Most RIS Patients Likely to Progress to MS Within 10 Years, Study Says

Stuart SchlossmanMultiple Sclerosis

JULY 14, 2020 —- BY MARISA WEXLER, MS

Most RIS Patients Likely to Progress to MS Within 10 Years, Study Says


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More half of people with brain imaging changes akin to multiple sclerosis (MS) go on to develop the disease within 10 years, a global study of those with radiologically isolated syndrome reported.
Progression to MS appears to be more likely in those who are younger, have spinal cord lesions, or have oligoclonal bands in the cerebrospinal fluid (SCF) and are a sign of inflammation-related proteins, its findings suggested.
Radiologically isolated syndrome, or RIS, is a rare phenomenon in which a person has no apparent MS symptoms but has MS-like brain damage visible on an MRI brain scan. The phenomenon was formally defined in 2009.
The extent to which people with RIS are likely to develop MS — or if specific features of RIS patients make them more or less likely to progress to symptomatic disease — remains unclear.

Researchers — in the U.S., France, Italy, and Turkey — analyzed clinical and demographic data from 451 people with RIS. These data were collected from 21 clinical sites across five countries. Of the people included, 141 had data that was current as of 2014 when a previous analysis using data from the same group was published.
Most of these 451 RIS patients were female (86.5%) and white (86%), and their average age at RIS diagnosis was 37.2.
“This cohort, which includes longitudinally acquired clinical and MRI data, is currently the largest and longest published study of RIS individuals,” the researchers wrote.

Results showed that the estimated risk of a person experiencing a clinical event — that is, developing MS symptoms — was 51.2% at 10 years after a RIS diagnosis.
“This trend towards symptom evolution also appeared to extend out beyond 10 years,” the researchers wrote.
Of the 173 people who developed symptoms, 21 (11.7%) fulfilled the criteria for primary progressive MS (PPMS).

The team then used statistical models to find factors potentially associated with a higher likelihood of a clinical event.

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