Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Two studies in one: A propensity-score-matched comparison of fingolimod versus interferons and glatiramer acetate using real-world data from the independent German studies, PANGAEA and PEARL

Two studies in one: A propensity-score-matched comparison of fingolimod versus interferons and glatiramer acetate using real-world data from the independent German studies, PANGAEA and PEARL

  • Jonathan Alsop, 
  • Jennie Medin, 
  • Christian Cornelissen, 
  • Stefan Viktor Vormfelde, 
  • Tjalf Ziemssen
PLOS
x

Abstract

Background

This study compared outcomes following fingolimod or BRACE treatments (beta-interferons/glatiramer acetate) in patients with active MS (≥ 1 relapse in the previous year) following previous BRACE treatment.

Methods and findings

Patients with active MS who previously received BRACE were identified from German prospective, observational studies, PANGAEA and PEARL. A novel methodology was developed to compare outcomes between propensity-score-matched cohorts (3:1 ratio) from the independent single-arm studies. Patients in PANGAEA (n = 1287) experienced 48% fewer relapses per year than those in PEARL (n = 429; annualized relapse rate ratio: 0.52; p < 0.001). The risk of 3-month or 6-month confirmed disability progression (CDP) was reduced in PANGAEA versus PEARL (3-month: 37% reduction; hazard ratio [HR], 0.63; p < 0.001; 6-month: 47% reduction; HR, 0.53; p < 0.001). A higher proportion of patients in PANGAEA (n = 1234) than PEARL (n = 401) were free from relapses and 3-month (65.7% vs 38.7%; p < 0.001) or 6-month (68.2% vs 39.2%; p < 0.001) CDP. The probability of confirmed disability improvement was higher in PANGAEA (n = 1163) than PEARL (n = 372; 3-month: 175% increase; HR, 2.75; p < 0.001; 6-month: 126% increase; HR, 2.26; p < 0.001). Patients in PANGAEA (n = 149) were less likely than those in PEARL (n = 307) to have taken sick leave (proportion with 0 days off work: 62.4% vs 44.6%; p = 0.0005). For change in disease severity from baseline (assessed by clinicians using the Clinical Global Impressions scale; PANGAEA, n = 1207; PEARL, n = 427), a larger proportion of patients had subjective improvement and a smaller proportion had worsening status in PANGAEA than PEARL (improvement: 28.2% vs 15.2%; worsening: 16.4% vs 30.4%; p < 0.0001).

Conclusions

Fingolimod appears to be more effective than BRACE in improving clinical and physician-/patient-reported outcomes in individuals with active MS.

Introduction

Multiple sclerosis (MS) is a chronic, inflammatory, degenerative disease of the central nervous system [1]. It is a leading cause of disability in young and middle-aged people, and affects approximately 2.3 million individuals worldwide [2]. Most patients (80–85%) present with the relapsing–remitting form of MS (RRMS), which is characterized by clearly defined symptomatic attacks (relapses) followed by periods of remission [3]. Loss of function in RRMS is caused by two types of damage. Widespread diffuse damage starts early in the disease and often goes unnoticed [47]. It is associated with progressive reduction in brain volume and accumulated loss of physical and cognitive function [47]; brain volume loss appears to be an important predictor of future disability [7]. The other type of damage is focal damage (distinct inflammatory lesions detected by magnetic resonance imaging [MRI]). Focal damage is associated with relapses, which are primary clinical manifestations of RRMS, that are followed by stretches of full or partial recovery [1, 5]. Incomplete recovery from relapses can lead to an accumulation of disability in patients with RRMS [1]. Patients with this form of MS initially experience relapses at a mean frequency of one per year, with wide inter-individual variation [1]. The frequency of relapses typically decreases over time [1].

Treatments for MS traditionally aim to modify the disease by reducing the number of relapses and delaying the progression of disability [8]. Glatiramer acetate (GA) and beta-interferons (IFN beta) collectively comprise the BRACE treatments (Betaseron®, Rebif®, Avonex®, Copaxone®, Extavia®), which are widely approved as disease-modifying therapies (DMTs) for MS. For many patients, however, these injectable therapies have suboptimal tolerability profiles and are only partially effective [8, 9]. Approximately one-quarter of patients with RRMS receiving BRACE therapies continue to experience relapse activity (estimates range from 18% to 46% for IFN beta [1014] and from 18% to 50% for GA [1417]). These patients with active MS usually continue to experience breakthrough disease unless their treatment is switched to an alternative agent with greater efficacy/effectiveness [14, 18].

Fingolimod, the first oral therapy approved for MS, was licensed in the EU in 2011 for the treatment of patients with highly active RRMS, which includes those who have high disease activity despite previously receiving at least one DMT [19]. Patients in this clinically relevant population may have received a BRACE therapy, the oral therapies dimethyl fumarate or teriflunomide, or the second-line infusible therapy natalizumab [19], which is associated with safety concerns owing to an increased risk of progressive multifocal leukoencephalopathy during long-term use [20, 21]. Fingolimod has been shown to reduce both focal and diffuse damage in patients with RRMS [2224]. In three phase 3 trials, fingolimod was found to have greater efficacy in reducing the number of relapses, slowing disability progression and improving MRI and brain atrophy outcomes compared with placebo (FTY720 Research Evaluating Effects of Daily Oral Therapy in Multiple Sclerosis [FREEDOMS] and FREEDOMS II) or intramuscular IFN beta-1a (Trial Assessing Injectable Interferon vs FTY720 Oral in Relapsing–remitting Multiple Sclerosis [TRANSFORMS]) [2224]. Post hoc analyses of data from FREEDOMS and FREEDOMS II have demonstrated the efficacy of fingolimod versus placebo in subgroups of patients with high disease activity despite previous DMT use [25, 26]. Similarly, a post hoc analysis of data from TRANSFORMS found a significant reduction in relapses for fingolimod versus IFN beta-1a in patients with high disease activity despite DMT use in the year before the study [27].

