Escalate treatment with risk of secondary progressive MS

Multiple sclerosis

By Mardi Chapman

21 Aug 2019

Associate Professor Tomas Kalincik

Associate Professor Tomas Kalincik

Research from a global study of almost 16,000 patients with adult-onset relapsing-remitting MS has led to an algorithm for predicting conversion to secondary progressive MS (SPMS).

The observational study, published in Multiple Sclerosis Journal and using data from the MSBase cohort, modeled the changing risk of SPMS at every clinic visit over 100,573 patient-years.

About 10% of patients converted to SPMS during their follow-up with a median time to SPMS of 32.4 years.

The study found older age, longer disease duration, worsening disability, a rapid disease trajectory and a greater number of relapses in the previous year were associated with conversion to SPMS.

A greater proportion of time spent on disease-modifying therapy and an improving disability trajectory ameliorated the risk of conversion.

Brain MRI activity, spinal MRI disease burden and the presence of oligoclonal bands in the CSF were not associated with SPMS.

The study used a case history to demonstrate an overall increase in the relative risk of SPMS conversion from two- to more than six-fold when combining all the individual risk factors over about 17 years.

One of the lead authors Associate Professor Tomas Kalincik, from the Royal Melbourne Hospital, told the limbic the findings were consistent with other research published earlier this year.

That study, previously reported here in the limbic, found disease-modifying therapies can reduce the risk of SPMS conversion.

Professor Kalincik said escalating therapy while patients still had relapsing-remitting MS was the key given there was little treatment approved and funded for secondary progressive MS.

“That may change in the future when there is more evidence,” he said.

“We have shown that proactive treatment with high efficacy therapies particularly early in the disease helps us delay the conversion to SPMS.”

“People in general can benefit from this approach but a predictive algorithm such as this one, may help us potentially identify someone who is at added risk of conversion to SPMS and not yet on high efficacy therapy.”

“I’m hoping if the clinician was aware of an individual at high risk of conversion to SPMS on interferon beta or glatiramer acetate that would make the clinician think about escalating the treatment to more potent therapy.”


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