteria. We performed random-effects meta-analyses to calculate suicide rate ratio (SRR) and 95% confidence intervals (CIs) for patients with multiple sclerosis.

RESULTS

The association between suicide and multiple sclerosis was statistically significant with a pooled SRR 1.72 (95%CI 1.48-1.99, I-squared = 55.0%). Risk of suicide at diagnosis of multiple sclerosis (SRR 2.12, 95% CI 1.84-2.46; I-squared = 4.4%) was higher than the risk of suicide at symptom onset (SRR 1.69; 95% CI 1.43-2.00; I-squared = 0.0%). Gender may exert an influence on the impact of sex on the association between MS multiple sclerosis and suicide, but this requires is controversial and need more further studies to demonstrate.

CONCLUSION

Our meta-analysis shows a significant association between suicide and multiple sclerosis, although ethnic and geographical differences were not considered. These findings should be confirmed and extended in future large studies.

Highly Aggressive Multiple Sclerosis

PURPOSE OF REVIEW

Newly introduced disease-modifying therapies offer greater efficacy than previous therapies but also have serious side effects. This article reviews factors useful in identifying those at risk of developing aggressive relapsing multiple sclerosis (MS) and therapies available for treatment.

RECENT FINDINGS

Several factors predict aggressive MS, including demographic factors, relapses, symptom characteristics, MRI activity, and other biomarkers. These can be used to select patients for more aggressive therapies, including natalizumab, alemtuzumab, fingolimod, and ocrelizumab. Additional off-label treatments are available for patients with severe disease. The benefits and side effects of these treatments must be considered when making therapeutic decisions.

SUMMARY

Selecting patients who are most appropriate for aggressive therapy involves considering risk factors for poor outcomes, early recognition of treatment failure, balancing treatment efficacy and side effects, and sharing the decision with patients to assist them in making optimal treatment choices. Vigilance for signs of treatment failure and early switching to more aggressive therapy are important components in optimal care.

Decreasing impact of late relapses on disability worsening in secondary progressive multiple sclerosis

Background:

Changes in relapse activity during secondary progressive multiple sclerosis (SPMS) need to be accurately characterized in order to identify patients who might benefit from continuing disease-modifying therapies.

Objective:

To describe relapse occurrence in patients with SPMS during long-term follow-up and assess its impact on disability worsening.

Methods:

This retrospective cohort study included 506 patients. We assessed the influence of relapses on time from SPMS onset to an Expanded Disability Status Scale score of 6 (EDSS 6), and on irreversible worsening of EDSS scores across different periods.

Results:

The annualized relapse rate (ARR) decreased with patient’s age (mean reduction of 43% per decade) and SPMS duration (mean reduction of 46% every 5 years). Post-progression relapses were associated with shorter time from secondary progressive (SP) phase onset to EDSS 6 (hazard ratio (HR) = 1.29, 95% confidence interval (CI) = (1.01, 1.64)). Relapse occurrence during the first 3 years and 3–5 years after SP onset was associated with an increased risk of irreversible EDSS worsening (OR = 3.12 (1.54, 6.31) and 2.04 (1.16, 3.58)). This association was no longer significant after 5 years.

Conclusion:

The occurrence of relapses was a marker of short-term disability progression during early SPMS, but did not have decisive impact in later SPMS.

Brazilian Consensus for the Treatment of Multiple Sclerosis: Brazilian Academy of Neurology and Brazilian Committee on Treatment and Research in Multiple Sclerosis

The expanding therapeutic arsenal in multiple sclerosis (MS) has allowed for more effective and personalized treatment, but the choice and management of disease-modifying therapies (DMTs) is becoming increasingly complex. In this context, experts from the Brazilian Committee on Treatment and Research in Multiple Sclerosis and the Neuroimmunology Scientific Department of the Brazilian Academy of Neurology have convened to establish this Brazilian Consensus for the Treatment of MS, based on their understanding that neurologists should be able to prescribe MS DMTs according to what is better for each patient, based on up-to-date evidence and practice. We herein propose practical recommendations for the treatment of MS, with the main focus on the choice and management of DMTs, as well as present a review of the scientific rationale supporting therapeutic strategies in 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.

