Clinical, Biochemical and Imaging Parameters that may be Predictive of High Disease Activity, Rapid Progression or Increased Disability in Multiple Sclerosis

2013 ◽  
Vol 8 ((Suppl.1)) ◽  
pp. 10
Author(s):  
Anders Svenningsson ◽  
Barry A Hendin ◽  
◽  
Keyword(s):  
Multiple Sclerosis ◽  
Age At Onset ◽  
Scoring Systems ◽  
Predictive Biomarker ◽  
Individual Characteristics ◽  
Response To Treatment ◽  
Prognostic Indicators ◽  
Rapid Progression ◽  
Disease Course ◽  
Alternative Means

Increased ability to predict disease course and response to disease-modifying therapies in multiple sclerosis (MS) would optimise treatment outcomes by guiding selection of patients for a particular therapeutic intervention. Several factors affecting disease progression have been identified, including individual characteristics such as age at onset and race onset of symptoms, early disease outcomes and radiological measures. While studies of magnetic resonance imaging (MRI) prognostic indicators have given mixed results, advances in technology are increasing the predictive power of MRI, and new techniques and outcome measures are providing alternative means of predicting disease course and response to treatment. The search for a predictive biomarker is an area of active research but studies remain poorly validated. Potential biomarkers include neurofilament proteins, microRNAs, gene expression and antibodies. Since it is unlikely that a single factor may predict disease course, a number of composite scoring systems have been proposed, but none have yet received widespread acceptance. However, it seems likely that in the future, a combination of MRI and biochemical biomarkers will provide a foundation for therapeutic decision-marking in MS allowing an individualised approach.

Characteristics of multiple sclerosis in aboriginals living in British Columbia, Canada

2012 ◽  
Vol 18 (9) ◽  
pp. 1239-1243 ◽  
Author(s):  
Jameelah Saeedi ◽  
Peter Rieckmann ◽  
Irene Yee ◽  
Helen Tremlett ◽  
Keyword(s):  
Multiple Sclerosis ◽  
British Columbia ◽  
Clinical Characteristics ◽  
Age At Onset ◽  
Disease Onset ◽  
Patient Characteristics ◽  
Onset Date ◽  
Disease Course ◽  
Chi Squared ◽  
Student’S T

Objectives: The objectives of this study were to identify and describe the demographic and clinical characteristics of multiple sclerosis (MS) in aboriginals in British Columbia (BC), Canada and compare these findings with non-aboriginal MS patients. Methods: This retrospective chart and database review accessed patient information from the linked BC-wide MS clinical and genetics databases. Data gathered included: demographics (age, sex and ethnicity); clinical characteristics (MS onset date, disease course and disability scores (Expanded Disability Status Scale [EDSS]). Aboriginals were identified via the database linkage augmented by physician and nurse recall. Two non-aboriginal comparator groups with definite MS were selected. Group one included all definite MS patients in the BC MS database, and group two comprised MS patients matched by sex, age at onset and initial disease course. Patient characteristics were compared using the Student’s t-test, chi-squared test, and Kaplan–Meier survival analysis was used to examine disease progression (time to sustained and confirmed EDSS 6) Results: We identified 26 aboriginals with MS, of which 19/26 (73%) were female, 23/26 (89%) had relapsing-onset MS and a mean onset age of 31.1 years. There were no significant differences between the MS aboriginals and the non-matched ( n = 5708) comparator group with respect to age, sex or disease course ( p > 0.1), However, aboriginals progressed more rapidly to EDSS 6 from disease onset ( p < 0.001) when compared with the matched and unmatched comparator groups. Conclusion: We identified a small, but important cohort of aboriginals with MS; being the largest identified to date. There was evidence of more rapid MS progression in aboriginals compared with non-aboriginals.

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

2011 ◽  
Vol 18 (1) ◽  
pp. 45-54 ◽  
Author(s):  
M Cossburn ◽  
G Ingram ◽  
C Hirst ◽  
Y Ben-Shlomo ◽  
TP Pickersgill ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Late Onset ◽  
Age At Onset ◽  
Disease Onset ◽  
Complete Recovery ◽  
Population Based ◽  
Disease Course ◽  
Index Event ◽  
Relapsing Remitting ◽  
Age Dependent

