Early clinical markers of aggressive multiple sclerosis

Neurology, 2012

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

Journal of Neurology, Neurosurgery & Psychiatry, 2013

Objective To explore the occurrence and characteristics of aggressive multiple sclerosis (AMS) in adult-onset multiple sclerosis (MS) patients. Methods Prospectively collected data from British Columbia, Canada, were retrospectively analysed. AMS was defined in three different ways (AMS1, 2 and 3): 'AMS1'-confirmed Expanded Disability Status Scale (EDSS) ≥6 within 5 years of MS onset; 'AMS2'-confirmed EDSS ≥6 by age 40; and 'AMS3'-secondary progressive MS within 3 years of a relapsing-onset course. Three respective 'non-aggressive' MS comparison cohorts were selected. Patients' characteristics were compared between aggressive and non-aggressive cohorts using multivariable logistic regression, with findings expressed as adjusted OR (AOR) and 95% CI. Results Application of the three definitions to the source population of 5891 patients resulted in 235/4285 (5.5%) patients fulfilling criteria for AMS1 (59.6% were female; 74.5% had relapsing-onset MS), 388/2762 (14.0%) for AMS2 (65.2% were female; 92.8% had relapsing-onset MS) and 195/4918 (4.0%) patients for AMS3 (61.0% were female). Compared to the respective control cohorts, those with AMS were more likely to be male (AOR=1.5, 95% CI 1.1 to 2.0 (AMS1); 1.6, 95% CI 1.3 to 2.1 (AMS2); 1.8, 95% CI 1.3 to 2.4 (AMS3)), older at MS symptom onset (AOR=1.1; 95% CI 1.1 to 1.1 (AMS1 and AMS3)) and have primary progressive MS (AOR=2.3, 95% CI 1.6 to 3.3 (AMS1); 2.7, 95% CI 1.7 to 4.4 (AMS2)). Conclusions AMS was identified in 4-14% of patients, depending on the definition used. Although there was a relative preponderance of men and primary progressive MS presenting with AMS, the majority of patients were still women and those with relapsing-onset MS.

European journal of neurology : the official journal of the European Federation of Neurological Societies, 2015

Early prediction of long-term disease evolution is a major challenge in the management of multiple sclerosis (MS). Our aim was to predict the natural course of MS using the Bayesian Risk Estimate for MS at Onset (BREMSO), which gives an individual risk score calculated from demographic and clinical variables collected at disease onset. An observational study was carried out collecting data from MS patients included in MSBase, an international registry. Disease impact was studied using the Multiple Sclerosis Severity Score (MSSS) and time to secondary progression (SP). To evaluate the natural history of the disease, patients were analysed only if they did not receive immune therapies or only up to the time of starting these therapies. Data from 14 211 patients were analysed. The median BREMSO score was significantly higher in the subgroups of patients whose disease had a major clinical impact (MSSS≥ third quartile vs. ≤ first quartile, P < 0.00001) and who reached SP (P < 0.000...

Frontiers in neurology, 2017

The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n = 133) and validation (n = 68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantag...

Multiple Sclerosis Journal

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 managem...

Frontiers in Neurology, 2021

Multiple sclerosis (MS) is primarily an inflammatory and degenerative disease of the central nervous system, triggered by unknown environmental factors in patients with predisposing genetic risk profiles. The prevention of neurological disability is one of the essential goals to be achieved in a patient with MS. However, the pathogenic mechanisms driving the progressive phase of the disease remain unknown. It was described that the pathophysiological mechanisms associated with disease progression are present from disease onset. In daily practice, there is a lack of clinical, radiological, or biological markers that favor an early detection of the disease's progression. Different definitions of disability progression were used in clinical trials. According to the most descriptive, progression was defined as a minimum increase in the Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 from a baseline level of 0, 1.0–5.0, and 5.5, respectively. Nevertheless, the EDSS is not...

Neurology, 2007

Objective:To develop covariate specific short-term disability curves to demonstrate the probability of progressing by Expanded Disability Status Scale (EDSS) at semiannual visits.Methods:Semiannual EDSS scores were prospectively collected in 218 relapsing-remitting (RR) and clinically isolated syndrome (CIS) patients as part of the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB) study. Baseline brain parenchymal fraction (BPF) and T2 lesion volume were available on 205 patients. A partial proportional odds model determined the influence of covariates on the change in EDSS score at subsequent visits. A discrete second order Markov transitional model was fit and generated a probability matrix for each subject; the 6-month probabilities of EDSS change were graphically represented.Results:The univariate analysis demonstrated the lowest baseline BPF quartile (OR 1.99;p= 0.0203) and the highest T2 lesion volume quartile (OR 2....

Frontiers in Neurology

BackgroundEarly identification of the transition from relapsing-remitting multiple sclerosis (RRMS) to secondary progressive MS (SPMS) can be challenging for clinicians, as diagnostic criteria for SPMS are primarily based on physical disability and a holistic interpretation.ObjectiveTo establish a consensus on patient monitoring to identify promptly disease progression and the most useful clinical and paraclinical variables for early identification of disease progression in MS.MethodsA RAND/UCLA Appropriateness Method was used to establish the level of agreement among a panel of 15 medical experts in MS. Eighty-three items were circulated to the experts for confidential rating of the grade of agreement and recommendation. Consensus was defined when ≥66% agreement or disagreement was achieved.ResultsConsensus was reached in 72 out of 83 items (86.7%). The items addressed frequency of follow-up visits, definition of progression, identification of clinical, cognitive, and radiological ...

Journal of the Neurological Sciences, 2013

Background: Daily practice is still faced with uncertainty in predicting the long-term disability of multiple sclerosis (MS). Most information comes from northern hemisphere cohorts, but in South America this information is scarce, and race, genetic and environmental factors could play an important role in the heterogeneity observed in disease outcomes. Methods: We evaluated 197 patients attending our MS Center gathering clinical and demographic information. Outcome measures analyzed were time from first clinical symptom to EDSS of 6, 7 and 8. For survival analysis we employed Cox regression models and the Kaplan-Meier method. Results: Time to EDSS 6 was 25.83 years (95% CI 15.36-36.31), and 36.25 years (95% CI 20.72-51.78) for EDSS 7. Male sex was associated with a 4.63 and 4.69 fold increased risk to EDSS 6 and 7, respectively (p b 0.001 and p = 0.006). Motor and brainstem symptoms at onset were also associated with an 8.1 and 13.1 fold increased risk to EDSS 6, respectively (p = 0.04 and p = 0.01). The number of relapses in five and ten years of disease onset was associated with a slightly increased risk to EDSS 8 (1.28 and 1.19, respectively; p = 0.032 and p = 0.015). Conclusions: Male patients presenting with frequent relapses, especially those with motor and brainstem involvement, deserve close observation and should be cautiously monitored to early signs of treatment failure.

Neurology, 1996

Accurate clinical course descriptions (phenotypes) of multiple sclerosis (MS) are important for communication, prognostication, design and recruitment of clinical trials, and treatment decision-making. Standardized descriptions published in 1996 based on a survey of international MS experts provided purely clinical phenotypes based on data and consensus at that time, but imaging and biological correlates were lacking. Increased understanding of MS and its pathology, coupled with general concern that the original descriptors may not adequately reflect more recently identified clinical aspects of the disease, prompted a re-examination of MS disease phenotypes by the International Advisory Committee on Clinical Trials of MS. While imaging and biological markers that might provide objective criteria for separating clinical phenotypes are lacking, we propose refined descriptors that include consideration of disease activity (based on clinical relapse rate and imaging findings) and disease progression. Strategies for future research to better define phenotypes are also outlined.