Survival and cause of death in multiple sclerosis: a 60-year longitudinal population study

OBJECTIVE

Survival and causes of death (COD) in multiple sclerosis (MS) provide ultimate endpoints. We aimed to investigate survival and COD in a 60-year population-based MS cohort compared with the general population.

METHODS

All patients with incident multiple sclerosis (MS) (N=1388) with onset during 1953-2012 in Hordaland County, Western Norway, were included. Data were obtained from patient records at Haukeland University Hospital and linked to the Norwegian COD registry. Survival adjusted for sex, age and disease course were estimated by Kaplan-Meier analyses from birth and from disease onset. Mortality and COD in MS relative to the general population were examined by standardised mortality ratio (SMR).

RESULTS

Of 1388 patients, 291 had deceased, mainly of MS (56.4%). Median life expectancy was 74.7 years for MS and 81.8 years for the general population (p<0.001); 77.2 years for women with MS and 72.2 years for men with MS (p<0.001). Life expectancy for patients with relapsing remitting MS (RRMS) was 77.8 years and -71.4 years for primary progressive MS (PPMS) (p<0.001). Overall SMR was 2.7 (p>0.0001); 2.9 in women and 2.5 in men (p=0.0009). SMR was 2.4 in RRMS and 3.9 in PPMS (p<0.0001). SMR from disease onset during 1953-1974 was 3.1; 2.6 during 1975-1996 and 0.7 during 1997-2012 (p<0.0083). No difference in cause-specific deaths were found (p=0.0871).

CONCLUSION

We found a 7-year shorter life expectancy and almost threefold higher mortality in MS compared with the general population. A rise in survival in MS was observed during the entire observation period.

Contribution of different relapse phenotypes to disability in multiple sclerosis

OBJECTIVE

This study evaluated the effect of relapse phenotype on disability accumulation in multiple sclerosis.

METHODS

Analysis of prospectively collected data was conducted in 19,504 patients with relapse-onset multiple sclerosis and minimum 1-year prospective follow-up from the MSBase cohort study. Multivariable linear regression models assessed associations between relapse incidence, phenotype and changes in disability (quantified with Expanded Disability Status Scale and its Functional System scores). Sensitivity analyses were conducted.

RESULTS

In 34,858 relapses recorded during 136,462 patient-years (median follow-up 5.9 years), higher relapse incidence was associated with greater disability accumulation (β = 0.16, p < 0.001). Relapses of all phenotypes promoted disability accumulation, with the most pronounced increase associated with pyramidal (β = 0.27 (0.25-0.29)), cerebellar (β = 0.35 (0.30-0.39)) and bowel/bladder (β = 0.42 (0.35-0.49)) phenotypes (mean (95% confidence interval)). Higher incidence of each relapse phenotype was associated with an increase in disability in the corresponding neurological domain, as well as anatomically related domains.

CONCLUSION

Relapses are associated with accumulation of neurological disability. Relapses in pyramidal, cerebellar and bowel/bladder systems have the greatest association with disability change. Therefore, prevention of these relapses is an important objective of disease-modifying therapy. The differential impact of relapse phenotypes on disability outcomes could influence management of treatment failure in multiple sclerosis.

Mortality in multiple sclerosis: meta-analysis of standardised mortality ratios

OBJECTIVE

There are inconsistent data on mortality in people with multiple sclerosis (MS). We performed a meta-analysis of all-cause, cause-specific and gender-specific crude mortality rates (CMRs), and standardised mortality ratios (SMRs) in MS, and estimated the rate of change of CMR and SMR over the past 50 years.

METHODS

Medline, Embase and the Cochrane Library were searched.

KEYWORDS

'Multiple Sclerosis' and ('standardised mortality' or 'standardized mortality').

INCLUSION CRITERIA

availability of data on the number of deaths; mean or median patient follow-up or reports of SMRs; being a longitudinal study. 12 studies were included covering the period 1949-2012 (27 423 patients; 6628 deaths; 437 832 person-years follow-up). CMR was calculated. SMRs were extracted. CMRs and natural logarithm of SMRs were pooled by the method of the inverse of the variance. Meta-regression models were used to investigate the secular trends.