Data from phase 3 trials of fingolimod have been complemented by real-world evidence collected from administrative claims databases and registry studies [28]. Administrative claims database studies have shown that fingolimod was associated with significantly lower relapse rates than BRACE therapies in patients with MS whose treatment was switched from IFN beta 1a or 1b [29] and in those with a history of relapse [30]. The relative effectiveness of switching to fingolimod or BRACE therapies following disease activity (relapse or disability progression) has also been investigated in a global MSBase registry study [31]. After switching, the fingolimod group had a significantly lower mean relapse rate (0.31 vs 0.42; p = 0.009) and risk of disability progression (hazard ratio [HR], 0.53; 95% confidence interval [CI]: 0.31–0.91; p = 0.02) than the propensity-score-matched BRACE cohort [31].

To investigate prospectively the long-term safety and efficacy of fingolimod and BRACE therapies further in daily clinical practice in a large cohort of patients with active RRMS, two independent large, non-interventional, prospective, observational phase 4 single-arm studies have been initiated in Germany. Fingolimod is being assessed in the 5-year Post-authorization Non-interventional German Safety Study of Gilenya® in MS Patients (PANGAEA) [32]. BRACE therapies were assessed in the independent, recently completed 2-year Prospective Pharmacoeconomic Cohort Evaluation (PEARL) [33]. PANGAEA and PEARL were conducted in Germany to allow analyses of prospectively collected data for substantial cohorts of patients in a single healthcare system that provides a wide range of treatment options for MS [34]. Germany has a relatively high prevalence of MS, with an estimated 130 000 people being affected [35, 36]; this permitted enrolment of a large number of patients. Owing to the fact that patients were recruited from the same country and centers and during an overlapping time period, environmental and pharmacoeconomic conditions are likely to have been similar.

The aim of this study was to evaluate real-world clinical outcomes in propensity-score-matched patients with active MS receiving either fingolimod or BRACE therapies using data from the two independent, single-arm registry studies, PANGAEA and PEARL. To compare data from separate, non-interventional studies, a novel methodological approach was developed and used.

Methods

PANGAEA and PEARL study designs

Patients with RRMS receiving fingolimod 0.5 mg were recruited into PANGAEA between April 2011 (when market approval was received for fingolimod in the EU) and December 2013, with the planned observational period ending in December 2018 [32]. Patients with RRMS receiving BRACE therapies for at least 30 days were recruited into PEARL between September 2010 and March 2011, with the observational period ending in March 2013 [33]. In both studies, prospective visits were conducted at intervals of 3 months.

Patient selection and propensity score matching

Patients with RRMS from the PANGAEA and PEARL studies were included in this analysis if they met all the inclusion criteria, including providing informed consent, receiving BRACE therapies before participating in the study, having at least one relapse in the year before the study, and not having missing information on the number of relapses in the year before recruitment. PANGAEA patients who had previously participated in PEARL were excluded. To compare clinical outcomes between the independent PANGAEA and PEARL single-arm registry studies, a novel methodology was developed. Patients in the PANGAEA cohort were matched in a 3:1 ratio to patients in the PEARL cohort using a propensity-score-matching approach. All patients were included in the propensity-score analysis regardless of whether they dropped out at a later stage in the study. Propensity scores were calculated as the probability of enrolment in PANGAEA (vs PEARL) according to the following baseline predictors: number of relapses in the preceding year; time since diagnosis of MS; previous type of BRACE therapy (Betaseron, Rebif, Avonex, Copaxone and Extavia); Expanded Disability Status Scale (EDSS) score; age; and sex. Full details of the propensity score model are presented in Supporting Information (S1 Supporting information). Subgroups of matched patients in the PANGAEA and PEARL cohorts were identified according to the additional criterion of having at least 1 year of follow-up.

Study outcomes

Relapses, 3-month and 6-month confirmed disability progression (CDP), and 3-month and 6-month confirmed disability improvement were assessed using follow-up data from the PANGAEA and PEARL study records. Relapses were not defined according to any pre-specified criteria but were assessed according to the clinical judgement of physicians in the real world. CDP was assessed according to increases in EDSS score, with confirmation of the increase in disability at a visit in the absence of a relapse as follows: a 1.5-point increase from baseline in patients with a baseline EDSS score of 0; a 1-point increase from baseline in patients with baseline EDSS scores of 1–5.0; and a 0.5-point increase in patients with baseline EDSS scores of 5.5 or more. A 3-month CDP required that the initial EDSS score at onset of disability progression, the 3-month confirming EDSS score and all EDSS evaluations in between met the disability progression criteria. Time to 3-month CDP was calculated using a threshold of 76 days (the protocol allows a visit window of ± 14 days for any 3-month interval visits) as the lower end of the 3-month visit and the corresponding threshold for 6-month CDP was 166 days. Confirmed disability improvement was defined as a decrease in EDSS score of at least 1 point for all patients regardless of baseline EDSS score, with the decrease in disability confirmed in a similar manner to that used for CDP. The number of relapses was assessed according to relapse dates. Relapses separated by fewer than 28 days were counted as a single relapse.

At each study visit, patients also reported the number of days of sick leave during the preceding 3 months. Days of sick leave were averaged over all visits during a patient’s participation in the study. Patient disease severity was assessed by clinicians using the Clinical Global Impressions (CGI) scale [37], which was administered at baseline and at months 12, 18 and 24. The CGI scale requires clinicians to rate the severity of a patient’s illness, relative to the clinician’s experience of patients with the same diagnosis, on a scale from 1 (patient is not at all sick) to 7 (patient is extremely sick) [37].