Background: Age at onset modifies prognosis in multiple sclerosis (MS) and may also exert an effect on the characteristics of disease ignition. Understanding how age influences presentation informs disease management and may allow differentiation of distinct clinical sub-groups. Objectives: To determine the nature of age-specific presentations of relapsing–remitting MS (RRMS) with respect to onset symptoms, gender ratios and index event outcomes. Methods: In a prospective, population-based sample of 1424 patients in South-East Wales we examined associations between age at onset, clinical features and outcome of the onset event, making specific comparisons between paediatric, adolescent and late-onset MS. Results: Age at onset varied significantly between sexes (Male 31.2, Female 29.3, p = 0.002), 0.7% had paediatric onset, 2.7% adolescent onset and 2.8% late-onset MS (>50 years). Optic neuritis was common in younger patients and declined after age 30. Lower limb motor, facial sensory, sexual and sphincteric symptoms rose with age independent of sex and disease course. F:M ratios were highest <16 years of age and declined with increasing age, with a male excess in those over 50. Probability of complete recovery from index event declined with age from 87.4% in the youngest group to 68% in the eldest ( p = 0.009). Conclusions: Age at disease onset in RRMS exerts a significant effect on gender ratios and presenting phenotype, and allows identification of specific clinical sub-groups. In addition, ability to recover from initial relapse declines with age, suggesting accumulation of disability in MS is an age-dependent response to relapse.

Primary progressive versus relapsing-onset multiple sclerosis: presence and prognostic value of cerebrospinal fluid oligoclonal IgM

2010 ◽  
Vol 17 (3) ◽  
pp. 303-311 ◽  
Author(s):  
Patrizia Sola ◽  
Jessica Mandrioli ◽  
Anna M Simone ◽  
Diana Ferraro ◽  
Roberta Bedin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Cerebrospinal Fluid ◽  
Prognostic Value ◽  
Age At Onset ◽  
Disease Course ◽  
Expanded Disability Status Scale ◽  
Biological Factors ◽  
Primary Progressive ◽  
Disability Status ◽  
The Impact

Background: There is increasing evidence on cerebrospinal fluid (CSF) oligoclonal IgM (OCIgM) predicting a more aggressive disease course in relapsing–remitting Multiple Sclerosis (MS), while there is a scarcity of data for primary progressive MS (PPMS). Objective: Our aim was to investigate the presence and possible prognostic value of CSF OCIgM in a group of PPMS and in a group of relapsing-onset MS patients. The possible prognostic role of other clinical and biological factors was also evaluated. Methods: We calculated the impact of single clinical and biological factors, including CSF OCIgM at onset, on the probability of reaching an Expanded Disability Status Scale of 3 and 4 in 45 PPMS and 104 relapsing-onset MS patients. Results: CSF OCIgM were found in only 13% of PPMS patients and did not influence the time taken to reach an Expanded Disability Status Scale of 3 and 4. Conversely, they were present in 46% of relapsing-onset MS patients and increased the risk of reaching an Expanded Disability Status Scale of 4. Clinical factors with a negative prognostic value in PPMS were age at onset <30 years and onset with pyramidal symptoms, while onset with sensory symptoms in relapsing-onset MS predicted a more favourable course. Conclusion: This study confirms that, in relapsing-onset MS patients, the presence of CSF OCIgM at onset predicts a worse disease course. In the cohort of PPMS patients, however, CSF OCIgM were rare, suggesting that heterogeneous pathogenetic mechanisms may be involved in the different MS forms.

The impact of HLA-A and -DRB1 on age at onset, disease course and severity in Scandinavian multiple sclerosis patients

2007 ◽  
Vol 14 (8) ◽  
pp. 835-840 ◽  
Author(s):  
C. Smestad ◽  
B. Brynedal ◽  
G. Jonasdottir ◽  
Å. R. Lorentzen ◽  
T. Masterman ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Age At Onset ◽  
Disease Course ◽  
Multiple Sclerosis Patients ◽  
The Impact

The most fulminant course of the Marburg variant of multiple sclerosis—autopsy findings

2014 ◽  
Vol 21 (4) ◽  
pp. 485-487 ◽  
Author(s):  
JC Nunes ◽  
H Radbruch ◽  
R Walz ◽  
K Lin ◽  
W Stenzel ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Inflammatory Disease ◽  
Clinical Presentation ◽  
Severe Disease ◽  
Clinical Signs ◽  
Demyelinating Diseases ◽  
Rapid Progression ◽  
Disease Course ◽  
Autopsy Findings ◽  
One Year