RESULTS

Pooled CMR was 9.78/1000 person-years (95% CI 6.81 to 14.02). Pooled all-cause SMR was 2.80 (95% CI 2.74 to 2.87). All-cause SMR was 2.56 (95% CI 2.47 to 2.66) in males and 3.06 (95% CI 2.97 to 3.17) in females. SMR due to cancer was 0.89 (95% CI 0.83 to 0.97). SMRs due to cardiovascular diseases, suicide, infection and respiratory diseases were 1.29 (95% CI 1.20 to 1.38), 2.13 (95% CI 1.80 to 2.51) and 2.91 (95% CI 2.60 to 3.26). There was no trend in CMRs, all-cause, and gender-specific SMRs.

CONCLUSIONS

The excess mortality in MS relative to the general population has not changed over the past 50 years. Female patients with MS have higher survival disadvantage compared to that of males. Death due to cardiovascular diseases, suicide and infection is higher in patients with MS compared to the general population.

Treatment Optimization in Multiple Sclerosis

The treatment of multiple sclerosis has finally become possible with the advent of the current disease-modifying therapies (DMTs) that have had a significant impact on those living with this disease. Though demonstrating clear efficacy on a number of short-term outcome measures, unfortunately, these agents are not “cures” and many patients with multiple sclerosis continue to experience disease activity in spite of treatment. Clinicians are becoming more comfortable initiating therapy with DMTs, but it is now important to focus attention on monitoring the results of the chosen therapy and deciding whether or not a patient is responding well to treatment. At present, however, clinicians lack criteria for defining optimal versus suboptimal responses to DMTs as well as evidence-based guidelines on how to improve treatment outcomes. Using a recently published model as a framework, The Canadian Multiple Sclerosis Working Group developed practical recommendations on how neurologists can assess the status of patients on DMTs and decide when it may be necessary to modify treatment in order to optimize outcomes. The Canadian Multiple Sclerosis Working Group's recommendations are based on monitoring relapses, neurological progression and MRI activity. Other possible causes of suboptimal treatment responses or treatment failure are also considered.

Disability profile of multiple sclerosis in New Zealand

New Zealand is a high risk region for multiple sclerosis (MS). The aim of this study was to investigate demographic, clinical and temporal factors associated with disability status in the New Zealand National Multiple Sclerosis Prevalence Study (NZNMSPS) cohort. Data were obtained from the 2006 NZNMSPS with MS diagnosis based on the 2005 McDonald criteria. Disability was assessed using the Expanded Disability Status Scale (EDSS). Disability profiles were generated using multiple linear regression analysis. A total of 2917 persons with MS was identified, of whom disability data were available for 2422 (75% females). The overall disability was EDSS 4.4±standard deviation 2.6. Higher disability was associated with older age, longer disease duration, older and younger ages of onset, spinal cord syndromes with motor involvement at onset, and a progressive onset type. Lower disability was associated with sensory symptoms at onset and a relapsing onset type. Overall, the factors studied explained about one-third of the variation in disability, and of this, about two-thirds was accounted for by age, age of onset and disease duration and one-third by the nature of first symptoms and type of disease onset (progressive or relapsing). Current age, age at onset and disease duration all had independent associations with disability and their effects also interacted in contributing to higher disability levels over the course of the disease.

A web-based decision support tool for prognosis simulation in multiple sclerosis

A multiplicity of natural history studies of multiple sclerosis provides valuable knowledge of the disease progression but individualized prognosis remains elusive. A few decision support tools that assist the clinician in such task have emerged but have not received proper attention from clinicians and patients. The objective of the current work is to implement a web-based tool, conveying decision relevant prognostic scientific evidence, which will help clinicians discuss prognosis with individual patients. Data were extracted from a set of reference studies, especially those dealing with the natural history of multiple sclerosis. The web-based decision support tool for individualized prognosis simulation was implemented with NetLogo, a program environment suited for the development of complex adaptive systems. Its prototype has been launched online; it enables clinicians to predict both the likelihood of CIS to CDMS conversion, and the long-term prognosis of disability level and SPMS conversion, as well as assess and monitor the effects of treatment. More robust decision support tools, which convey scientific evidence and satisfy the needs of clinical practice by helping clinicians discuss prognosis expectations with individual patients, are required. The web-based simulation model herein introduced proposes to be a step forward toward this purpose.