Statistical analyses

Annualized relapse rates (ARRs) were analyzed using a negative binomial regression model. The model used the number of relapses as the response variable and the logarithm of time on study as an offset variable. The ARR ratio (patients treated with fingolimod vs those receiving BRACE) and its associated 95% CI and p-value were estimated from this model. Times to 3-month and 6-month CDP, and 3-month and 6-month confirmed disability improvement were estimated using a Kaplan–Meier approach, and differences were assessed using a log-rank test. HRs for the relative probabilities of CDP and confirmed disability improvement (fingolimod vs BRACE) were estimated using a Cox proportional hazards model adjusted for treatment. Patients were assessed for outcomes of interest until the point of drop out, at which point they were censored. In analysis of rates, only the period over which patients were part of the study were considered. In each analysis of ‘time-to’ outcomes, patients without the outcome event were censored at their last available visit. The proportions of patients in each cohort who were free from both relapses and disability progression were compared using a Fisher’s exact test. Similar analyses were performed for the subgroup of matched patients with at least 1 year of follow-up. To allow for any residual confounding, analyses were also conducted with adjustment for baseline covariates (age and sex) and pre-study clinical characteristics (pre-study relapses, time since diagnosis, baseline EDSS score and pre-study DMT). For sick leave and CGI analyses, data were only available for a subset of patients, and were analyzed in the two matched cohorts using a two-sided Jonckheere–Terpstra test.

Results

Study population and cohort characteristics

In total, 2255 and 587 patients met the inclusion criteria in the PANGAEA and PEARL cohorts, respectively. Baseline demographic and clinical characteristics are shown in Table 1. On average, compared with patients in PEARL, those in PANGAEA had a diagnosis of MS for longer (mean duration, 7.7 vs 5.8 years), had a slightly higher EDSS score (mean, 2.9 vs 2.4) and were more likely to have had multiple relapses in the previous year (56.7% vs 30.1%). After propensity score matching, 1287 and 429 patients were retained for further analysis in the PANGAEA and PEARL cohorts, respectively. There were no clinically significant differences between the propensity-score-matched cohorts (Table 1 and Supporting Information [S1 Supporting information]). Mean follow-up time was slightly shorter in the PANGAEA cohort than in the PEARL cohort (for both the matched and unmatched populations); however, total study time in patient-years was longer in the PANGAEA cohort than the PEARL cohort (for both the matched and unmatched populations) owing to differences in the size of the cohorts. A subgroup of matched patients with the additional criterion of having at least 1 year of follow-up data included 730 patients in PANGAEA and 325 in PEARL (S2 Table). The proportion of patients discontinuing the study during the follow-up period was comparable between the PANGAEA and PEARL cohorts in the unmatched (19.8% and 18.2%, respectively) and matched population (18.6% and 20.3%, respectively).

thumbnail
Table 1. Baseline demographic and clinical characteristics in the unmatched and propensity-score-matched PANGAEA and PEARL cohorts.

https://doi.org/10.1371/journal.pone.0173353.t001

Relapse rates in the PANGAEA and PEARL cohorts

In the overall unmatched PANGAEA and PEARL cohorts, ARRs were 0.41 and 0.62, respectively (p < 0.001). This trend was repeated in the propensity-score-matched cohorts, with the ARR being lower for patients in PANGAEA than for those in PEARL (0.36 vs 0.70; Fig 1). On average, propensity-score-matched patients in the PANGAEA cohort experienced 48% fewer relapses per year than those in the PEARL cohort (ARR ratio: 0.52; 95% CI: 0.43–0.61; p < 0.001; Fig 1). Similar results were observed for ARRs in the subgroup of propensity-score-matched patients with at least 1 year of follow-up (ARR ratio: 0.52; 95% CI: 0.43–0.64; p < 0.001) and in the propensity-score-matched cohorts adjusted for baseline covariates not included in propensity score matching (ARR ratio: 0.49; 95% CI: 0.42–0.58; p < 0.001). The time to first relapse was typically longer for patients in PANGAEA than for those in PEARL in propensity-score-matched analyses (Fig 2).

thumbnail
Fig 1. Unadjusted annualized relapse rates.

Unadjusted ARRs in propensity-score-matched patients from the PANGAEA (fingolimod) and PEARL (BRACE) cohorts. Unadjusted ARR ratios were calculated from a negative binomial regression model using PEARL as the reference cohort. ARR, annualized relapse rate; BRACE, Betaseron®, Rebif®, Avonex®, Copaxone®, Extavia® (beta interferons or glatiramer acetate); CI, confidence interval; PANGAEA, Post-authorization Non-interventional German Safety Study of Gilenya® in Multiple Sclerosis Patients; PEARL, Prospective Pharmacoeconomic Cohort Evaluation.

https://doi.org/10.1371/journal.pone.0173353.g001

thumbnail
Fig 2. Time to first relapse in propensity-score-matched patients from the PANGAEA (fingolimod) and PEARL (BRACE) cohorts.

Kaplan–Meier estimate (cumulative distribution function) showing time to first relapse in propensity-score-matched patients from the PANGAEA (fingolimod) and PEARL (BRACE) cohorts. BRACE, Betaseron®, Rebif®, Avonex®, Copaxone®, Extavia® (beta interferons or glatiramer acetate); PANGAEA, Post-authorization Non-interventional German Safety Study of Gilenya® in Multiple Sclerosis Patients; PEARL, Prospective Pharmacoeconomic Cohort Evaluation.

https://doi.org/10.1371/journal.pone.0173353.g002

Confirmed disability progression in the PANGAEA and PEARL cohorts

In the overall unmatched PANGAEA and PEARL cohorts, Kaplan–Meier estimates for the proportions of patients with 3-month CDP at the end of year 2 were 16.4% and 22.0%, respectively (log-rank test p = 0.008); proportions of patients with 6-month CDP were 11.1% and 19.1%, respectively (p < 0.001). In propensity-score-matched cohorts, the risk of 3-month CDP was significantly reduced in the PANGAEA cohort compared with the PEARL cohort (37% reduction; HR, 0.63; 95% CI: 0.48–0.83; p < 0.001; Fig 3A). The risk of 6-month CDP in the matched cohorts was also significantly reduced in PANGAEA compared with PEARL (47% reduction; HR, 0.53; 95% CI: 0.39–0.74; p < 0.001; Fig 3B). In the subgroup of propensity-score-matched patients with at least 1 year of follow-up, similar reductions were observed in the risk of both 3-month CDP (30% reduction; HR, 0.70; 95% CI: 0.52–0.94; p = 0.019) and 6-month CDP (39% reduction; HR, 0.61; 95% CI: 0.44–0.85; p = 0.004). Comparable results for the risk of both 3-month CDP (38% reduction; HR, 0.62; 95% CI: 0.47–0.82; p < 0.001) and 6-month CDP (47% reduction; HR, 0.53; 95% CI: 0.38–0.73; p < 0.001) were observed in the matched cohorts when analyses were adjusted for baseline covariates.

thumbnail
Fig 3.

