Could big data, combined with a biomarker, provide beneficial information for physicians treating multiple sclerosis?
Thor Ameri Chalmer, MD
In multiple sclerosis (MS) treatment, the one thing that is not lacking is the number of treatment options. In fact, physicians treating MS have been fortunate enough to count their arsenal in double-digits. However, this has sparked a need for help in treatment decisions.
With a number of efficacious disease-modifying therapies (DMTs), it has become a challenge to determine which option would be most effective for which patient. Thus far, the search to this answer has been pointed toward the discovery of a biomarker, with neurofilament light currently leading the way as the top candidate. Although, with the amount of high-quality data being collected in this field, an argument has been made for incorporating this information into that decision process.
Thor Ameri Chalmer, MD, who works with the Danish Multiple Sclerosis Registry at Rigshospitalet, in Denmark, believes this could be the case. In a recent observational study, he and colleagues used real-world data to assess whether or not it was more effective for patients to switch from one DMT to another of equal efficacy, or a higher-efficacy option, after a relapse.
To gain some of his insight from working with the registry, NeurologyLive
sat down with him to find out more about the study’s findings.
NeurologyLive: What work are you and your colleagues doing at the Danish MS Registry?
Thor Ameri Chalmer, MD:
It's a nationwide registry, so all the MS clinics in Denmark are coming together, collecting data for every patient in Denmark who has a diagnosis of MS. They have been collecting data since, actually, all the way back to 1948, and from when the first disease-modifying therapy was approved in Denmark, in 1996. We’ve collected, prospectively, at each visit, information about what kind of drug you’re taking, have you had any adverse effects, and you current EDSS score, and how many relapses did you have since last time.
How did this study utilize that information, and what were the findings?
The study is based on data from the Danish MS Registry, and we looked specifically at patients who started on what we call a moderately effective DMT. We looked at those on their first ever-prescription of a DMT who experienced a disease breakthrough—whether they had a relapse or in other ways experienced disease breakthrough—when a physician has to choose whether to switch to another a moderately effective DMT or to escalate to a highly-effective DMT.
We found all the patients who did that, within certain inclusion criteria, and then we compared them over time. How well did the treatment prevent further relapses, how well did the treatment prevents an EDSS worsening or actually give improvement?
The main results from this study were based on relapses, and we found that the group that escalated to a highly-effective DMT (fingolimod or natalizumab) had 33% lower relapse rate ratio in the years after the switch. Just like in the randomized clinical trials, we saw a clear reduction in relapse activity, but, actually, we didn't see any clear differences in EDSS worsening or improvement. The tendency was that the highly-effective DMT group did better, but it was not statistically significant. We couldn't say that it's an absolute difference between the two groups.
Do the findings help inform one side of the induction vs. escalation discussion?
From my perspective, I think when patients experience disease breakthrough, especially a clinical relapse, it's a good time to escalate the patient. Whether you should escalate a patient who has a disease breakthrough by MRI—we couldn't include that in our study, so that's perhaps another discussion. But, on average, in our study at least, the escalation won't necessarily progress your EDSS compared with a switch to another moderately effective DMT.
Of course, this is an observational study, it’s not a randomized study, so the two groups are similar in the variables we can adjust for, but for the variables we couldn't adjust for—the unmeasured components—they could be different in some ways. Perhaps the patient who escalated had more severe relapses, had worse MRIs, and that could be the reason why we didn't see any difference. Perhaps there is a benefit in escalation, in terms of EDSS worsening and improvement, but we couldn't find it in a real-world study. Then again, perhaps the EDSS worsening is just related to the relapses. If you make sure that the patient doesn't have as many relapses that, in time, also makes sure that they don't progress as much in their disease. In my opinion, at least, patients who experienced clinical relapse should escalate.
In terms of the other hot topics in MS, what’s been the biggest step the field has made, in your experience?
The introduction of all these new therapies is, in general, a good thing for patients. If one treatment doesn't work, they can switch to another treatment and perhaps that works better. For me personally, I'm a scientist working with register-based data, and I think it's really good that we now recognize that the results we find in randomized clinical trials on a selected group of people—designed to show a benefit, of course, for the treatment—but when you put that into a population where they have comorbidities, and they don't take the medicine as they’re supposed to and so on, if you still can find an effect in that population, that must really mean that the treatment actually is effective. What I'm trying to say is that I think it's a good thing that the registry data and observational studies are also used as evidence today, compared with perhaps 10 years ago.
What spurred this move toward incorporating data more efficiently, and what could the impact be?
I'm not sure exactly why, and that's a tendency not just in MS and not just in medicine, but everywhere. If you really have big data, you can use it to say something not just about the group, but more specifically, about the male between 20 and 25, who lives in this area. We can predict more specifically for the individual patient. Now, we have so many treatments, so what treatment do we need to choose for this patient, and for this patient? Perhaps, these big data sets can help us to see more precisely what treatment to choose for the specific patient.
If you could find a biomarker that said exactly what specific treatment to use, then the registries would be less and less important. But even better would be to combine the information from the registries with a biomarker, so you can predict even more precisely what effect you could expect for this treatment for this patient. Perhaps some combination of the two is even better than focusing on just at the biomarker.
Transcript edited for clarity
Ameri Chalmer T. Treatment escalation leads to fewer relapses compared with switching to another moderately effective therapy. Presented at ECTRIMS 2018; October 10-12, 2018; Berlin, Germany. onlinelibrary.ectrims-congress.eu/ectrims/2018/ectrims-2018/232016/thor.ameri.chalmer.treatment.escalation.leads.to.fewer.relapses.compared.with.html.