Aggressive multiple sclerosis: proposed definition and treatment algorithm

Multiple sclerosis (MS) is a CNS disorder characterized by inflammation, demyelination and neurodegeneration, and is the most common cause of acquired nontraumatic neurological disability in young adults. The course of the disease varies between individuals: some patients accumulate minimal disability over their lives, whereas others experience a rapidly disabling disease course. This latter subset of patients, whose MS is marked by the rampant progression of disability over a short time period, is often referred to as having 'aggressive' MS. Treatment of patients with aggressive MS is challenging, and optimal strategies have yet to be defined. It is important to identify patients who are at risk of aggressive MS as early as possible and implement an effective treatment strategy. Early intervention might protect patients from irreversible damage and disability, and prevent the development of a secondary progressive course, which thus far lacks effective therapy.

Flexible modeling of disease activity measures improved prognosis of disability progression in relapsing-remitting multiple sclerosis

OBJECTIVES

To illustrate the advantages of updating time-varying measures of disease activity and flexible modeling in prognostic clinical studies using the example of the association between the frequency of past relapses and occurrence of ambulation-related disability in multiple sclerosis (MS).

STUDY DESIGN AND SETTING

Longitudinal population-based study of 288 patients from Burgundy, France, diagnosed with relapsing-remitting MS in 1990-2003. The end point was a nonreversible moderate MS disability (European Database for Multiple Sclerosis score ≥ 3.0 derived from Extended Disability Status Scale). Alternative time-varying measures of attacks frequency included (1) conventional number of early MS attacks in the first 2 years after diagnosis; and two new measures, continuously updated during the follow-up; (2) cumulative number of past attacks; and (3) number of recent attacks, during the past 2 years. Multivariate analyses used Cox proportional hazards model and its flexible generalization, which accounted for time-dependent changes in the hazard ratios (HRs) for different attack frequency measures.

RESULTS

HRs for all measures decreased significantly with increasing follow-up time. The proposed updated number of recent attacks improved model's fit to data, relative to alternative measures of attack frequency, and was associated with a statistically significantly increased hazard of developing ambulation-related MS disability in the next 2 years during the entire follow-up period.

CONCLUSION

Updated measures of recent disease activity, such as frequency of recent attacks and modeling of their time-dependent effects, may substantially improve prognosis of clinical outcomes, such as development of MS disability.

Treatment Optimization in MS: Canadian MS Working Group Updated Recommendations

The Canadian Multiple Sclerosis Working Group (CMSWG) developed practical recommendations in 2004 to assist clinicians in optimizing the use of disease-modifying therapies (DMT) in patients with relapsing multiple sclerosis. The CMSWG convened to review how disease activity is assessed, propose a more current approach for assessing suboptimal response, and to suggest a scheme for switching or escalating treatment. Practical criteria for relapses, Expanded Disability Status Scale (EDSS) progression and MRI were developed to classify the clinical level of concern as Low, Medium and High. The group concluded that a change in treatment may be considered in any RRMS patient if there is a high level of concern in any one domain (relapses, progression or MRI), a medium level of concern in any two domains, or a low level of concern in all three domains. These recommendations for assessing treatment response should assist clinicians in making more rational choices in their management of relapsing MS patients.

Onset of secondary progressive phase and long-term evolution of multiple sclerosis

OBJECTIVES

To assess factors affecting the rate of conversion to secondary progressive (SP) multiple sclerosis (MS) and its subsequent evolution.

METHODS

Among 806 patients with relapsing remitting (RR) onset MS from the London Ontario database, we used Kaplan-Meier, Cox regression and multiple logistic regression analyses to investigate the effect of baseline clinical and demographic features on (1) the probability of, and the time to, SP disease, (2) the time to bedbound status (Disability Status Scale (DSS 8)) from onset of progression.