Time to (a) 3-month and (b) 6-month confirmed disability progression, and (c) 3-month and (d) 6-month confirmed disability improvement in propensity-score-matched patients from the PANGAEA (fingolimod) and PEARL (BRACE) cohorts. Kaplan–Meier estimate (cumulative distribution function) showing time to cumulative disability progression and time to cumulative disability improvement in propensity-score-matched patients from the PANGAEA (fingolimod) and PEARL (BRACE) cohorts. Not all matched patients had available Expanded Disability Status Scale scores and therefore some individuals could not be included in these analyses. BRACE, Betaseron®, Rebif®, Avonex®, Copaxone®, Extavia® (beta interferons or glatiramer acetate); PANGAEA, Post-authorization Non-interventional German Safety Study of Gilenya® in Multiple Sclerosis Patients; PEARL, Prospective Pharmacoeconomic Cohort Evaluation.

https://doi.org/10.1371/journal.pone.0173353.g003

Among patients in the unmatched cohorts for whom data were available (PANGAEA, n = 2009; PEARL, n = 548), a significantly higher proportion in PANGAEA than in PEARL were free from both relapses and 3-month (65.3% vs 42.3%; p < 0.001) or 6-month CDP (67.4% vs 42.9%; p < 0.001). Similar results were seen in the propensity-score-matched PANGAEA and PEARL cohorts for the proportions of patients who were relapse-free and CDP-free (PANGAEA, n = 1234; PEARL, n = 401; relapse-free and 3-month CDP-free: 65.7% vs 38.7%; p < 0.001; relapse-free and 6-month CDP-free: 68.2% vs 39.2%; p < 0.001). Results were also consistent in the subgroup of propensity-score-matched patients in the PANGAEA and PEARL cohorts with at least 1 year of follow-up (PANGAEA, n = 720; PEARL, n = 325) for the proportions of patients who were free from relapses and 3-month (57.9% vs 39.1%; p < 0.001) or 6-month CDP (60.8% vs 39.4%; p < 0.001).

Confirmed disability improvement in the PANGAEA and PEARL cohorts

The proportions of patients in the overall unmatched PANGAEA and PEARL populations (PANGAEA, n = 1924; PEARL, n = 499) with 3-month confirmed disability improvement at the end of year 2 were 15.2% and 6.6%, respectively (log-rank test p < 0.001). Similar results were seen for 6-month confirmed disability improvement (11.5% vs 5.8%; p < 0.001). In the propensity-score-matched cohorts (PANGAEA, n = 1163; PEARL, n = 372), the probability of 3-month confirmed disability improvement was again significantly higher in PANGAEA than in PEARL (175% increase; HR, 2.75; 95% CI: 1.82–4.15; p < 0.001; Fig 3C). The probability of 6-month confirmed disability improvement was also significantly higher in PANGAEA than in PEARL for these matched cohorts (126% increase; HR, 2.26; 95% CI: 1.45–3.53; p < 0.001; Fig 3D). In the subgroup of propensity-score-matched patients with at least 1 year of follow-up (PANGAEA, n = 684; PEARL, n = 297), similar increases were observed in the probability of both 3-month (176% increase; HR, 2.76; 95% CI: 1.80–4.22; p < 0.001) and 6-month (136% increase; HR, 2.36; 95% CI: 1.50–3.69; p < 0.001) confirmed disability improvement. Adjustment for baseline covariates in the matched cohorts had little impact on analyses, with the probability of 3-month (168% increase; HR, 2.68; 95% CI: 1.75–4.12; p < 0.001) and 6-month (127% increase; HR, 2.27; 95% CI: 1.45–3.57; p < 0.001) confirmed disability improvement remaining significantly higher in PANGAEA than in PEARL.

Physician-/patient-reported outcomes in the PANGAEA and PEARL cohorts

Sick leave data were available for only a subset of the propensity-score-matched cohorts (PANGAEA, n = 149; PEARL, n = 307). The mean number of days off work sick during the preceding 3 months was lower in PANGAEA than in PEARL (4.2 vs 7.7 days), and patients in the PANGAEA cohort were significantly less likely than those in the PEARL cohort to have taken any sick leave (proportion with 0 days off sick: PANGAEA, 62.4%; PEARL, 44.6%; p = 0.0005; Fig 4). In the matched cohorts with CGI data (PANGAEA, n = 1207; PEARL, n = 427), analysis of the change in CGI score from baseline to the last available visit showed a higher proportion of patients with improvement in PANGAEA, and a smaller proportion with worsening status, than in PEARL (improvement: 28.2% vs 15.2%; worsening: 16.4% vs 30.4%; p < 0.0001). Similar results were seen for patients with 2 years of CGI data (PANGAEA, n = 389; PEARL, n = 249; improvement: 28.8% vs 16.1%; worsening: 17.5% vs 27.7%; p < 0.0001).

thumbnail
Fig 4. Proportions of patients with different numbers of days off work sick in 3 months.

