Dr Jan Hillert on the Misclassification of SPMS as RRMS

Many patients with secondary progressive multiple sclerosis (SPMS) are actually being misclassified and treated as if they have relapsing-remitting MS (RRMS), which can have an impact on research, treatment, and health care planning, said Jan Hillert, MD, PhD, professor and senior physician at Karolinska Institutet.

Why do you think misclassification of relapsing-remitting MS as secondary progressive MS occurs? And how can that misclassification be avoided?

So, the background is that we've seen that the proportion of people supposedly having SPMS varies between different data sources, whether different materials from different countries different MS registries. And of course, we wonder why that is. Is that because patients with SPMS are neglected by MS care? The reason [is] because they are, they may actually be, misclassified.

And therefore, now that we are starting to get to treatments that are specifically approved for SPMS it becomes more important for us to know: what is the proportion these days of SPMS? And I mean, this is important for us, as physicians to plan our treatments to plan our health care, plan economically, possibly, depending on your system. So, it became really important to know.

And that's why we thought that an objective algorithm that would designate patients into RRMS or SPMS would be a useful thing, at least in research, and also in health care planning, Potentially, also in the clinical situation, depending on how you can use it.

What is the algorithm and how can providers and clinicians use it to make sure that they're not misclassifying patients with MS?

So, the idea though, and there have been several different ways of trying to find the best way to assign RRMS and SPMS. But a general problem with those is that they require quite a bit of information, most typically several points of EDSS [Expanded Disability Status Scale] measurement. Whereas in practical life, as well as in many databases, you have the scarcity of EDSS points.

So, we wanted to set up something simple that required just age and 1 EDSS. And if we could find out an algorithm that could actually do a good job based only on those 2 variables, then that would be great. And that's what we set out to do.

And that, in the end, appeared to be possible. We applied some machine learning techniques to categorize patients into age groups and EDSS groups. And then we came up with the best model, we prune the model to bring it down to something more practical, and then that ended up in this algorithm that we now have as this decision tree classification. And it's not yet published, I should say it's submitted, like to be published pretty soon.