RESULTS

The risk of entering the SP phase increased proportionally with disease duration (OR=1.07 for each additional year; p<0.001). Shorter latency to SP was associated with shorter times to severe disability. The same association was found even when patients were grouped by number of total relapses before progression. However, the evolution of the SP phase was not influenced by the duration of the RR phase. Male sex (HR=1.41; p<0.001), older age at onset (age ≤20 and 21-30 vs >30 HR=0.52 (p<0.001), 0.65 (p<0.001), respectively) and high early relapse frequency (1-2 attacks vs ≥3 HR=0.63 (p<0.001), 0.75 (p=0.04), respectively) predicted significantly higher risk of SP MS and shorter latency to progression. Times to DSS 8 from onset of progression were significantly shorter among those with high early relapse frequency (≥3 attacks), and among those presenting with cerebellar and brainstem symptoms.

CONCLUSIONS

The onset of SP MS is the dominant determinant of long-term prognosis, and its prevention is the most important target measure for treatment. Baseline clinical features of early relapse frequency and age at onset can be used to select groups at higher risk of developing severe disability based on the probability of their disease becoming progressive within a defined time period.

An agent-based simulation model for informed shared decision making in multiple sclerosis

Shared decision making (SDM) is concerned with patient involvement into medical decisions and chronic conditions such as Multiple sclerosis (MS), with only partially effective treatments leading to potential severe side effects, conflicting evidence, and uncertain evidence on outcomes, constitute a typical condition for SDM. As treatment options increase and patients participate more intensively in decisions, the need for evidence-based information (EBI) becomes clear. Natural history (NH) studies of MS represent the basic sources for required EBI and are especially useful to contribute to the practical exercise of prognosis formulation and to enable the evaluation of effectiveness in the context of treatment. Several of these identify early clinical factors predictive of the course of MS but there is no consensus method for determining the long term progression of disability and evolution of individual patients on the basis of observations on the early stages of the disease, which constitutes a major challenge for the practicing neurologist. Aiming at delivering more reliable prognosis estimation, this study combines the distribution of patients reaching specific levels of disability within defined time periods as determined in NH studies, with disability curves and severity scores as a function of time, in terms of percentiles and deciles respectively, derived from longitudinal data analysis studies. A computer agent-based simulation model was implemented as a comprehensive and easy to utilize tool able to predict and monitor progression of disability in MS patients, and to support the neurologist discussing prognosis scenarios with the individual patient for effective SDM.

Health-related quality of life as an independent predictor of long-term disability for patients with relapsing-remitting multiple sclerosis

BACKGROUND AND PURPOSE

Predictive factors of long-term disability in patients with relapsing-remitting multiple sclerosis (RR-MS) are well known, but the weight of these factors does not explain the entire change of disability. Few studies have examined the predictive value of quality of life (QoL).

OBJECTIVES

To determine the value of the initial level of QoL to predict changes in the disability status of patients with MS and to determine if specific-MS questionnaires predict more accuracy in these changes than generic questionnaires.

METHODS

DESIGN

multicenter, multi-regional, and longitudinal study. Main inclusion criteria: patients with RR-MS subtype and an Expanded Disability Status Scale (EDSS) score lower than 7.0. Sociodemographic and clinical data were recorded at baseline. Every 6 months up to month 24, QoL (MusiQoL and SF-36) was recorded. At 24 months, individuals were defined into two 'disability change' groups: the worsened and not worsened patients.

RESULTS

Five-hundred and twenty-six patients were enrolled: 386 (83.7%) not worsened and 75 (16.3%) worsened patients at 24 months. The activity of daily living and the relationship with healthcare workers dimensions of MusiQoL questionnaire were independent predictors of change in the EDSS score after 24 months. The physical-functioning dimension of the SF-36 questionnaire predicted independently disability change after 24 months.

CONCLUSIONS

Patient-reported baseline QoL levels provide additional prognostic information on MS disability beyond traditional clinical or sociodemographic factors. These findings reinforce the importance of incorporating a patient's evaluation of their own QoL level during patient monitoring and the assessment of treatment effects.