The proportions of patients were calculated in the propensity-score-matched PANGAEA (n = 149) and PEARL (n = 307) cohorts. PANGAEA, Post-authorization Non-interventional German Safety Study of Gilenya® in Multiple Sclerosis Patients; PEARL, Prospective Pharmacoeconomic Cohort Evaluation.

https://doi.org/10.1371/journal.pone.0173353.g004

Discussion

This long-term, real-world, observational study developed and used a novel methodological approach to compare important outcomes of disease among patients with active MS from two separate, single-arm, prospective, observational studies: PANGAEA, in which patients received fingolimod, and PEARL, in which they received BRACE therapy. Treatments for MS aim to reduce disease activity [8], but in some patients this remains high despite BRACE therapy, prompting a switch to an alternative treatment [3840]. The results of the present study focus on patients with active disease who are receiving BRACE therapy, the indication for which fingolimod is approved in the EU [19]. This study found that in clinical practice, switching to fingolimod treatment is more effective than continuing BRACE therapy in improving a number of clinical and physician-/patient-reported outcomes, including relapse rates, disability progression and improvement, and measures of productivity in patients with active MS.

In our propensity-score-matched cohorts, we observed a 48% reduction in the number of relapses in patients receiving fingolimod in PANGAEA compared with those continuing to be treated with BRACE in PEARL. The relative reduction in ARR observed in PANGAEA and PEARL was consistent with the findings of a post hoc analysis of data from the phase 3 TRANSFORMS [27]. In patients with high disease activity despite receiving a DMT in the year before entering TRANSFORMS, fingolimod reduced the ARR by 50% versus IFN beta [27]. In addition, the observed relative reduction in relapses in our study is consistent with that seen in recent real-world evidence studies [29, 30, 41]. In a US administrative claims database study, a 50% reduction in ARR was found in patients with a history of relapse treated with fingolimod, compared with those receiving BRACE therapies [30]. It is notable that similar ARR reductions were seen despite differences in baseline demographic and clinical characteristics between the US database study and the present analysis [30]. An additional claims database study found a 62% reduction in ARR in patients switching therapy from IFN beta to fingolimod compared with those switching to glatiramer acetate [29]. The observations in PANGAEA and PEARL are also similar to those of a global MSBase registry study [31]. Patients switching from BRACE to fingolimod had a lower mean ARR (0.31 vs 0.42) and a 26% decrease in hazard of first on-treatment relapse compared with those switching from one BRACE therapy to another [31]. On average, patients in the MSBase analysis had a similar duration and severity of disease to those in the present study [31]. In summary, although the magnitude of ARR reduction varies among studies, reductions in relapse rates for fingolimod compared with BRACE are seen both in the present study and across phase 3 trial data and real-world evidence studies, despite differences in patient baseline characteristics and study types [27, 2931, 41].

In our study, there was a 37% reduced risk of 3-month CDP and a 47% reduced risk of 6-month CDP in the PANGAEA cohort versus the PEARL cohort. Furthermore, patients in PANGAEA were more likely to be free from both relapses and CDP than patients in PEARL. A recent analysis of the global MSBase registry has identified a similar reduction in the risk of CDP [42]. Patients whose therapy was switched from BRACE to fingolimod in the MSBase study had a 47% reduced risk of 3-month CDP compared with those switching from one BRACE therapy to another [31]. The results of the present study, conducted in a single healthcare system using data collected prospectively in two independent studies according to a predefined protocol, confirm the reduction in risk of CDP with fingolimod in a larger sample of patients than that used in the MSBase study. In addition to the observed reduction in the risk of CDP, the relative probability of experiencing 3-month and 6-month confirmed disability improvement was increased by 175% and 126%, respectively, for PANGAEA versus PEARL. These findings are consistent with those of post hoc analyses of data from FREEDOMS and TRANSFORMS, which show that fingolimod may be associated with confirmed improvement in disability [42].

Relapses and disability progression are associated with significant reductions in quality of life, and can lead to loss of employment [4345]. Advanced states of disability and relapses are associated with increased use of healthcare resources, and increased direct costs and indirect costs, such as costs associated with sick leave and informal care [4446]. In our analysis, patients with active disease who switched to fingolimod in PANGAEA were less likely than those who continued on BRACE therapy in PEARL to have taken time off work in the preceding 3 months, and were more likely to have improved CGI scores. By reducing the rate of relapses and disability progression, fingolimod may improve patients’ quality of life and productivity, and reduce the burden of MS [47]. Many patients with MS who experience relapses despite receiving a previous DMT would benefit from a therapy with a drug with these qualities; our findings therefore have clinical relevance. Other treatment options for this group include natalizumab. In real-world studies comparing fingolimod and natalizumab, results are inconclusive regarding the comparative effectiveness of these treatments: some studies report that natalizumab and fingolimod have similar effectiveness [48, 49] and other studies suggest that natalizumab is more effective than fingolimod.[5052] Reasons for differences in trends across studies may be attributed to differences in study design, patient characteristics or data sources across the publications.

A strength of the PANGAEA and PEARL registries is that they contain high-quality data owing to the use of external validation checks at the time of, and following, data entry. More generally, prospective, observational trials allow for the assessment of long-term, real-world clinical outcomes in patient populations that can be regarded as being more representative of clinical practice than those enrolled in randomized controlled trials [28]. The use of this methodology, particularly when supported by specific software tools such as the MS management system, MSDS 3D [53], is effective in terms of both time and cost because the data assessed are collected from visits scheduled as part of local clinical practice [54, 55]. As part of this study, a novel methodology was developed to compare treatments between propensity-score-matched cohorts of patients from separate, single-arm, non-interventional studies. This approach to generating high-quality real-world data from independent studies probably has more general applications for measuring treatment benefit in clinical practice, beyond the field of MS, and also expands the data sources that can be used for real-world evidence generation.

