DMT will you switch to? #ClinicSpeak
“Data-mining is becoming
increasingly ubiquitous and is being used by health economists to assess how
healthcare interventions are performing in real-life and to make comparisons
between drugs. The study below compares MSers who have failed and interferon and
are then switched to either fingolimod or GA. Surprise, surprise those switched
to fingolimod were ~ 60% less likely to have a relapse compared to those
switched to GA. This tells us that fingolimod is a better DMT for treating
interferon failures than GA and would support the relative efficacy results of
the phase three studies of these two compounds.”
high quality in the sense that it is not from a randomised controlled trial.
One could argue that some systematic factor may be at play that determines who
is switched to GA and this factor could explain factor the differences in
efficacy between the two groups of MSers. This is what we call a bias and is
the main reason we do randomised trials. Randomisation ensures, at least most
of the time, that baseline characteristics or biases are neutralised by being equally
distributed between the arms of the study. The other factor that needs to be
kept in mind is that this study was funded and performed by Novartis, the
company who produces and markets fingolimod. Therefore it is not surprising that
this study was positive. Do you think Novartis would publish or release
negative data on fingolimod? These results could therefore represent the
phenomenon called publication bias; only positive studies get published. Publication
bias is a common problem and can only be counteracted by initiatives that
ensure all studies are registered and the investigators are forced to publish
their results. Saying this this will only work for clinical trials; I can’t see
this working for data-mining studies as the one below. If Novartis purchases
data from a healthcare company, or the NHS for that matter, who is going to know?
These deals are usually confidential. It is however reassuring that data from
the large MSBase project has been presented confirming these results; if you switch
from interferon to GA or from GA to interferon you are less likely to be
relapse free than if you switch to fingolimod.”
neurologists will take this data and assimilate it with other data and come to
the conclusion that fingolimod is more effective than GA.”
horses for courses. Some MSers who switch from an interferon to GA may respond
very well and have NEDA (no evident disease activity). I agree that this will
be a minority of cases and will be a lower proportion than those on fingolimod.
It is a great pity we can’t predict up front who will be a responder or
non-responder to GA so that we can make a better decision. At present we need
to monitor MSers on GA for 9 to 21 months, and beyond, to find out whether or
not they are responders or non-responders. I say 9 months as this is the time point
I used for rebaseling MSers on GA. Why 9 months? This is how long it takes for
GA to reach its maximal levels of efficacy on MRI. So if someone has a Gd-enhancing
lesion or lesion on their 9 month rebaselining scan I would recommend switching
them to a more effective treatment. If there are no Gd-enhancing lesions I
would rescan them in 12 months; the 21 month scan will then be compared to the
9 month scan and if there were any new T2 lesions or a Gd-enhancing lesion I
would switch them. Obviously an objective clinical relapse in this period would
trump the MRI activity.”
monitor and assess response to treatment. My big concern about this approach is
that if you have smouldering, or subclinical, disease activity 21 months is a
long time to have a shredder active in your brain. This is why we need to push
for better metrics to assess response and non-response to treatments at an
earlier stage. We need to be able to make the call within 6 months of starting
a treatment. To do this we need to validate other biomarkers in our treatment algorithms.
We were hoping to this as part of a large UK study, but the MRC decided not to
fund the programme. I think we have missed a big opportunity to improve the way
we treat MS in this country. At the moment there are no evidence-based
guidelines to inform us on how to sequence DMT treatments to get the best
outcomes for MSers.”
therapy which DMT are you going to switch to? This is not a trivial question
and depends on a large number of factors. I am hoping to develop a decision aid
to help you with this decision. Would you be interested in using it?”
Bergvall et al Relapse Rates in Patients with Multiple Sclerosis Interferon Switching from Glatiramer Acetate or to Fingolimod: A U.S. Claims Database Study. PLoS One 6 February 2014, 9 (2): e88472.
BACKGROUND: Approximately one-third of patients with multiple sclerosis (MS) are unresponsive to, or intolerant of, interferon (IFN) therapy, prompting a switch to other disease-modifying therapies. Clinical outcomes of switching therapy are unknown. This retrospective study assessed differences in relapse rates among patients with MS switching from IFN to fingolimod or glatiramer acetate (GA) in a real-world setting.
METHODS: US administrative claims data from the PharMetrics Plus™ database were used to identify patients with MS who switched from IFN to fingolimod or GA between October 1, 2010 and March 31, 2012. Patients were matched 1∶1 using propensity scores within strata (number of pre-index relapses) on demographic (e.g. age and gender) and disease (e.g. timing of pre-index relapse, comorbidities and symptoms) characteristics. A claims-based algorithm was used to identify relapses while patients were persistent with therapy over 360 days post-switch. Differences in both the probability of experiencing a relapse and the annualized relapse rate (ARR) while persistent with therapy were assessed.
RESULTS: The matched sample population contained 264 patients (n = 132 in each cohort). Before switching, 33.3% of patients in both cohorts had experienced at least one relapse. During the post-index persistence period, the proportion of patients with at least one relapse was lower in the fingolimod cohort (12.9%) than in the GA cohort (25.0%), and ARRs were lower with fingolimod (0.19) than with GA (0.51). Patients treated with fingolimod had a 59% lower probability of relapse (odds ratio, 0.41; 95% confidence interval [CI], 0.21-0.80; p = 0.0091) and 62% fewer relapses per year (rate ratio, 0.38; 95% CI, 0.21-0.68; p = 0.0013) compared with those treated with GA.
CONCLUSIONS: In a real-world setting, patients with MS who switched from IFNs to fingolimod were significantly less likely to experience relapses than those who switched to GA.