Relapsing Remitting Multiple Sclerosis
Conditions
Brief summary
In relapsing remitting multiple sclerosis (RRMS) the relationship between cognitive impairment (CI), fatigue and physical disability with white matter lesion load (WM-LL), location among other volumetric measures using automated platforms is still unclear.
Detailed description
Cognitive impairment (CI) and fatigue have been recognized as an important feature of MS, affecting up to 70% patients (1), evident since onset and increase in both prevalence and severity as the disease progresses (2). In fact, their effects on patients and even their caregivers are more pronounced than clinical disability, causing unemployment, treatment non-adherence, personality changes as well as several psychosocial dysfunctions (3-5). Therefore, beside evaluating the physical disability, it is essential for health professionals to objectively evaluate either the cognitive function or fatigue at both baseline and during routine follow up visits for early detection and management (6). Through the advances in MRI techniques and availability of a number of automated software, quantitative radiological assessments became more readily available and feasible in daily practice (7) allowing objective longitudinal monitoring of patients (8,9). Although burden and location of lesions in RRMS is thought to be associated with cognitive impairment (CI), fatigue and physical disability, some controversy results were obtained from previous studies. So, by conducting this study, we aimed at exploring the relationship between different parameters of lesion load and location with fatigue, cognitive and physical disability in RRMS patients.
Interventions
DIAGNOSTIC_TESTVolBrain, a fully automated platform to generate the volumetric data (lesion count, volume and location as well as different atrophy measures)
VolBrain, a fully automated platform that uses anonymized compressed NIFTI files to generate the volumetric data. Volbrain is increasingly recognized and compared to other volumetric tools. LesionBrain is a pipeline to automatically segment WM-L from T1 and FLAIR data. Number of lesions, absolute total lesion volume (in cubic cm), normalized lesion volume (percentage of total lesion volume to whole brain volume), lesion burden (percentage of total WM lesion volume to WM volume), and location (Periventricular, Juxtacortical, and Infratentorial) were obtained.
OTHERBrief International Cognitive Assessment for Multiple Sclerosis (BICAMS)
It includes the Symbol Digit Modalities Test (SDMT) for evaluating the information processing speed, the California Verbal Learning Test (CVLT-II) for evaluating verbal learning and memory, and the Brief Visual Memory Test (BVMT) for evaluating visual learning and memory. Cut off values were calculated as 1.5 SD below mean according to a group of healthy individuals who are matched in age, sex and education as following: 22 for SDMT, 38 for CVLT, and 10 for BVMTR
Sponsors
Assiut University
Study design
Observational model
CASE_ONLY
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* : adult patients of both sexes diagnosed with RRMS according to 2017 McDonald diagnostic criteria
Exclusion criteria
* Patients with relapse or steroid administration in the past 30 days were excluded. Patients who have any medical conditions that may affect cognition, and those on psychoactive pharmacotherapy were also excluded.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The relation of white matter lesion load, location and parameters of cognitive function measured by BICAMS | 6 months | correlation of BICAMS subtests scores to different parameters of white matter lesion load, distribution and load (volume and number of lesion) measurements (whole brain atrophy, grey matter and white matter lesion load) to identify best predictors of cognitive impairment |
| The relation of white matter lesion load, location and Fatigue in patients with relapsing remitting multiple sclerosis | 6 months | correlation between fatigue severity, with white matter lesion load, and distribution in order to identify predictors of fatigue |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| the relation of white matter lesion load, location and atrophy parameters to physical disability in patients with relapsing remitting multiple sclerosis | 6 months | correlation of EDSS scores, with white matter lesion load and distribution |
Countries
Egypt
Outcome results
None listed