One of the general limitations of phase 4 observational studies is the difficulty in determining whether patients have used DMTs as prescribed. Therefore, DMT exposure is presumed and, in the case of injectable DMTs, which have been shown to be associated with lower persistence and adherence than oral DMTs [56], may be overestimated. The results of the analyses in the present study are, however, consistent with the results of the fingolimod phase 3 trials. Another limitation of observational studies is the lack of randomization and blinding, which can lead to confounding and bias between the patient cohorts. To compensate for differences between study cohorts in the present study, we used propensity score matching to select comparable patient cohorts for this analysis. While some caution is needed in the interpretation of results arising from this type of statistical approach, propensity score matching is a relatively robust technique to reduce the chances of differences in outcomes being attributable to measured covariates. Nevertheless, propensity score matching cannot account for other possible important but unknown or unmeasured covariates, such as MRI lesion activity. Fingolimod treatment resulted in more favorable relapse and CDP outcomes, both in propensity-score-matched cohorts and in additional analyses adjusted for residual confounding. It should be noted that both PANGAEA and PEARL were conducted only in Germany, which allows a better comparison of the data than if the studies had been conducted in different countries. At present, patients with MS in the German healthcare system have a wide range of available treatment options [34, 57], and assignment to receive fingolimod or BRACE therapies is unlikely to be confounded by potential treatment access issues. The results for these observational study populations are likely to be broadly applicable to the overall MS population in Germany; however, caution may be needed in generalizing the results of these studies to other settings in which patients may have had different treatment in the earlier stages of MS.

The results of this study suggest that in the real world, long-term fingolimod treatment is more effective than injectable BRACE therapies in patients with MS with disease activity despite previous DMT use. This study also developed a novel methodology to compare data from two separate, single-arm, prospective, non-interventional studies, which is likely to have more general applications for generating data for other real-world studies.

Supporting information

S1 Table. Inclusion and exclusion criteria for propensity score matching.

https://doi.org/10.1371/journal.pone.0173353.s002

(PDF)

S3 Table. Multivariable logistic regression of the probability of fingolimod treatment (vs BRACE).

https://doi.org/10.1371/journal.pone.0173353.s004

(PDF)

S1 Fig. Distribution of propensity scores before and after matching.

https://doi.org/10.1371/journal.pone.0173353.s005

(EPS)

Acknowledgments

The authors take full responsibility for the content of the paper. The authors thank Dr Colin Glen, Dr Anne-Marie Couto and Dr Gemma Carter of Oxford PharmaGenesis, Oxford, UK for medical writing support, editorial assistance and collation and incorporation of comments from all authors (funded by Novartis Pharma AG, Basel, Switzerland).

Author Contributions

  1. Conceptualization: JA JM CC SVV TZ.
  2. Data curation: CC SVV TZ.
  3. Formal analysis: JA JM CC SVV TZ.
  4. Funding acquisition: JM CC SVV.
  5. Investigation: TZ CC SVV.
  6. Methodology: JA JM CC SVV TZ.
  7. Project administration: CC SVV.
  8. Resources: CC SVV TZ.
  9. Software: JA.
  10. Supervision: TZ.
  11. Validation: JA JM CC SVV TZ.
  12. Visualization: JA.
  13. Writing – review & editing: JA JM CC SVV TZ.

