Data-Driven Categorization of Patients With Multiple Sclerosis: Daniel Ontaneda, MD, PhD

“The purpose of our project was to say, ‘Let’s not use these old classification schemes based on 1 arbitrary method, but let’s use as much quantitative data as we can to try to derive different categories of multiple sclerosis. And rather than us defining what those are, let’s just let the data tell us how our patients are grouped differently.’”

Individuals with multiple sclerosis (MS) are often categorized based on their disease status, with the classic categorizations falling under one of the following descriptions: relapsing-remitting MS, secondary progressive MS, and primary progressive MS. All of these, Daniel Ontaneda, MD, PhD, points out, are derived from how these individuals are accruing disability—either through incomplete recovery from relapses or gradually without any influence of disease relapse.

Ontaneda, among others in the field, has often noted that these designations do not provide the complete picture. Every patient is unique in some sense, and as expected, not every individual will fit so neatly into the aforementioned categorizations. At the 2021 American Academy of Neurology (AAN) Annual Meeting, April 17-22, Ontaneda, who is associate professor of Neurology at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a staff member at the Cleveland Clinic Neurological Institute’s Mellen Center for Multiple Sclerosis, presented data from an assessment that he and colleagues conducted attempting to take a more data-driven approach to this stratification.

In this interview with NeurologyLive, he shared his perspective on those findings, offered some context as to why these categorizations might be used, and clarified by the current methods of classification might not offer the best clinical insight.

For more coverage of AAN 2021, click here.

REFERENCE
Ontaneda D, Qu J, McGinley MP, et al. A Data Driven Approach to Define Disease Course Categories in Multiple Sclerosis. Presented at American Academy of Neurology Annual Meeting; April 17-22. Abstract 1115.