Multiple sclerosis (MS) is usually a chronic and disabling inflammatory disease. Marburg’s type of MS is characterized by rapid progression and severe disease course that leads to death within one year after the onset of clinical signs. We describe a fulminant clinical presentation of this malignant subtype of MS and discuss the neuropathological hallmarks as well as differential diagnoses of other fulminant demyelinating diseases. To the best of our knowledge, this is the most fulminant course of this MS variant reported in the literature.

scholarly journals Late onset multiple sclerosis: concerns in aging patients

2017 ◽  
Vol 75 (7) ◽  
pp. 451-456 ◽  
Author(s):  
Claudia Beatriz de Campos Lotti ◽  
Acary Souza Bulle Oliveira ◽  
Denis Bernardi Bichuetti ◽  
Isac de Castro ◽  
Enedina Maria Lobato Oliveira
Keyword(s):  
Multiple Sclerosis ◽  
Medical Records ◽  
Clinical Characteristics ◽  
Matched Control ◽  
Late Onset ◽  
Age At Onset ◽  
Control Group ◽  
Disease Course ◽  
Matched Control Group ◽  
Progressive Course

ABSTRACT Late onset multiple sclerosis (LOMS) is when the first symptom starts after 50 years of age, representing 4.5% of multiple sclerosis (MS) patients. This study describes the clinical characteristics of patients with LOMS followed at a specialized MS center in São Paulo. Data was obtained from medical records of 742 patients with MS. The LOMS frequency was 4.18%, median age at onset was 54 years and the predominant disease course was primary progressive (64.3%). The patients reached the disability landmarks of EDSS grades 3.0, 6.0 and 7.0 in the following proportion and time: EDSS 3.0: 77.42% of patients in 3.7 years; EDSS 6.0: 58.06% in 5.1 years and EDSS 7.0: 32.26% in 5.7 years. The comparative analysis with a matched control group of patients with early onset MS showed that late onset, associated with a progressive course, were predictors of reaching EDSS 3.0 and 6.0 in a shorter time.

scholarly journals Exploring polypharmacy phenomenon in newly diagnosed relapsing–remitting multiple sclerosis: a cohort ambispective single-centre study

2021 ◽  
Vol 12 ◽  
pp. 204062232098312
Author(s):  
Aurora Zanghì ◽  
Emanuele D’Amico ◽  
Salvatore Lo Fermo ◽  
Francesco Patti
Keyword(s):  
Multiple Sclerosis ◽  
Age At Onset ◽  
Rank Test ◽  
Multinomial Regression ◽  
Single Centre ◽  
Centre Study ◽  
Relapsing Remitting ◽  
Single Centre Study ◽  
Remitting Multiple Sclerosis ◽  
Relapsing Remitting Multiple Sclerosis

Aims: We aimed to examine the frequency of polypharmacy in a large cohort of patients at the time of diagnosis of relapsing–remitting multiple sclerosis (RRMS) and to explore its effects on discontinuation of first disease-modifying treatment (DMT) using survival analysis. Methods: This was a cohort ambispective single-centre study. We enrolled RRMS patients starting their first DMT between 1st January 2013 and 31st December 2015. According to the number of medicines prescribed (except DMTs), we divided the patients into three groups: no-poly RRMS, minor-poly RRMS (from one to three medications), and major-poly RRMS (more than three medications). Results: A total of 392 RRMS patients were enrolled (mean age 41.1). The minor-poly RRMS group included 61 patients (15.6%) and the major-poly RRMS group included 112 (28.6%). Individuals in these groups were older and had higher median body mass index (BMI) than patients in the no-poly RRMS group ( p < 0.05). Upon multinomial regression analysis, older age at onset was associated with minor and major polypharmacy (OR 1.050, CI 1.010–1.093, p = 0.015 and OR 1.063, CI 1.026–1.101, p = 0.001, respectively) and higher BMI was associated with major polypharmacy (OR 1.186, CI 1.18–1.29, p = 0.001). The rates of discontinuation of first DMT were similar among the three groups (50.7% for no-Poly RRMS, 50.8% for minor-Poly RRMS, and 53.3% for major-Poly RRMS, p = 0.264). At log-Rank test, there were no differences among the three groups ( p = 0.834). Conclusion: Polypharmacy was more common in older RRMS patients with high BMI.

scholarly journals Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 122
Author(s):  
Ruggiero Seccia ◽  
Silvia Romano ◽  
Marco Salvetti ◽  
Andrea Crisanti ◽  
Laura Palagi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
High Impact ◽  
Prognostic Models ◽  
Early Prediction ◽  
Disease Course ◽  
Slow Progression ◽  
Relapsing Remitting ◽  
Long Time ◽  
Effective Drugs

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

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