References

  1. 1. Compston A, Coles A. Multiple sclerosis. Lancet. 2002;359(9313):1221–31. Epub 2002/04/17. pmid:11955556
  2. 2. National MS Society. MS prevalence. Available from: http://www.nationalmssociety.org/About-the-Society/MS-Prevalence.
  3. 3. Compston A, Coles A. Multiple sclerosis. Lancet. 2008;372(9648):1502–17. pmid:18970977
  4. 4. De Stefano N, Airas L, Grigoriadis N, Mattle HP, O'Riordan J, Oreja-Guevara C, et al. Clinical relevance of brain volume measures in multiple sclerosis. CNS drugs. 2014;28(2):147–56. Epub 2014/01/22. pmid:24446248
  5. 5. Kutzelnigg A, Lassmann H. Pathology of multiple sclerosis and related inflammatory demyelinating diseases. Handbook of clinical neurology. 2014;122:15–58. pmid:24507512
  6. 6. Filippi M, Rocca MA. MRI evidence for multiple sclerosis as a diffuse disease of the central nervous system. Journal of neurology. 2005;252 (Suppl 5):v16–24.
  7. 7. Bermel RA, Bakshi R. The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurol. 2006;5(2):158–70. pmid:16426992
  8. 8. Derwenskus J. Current disease-modifying treatment of multiple sclerosis. Mt Sinai J Med. 2011;78(2):161–75. Epub 2011/03/23. pmid:21425262
  9. 9. Haase R, Kullmann JS, Ziemssen T. Therapy satisfaction and adherence in patients with relapsing–remitting multiple sclerosis: the THEPA-MS survey. Ther Adv Neurol Disord. 2016;9:250–63. pmid:27366231
  10. 10. Pereira VC, Malfetano FR, Meira ID, Souza LF, Liem AM, Maiolino A, et al. Clinical response to interferon beta and glatiramer acetate in multiple sclerosis patients: a Brazilian cohort. Arquivos de neuro-psiquiatria. 2012;70(10):774–9. pmid:23060103
  11. 11. Portaccio E, Zipoli V, Siracusa G, Sorbi S, Amato MP. Response to interferon-beta therapy in relapsing-remitting multiple sclerosis: a comparison of different clinical criteria. Multiple sclerosis. 2006;12(3):281–86. pmid:16764340
  12. 12. Prosperini L, Capobianco M, Gianni C. Identifying responders and non-responders to interferon therapy in multiple sclerosis. Degener Neurol Neuromuscul Dis. 2014;4:75–84.
  13. 13. Rice GP, Incorvaia B, Munari L, Ebers G, Polman C, D'Amico R, et al. Interferon in relapsing-remitting multiple sclerosis. The Cochrane database of systematic reviews. 2001;(4):CD002002. pmid:11687131
  14. 14. Castillo-Trivino T, Mowry EM, Gajofatto A, Chabas D, Crabtree-Hartman E, Cree BA, et al. Switching multiple sclerosis patients with breakthrough disease to second-line therapy. PloS one. 2011;6(2):e16664. PubMed Central PMCID: PMC3033401. pmid:21304907
  15. 15. Fusco C, Andreone V, Coppola G, Luongo V, Guerini F, Pace E, et al. HLA-DRB1*1501 and response to copolymer-1 therapy in relapsing-remitting multiple sclerosis. Neurology. 2001;57(11):1976–79. pmid:11739812
  16. 16. Ziemssen T, Calabrese P, Penner IK, Apfel R. QualiCOP: real-world effectiveness, tolerability, and quality of life in patients with relapsing-remitting multiple sclerosis treated with glatiramer acetate, treatment-naive patients, and previously treated patients. Journal of neurology. 2016;263(4):784–91. pmid:26914926
  17. 17. Ziemssen T, Bajenaru OA, Carra A, de Klippel N, de Sa JC, Edland A, et al. A 2-year observational study of patients with relapsing-remitting multiple sclerosis converting to glatiramer acetate from other disease-modifying therapies: the COPTIMIZE trial. Journal of neurology. 2014;261(11):2101–11. PubMed Central PMCID: PMCPMC4221652. pmid:25119836
  18. 18. Ziemssen T, Derfuss T, de Stefano N, Giovannoni G, Palavra F, Tomic D, et al. Optimizing treatment success in multiple sclerosis. Journal of neurology. 2015;263:1053–65. pmid:26705122
  19. 19. European Medicines Agency. Gilenya EPAR Product Information. 2014. Available from: http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-_Product_Information/human/002202/WC500104528.pdf.
  20. 20. Carruthers RL, Rotstein DL, Healy BC, Chitnis T, Weiner HL, Buckle GJ. An observational comparison of natalizumab vs. fingolimod using JCV serology to determine therapy. Multiple sclerosis. 2014;20(10):1381–90. pmid:24852928
  21. 21. European Medicines Agency. Tysabri EPAR Product Information. 2013. Available from: http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-_Product_Information/human/000603/WC500044686.pdf.
  22. 22. Calabresi PA, Radue EW, Goodin D, Jeffery D, Rammohan KW, Reder AT, et al. Safety and efficacy of fingolimod in patients with relapsing-remitting multiple sclerosis (FREEDOMS II): a double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Neurol. 2014;13(6):545–56. Epub 2014/04/02. pmid:24685276
  23. 23. Cohen JA, Barkhof F, Comi G, Hartung HP, Khatri BO, Montalban X, et al. Oral fingolimod or intramuscular interferon for relapsing multiple sclerosis. N Engl J Med. 2010;362(5):402–15. pmid:20089954
  24. 24. Kappos L, Radue EW, O'Connor P, Polman C, Hohlfeld R, Calabresi P, et al. A placebo-controlled trial of oral fingolimod in relapsing multiple sclerosis. N Engl J Med. 2010;362(5):387–401. pmid:20089952
  25. 25. Devonshire V, Havrdova E, Radue EW, O'Connor P, Zhang-Auberson L, Agoropoulou C, et al. Relapse and disability outcomes in patients with multiple sclerosis treated with fingolimod: subgroup analyses of the double-blind, randomised, placebo-controlled FREEDOMS study. Lancet Neurol. 2012;11(5):420–8. pmid:22494956
  26. 26. Bergvall N, Tomic D, Sfikas N, Kappos L. Impact of fingolimod on achieving no evidence of disease activity in pretreated patients with high disease activity in FREEDOMS and FREEDOMS II. Multiple sclerosis. 2014;20 (Suppl 1):117–18.
  27. 27. Cohen JA, Barkhof F, Comi G, Izquierdo G, Khatri B, Montalban X, et al. Fingolimod versus intramuscular interferon in patient subgroups from TRANSFORMS. Journal of neurology. 2013;260(8):2023–32. PubMed Central PMCID: PMC3737385. pmid:23632946
  28. 28. Ziemssen T, Hillert J, Butzkueven H. The importance of collecting structured clinical information on multiple scelorosis. BMC Med. 14:81. pmid:27246898
  29. 29. Bergvall N, Makin C, Lahoz R, Agashivala N, Pradhan A, Capkun G, et al. Relapse rates in patients with multiple sclerosis switching from interferon to fingolimod or glatiramer acetate: a US claims database study. PloS one. 2014;9(2):e88472. PubMed Central PMCID: PMC3916439. pmid:24516663
  30. 30. Bergvall N, Makin C, Lahoz R, Agashivala N, Pradhan A, Capkun G, et al. Comparative effectiveness of fingolimod versus interferons or glatiramer acetate for relapse rates in multiple sclerosis: a retrospective US claims database analysis. Curr Med Res Opin. 2013;29(12):1647–56. Epub 2013/09/26. pmid:24059944
  31. 31. He A, Spelman T, Jokubaitis V, Havrdova E, Horakova D, Trojano M, et al. Comparison of switch to fingolimod or interferon beta/glatiramer acetate in active multiple sclerosis. JAMA Neurol. 2015;72(4):405–13. pmid:25665031
  32. 32. Ziemssen T, Kern R, Cornelissen C. The PANGAEA study design–a prospective, multicenter, non-interventional, long-term study on fingolimod for the treatment of multiple sclerosis in daily practice. BMC Neurol. 2015;15:93. Epub 2015/06/19. PubMed Central PMCID: PMCPMC4472406. pmid:26084334
  33. 33. Vormfelde SV, Ortler S, Ziemssen T. The PEARL study design–a non-interventional study on clinical and pharmacoeconomic aspects of multiple sclerosis therapy with disease-modifying treatments in Germany JMIR Res Protoc. 2016 5(1):e23. pmid:26846334
  34. 34. European MS Platform. MS Barometer 2013. Available from: http://www.emsp.org/tmp/ms_barometer_2013_May_2014.pdf.
  35. 35. Multiple Sclerosis International Federation. Atlas of MS database. 2013. Available from: http://www.atlasofms.org/.
  36. 36. Multiple Sclerosis International Federation. Atlas of MS 2013. Mapping multiple sclerosis around the world. Available from: http://www.msif.org/wp-content/uploads/2014/09/Atlas-of-MS.pdf.
  37. 37. Busner J, Targum SD. The Clinical Global Impressions scale: applying a research tool in clinical practice. Psychiatry (Edgmont). 2007;4(7):28–37. PubMed Central PMCID: PMCPMC2880930.
  38. 38. Portaccio E, Zipoli V, Siracusa G, Sorbi S, Amato MP. Long-term adherence to interferon beta therapy in relapsing-remitting multiple sclerosis. Eur Neurol. 2008;59(3–4):131–5. Epub 2007/12/07. pmid:18057899
  39. 39. Zwibel HL. Glatiramer acetate in treatment-naive and prior interferon-beta-1b-treated multiple sclerosis patients. Acta Neurol Scand. 2006;113(6):378–86. Epub 2006/05/06. pmid:16674604
  40. 40. Ziemssen T, De Stefano N, Pia Sormani M, Van Wijmeersch B, Wiendl H, Kieseier BC. Optimizing therapy early in multiple sclerosis: an evidence-based view. Mult Scler Relat Disord. 2015;4(5):460–9. pmid:26346796
  41. 41. Spelman T, Bergvall N, Tomic D, He A, Kalincik T, Trojano M, et al. Real-world comparative effectiveness of fingolimod and interferon/glatiramer therapies in a switch population using propensity-matched data from MSBase. Multiple sclerosis. 2013;19 (Suppl 11):514.
  42. 42. Bergvall N, Sfikas N, Chin P, Tomic D, Cutter G, Kappos L. Effect of fingolimod on confirmed improvement in disability in patients with multiple sclerosis: post hoc analyses of FREEDOMS and TRANSFORMS. Neurology. 2013;80 (Suppl 7):P07.108.
  43. 43. Orme M, Kerrigan J, Tyas D, Russell N, Nixon R. The effect of disease, functional status, and relapses on the utility of people with multiple sclerosis in the UK. Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2007;10(1):54–60.
  44. 44. Kobelt G, Berg J, Lindgren P, Elias WG, Flachenecker P, Freidel M, et al. Costs and quality of life of multiple sclerosis in Germany. Eur J Health Econ. 2006;7 (Suppl 2):S34–44.
  45. 45. Kobelt G, Berg J, Lindgren P, Jonsson B. Costs and quality of life in multiple sclerosis in Europe: method of assessment and analysis. Eur J Health Econ. 2006;7 (Suppl 2):S5–13.
  46. 46. Sobocki P, Pugliatti M, Lauer K, Kobelt G. Estimation of the cost of MS in Europe: extrapolations from a multinational cost study. Multiple sclerosis. 2007;13(8):1054–64. pmid:17623729
  47. 47. Reese JP, John A, Wienemann G, Wellek A, Sommer N, Tackenberg B, et al. Economic burden in a German cohort of patients with multiple sclerosis. Eur Neurol. 2011;66(6):311–21. Epub 2011/11/17. pmid:22086151
  48. 48. Bergvall N, Lahoz R, Reynolds T, Korn JR. Healthcare resource use and relapses with fingolimod versus natalizumab for treating multiple sclerosis: a retrospective US claims database analysis. Curr Med Res Opin. 2014;30(8):1461–71. Epub 2014/04/24. pmid:24754349
  49. 49. Koch-Henriksen N, Magyari M, Sellebjerg F, Soelberg Sorensen P. A comparison of multiple sclerosis clinical disease activity between patients treated with natalizumab and fingolimod. Multiple sclerosis. 2016.
  50. 50. Barbin L, Rousseau C, Jousset N, Casey R, Debouverie M, Vukusic S, et al. Comparative efficacy of fingolimod vs natalizumab: A French multicenter observational study. Neurology. 2016;86(8):771–8. PubMed Central PMCID: PMC4763805. pmid:26826205
  51. 51. Kalincik T, Horakova D, Spelman T, Jokubaitis V, Trojano M, Lugaresi A, et al. Switch to natalizumab versus fingolimod in active relapsing-remitting multiple sclerosis. Ann Neurol. 2015;77(3):425–35. Epub 2014/12/30. pmid:25546031
  52. 52. Baroncini D, Ghezzi A, Annovazzi PO, Colombo B, Martinelli V, Minonzio G, et al. Natalizumab versus fingolimod in patients with relapsing-remitting multiple sclerosis non-responding to first-line injectable therapies. Multiple sclerosis. 2016;22(10):1315–26. pmid:27230789
  53. 53. Ziemssen T, Kempcke R, Eulitz M, Grossmann L, Suhrbier A, Thomas K, et al. Multiple sclerosis documentation system (MSDS): moving from documentation to management of MS patients. J Neural Transm (Vienna). 2013;120 (Suppl 1):S61–6.
  54. 54. Registrat®. The Value of Patient Registries in Clinical Research. 2008.
  55. 55. Sorensen HT. Regional administrative health registries as a resource in clinical epidemiology. A study of options, strengths, limitations and data quality provided with examples of use. Int J Risk Saf Med. 1997;10(1):1–22. Epub 1997/01/01. pmid:23511270
  56. 56. Agashivala N, Wu N, Abouzaid S, Wu Y, Kim E, Boulanger L, et al. Compliance to fingolimod and other disease modifying treatments in multiple sclerosis patients, a retrospective cohort study. BMC Neurol. 2013;13:138. Epub 2013/10/08. PubMed Central PMCID: PMC3851325. pmid:24093542
  57. 57. European MS Platform. MS Barometer 2011. Available from: http://www.emsp.org/attachments/article/160/MS_Barometer_2011.pdf.