‘This is the peer reviewed version of the following article:
Nguyen, A.-L., Havrdova, E. K., Horakova, D., Izquierdo, G., Kalincik, T., van der Walt, A., … Jokubaitis, V. (2019).
Incidence of pregnancy and disease-modifying therapy exposure trends in women with multiple sclerosis: A contemporary cohort study. Multiple Sclerosis and Related Disorders, 28, 235–243. https://doi.org/10.1016/
j.msard.2019.01.003
which has been published in final form at https://doi.org/10.1016/j.msard.2019.01.003
© 2019 Elsevier BV. This manuscript version is made available under the CC-BY-NC-ND 4.0 license:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Accepted Manuscript
Incidence of pregnancy and disease-modifying therapy exposure trends in women with multiple sclerosis: a contemporary cohort study
Ai-Lan Nguyen , Eva Kubala Havrdova , Dana Horakova , Guillermo Izquierdo , Tomas Kalincik , Anneke van der Walt , Murat Terzi , Raed Alroughani , Pierre Duquette , Marc Girard , Alexandre Prat , Cavit Boz , Patrizia Sola , Diana Ferraro ,
Alessandra Lugaresi , Jeannette Lechner-Scott , Michael Barnett , Francois Grand’Maison , Pierre Grammond , Cristina Ramo-Tello , Recai Turkoglu , Pamela McCombe , Eugenio Pucci ,
Maria Trojano , Franco Granella , Daniele Spitaleri , Vincent Van Pesch , Aysun Soysal , Celia Oreja-Guevara , Freek Verheul , Steve Vucic , Suzanne Hodgkinson , Mark Slee , Radek Ampapa , Julie Prevost , Jose Luis Sanchez Menoyo , Olga Skibina , Claudio Solaro , Javier Olascoaga ,
Cameron Shaw , Klaus Gregaard Madsen , Kerisha Naidoo , Robert Hyde , Helmut Butzkueven , Vilija Jokubaitis , On Behalf of the MSBase Study Group
PII: S2211-0348(19)30003-3
DOI: https://doi.org/10.1016/j.msard.2019.01.003
Reference: MSARD 1109
To appear in: Multiple Sclerosis and Related Disorders Received date: 11 September 2018
Accepted date: 1 January 2019
Please cite this article as: Ai-Lan Nguyen , Eva Kubala Havrdova , Dana Horakova , Guillermo Izquierdo , Tomas Kalincik , Anneke van der Walt , Murat Terzi , Raed Alroughani , Pierre Duquette , Marc Girard , Alexandre Prat , Cavit Boz , Patrizia Sola , Diana Ferraro , Alessandra Lugaresi , Jeannette Lechner-Scott , Michael Barnett , Francois Grand’Maison , Pierre Grammond , Cristina Ramo-Tello , Recai Turkoglu , Pamela McCombe , Eugenio Pucci , Maria Trojano , Franco Granella , Daniele Spitaleri , Vincent Van Pesch , Aysun Soysal , Celia Oreja-Guevara , Freek Verheul , Steve Vucic , Suzanne Hodgkinson , Mark Slee , Radek Ampapa , Julie Prevost , Jose Luis Sanchez Menoyo , Olga Skibina , Claudio Solaro , Javier Olascoaga , Cameron Shaw , Klaus Gregaard Madsen , Kerisha Naidoo , Robert Hyde , Helmut Butzkueven , Vilija Jokubaitis , On Behalf of the MSBase Study Group, Incidence of pregnancy and disease-modifying therapy exposure trends in women with multiple scle- rosis: a contemporary cohort study, Multiple Sclerosis and Related Disorders (2019), doi:
https://doi.org/10.1016/j.msard.2019.01.003
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Highlights
We performed a retrospective study of women with relapsing-remitting MS
There was a low pregnancy incidence rate over the last twelve years
An increasing number of pregnancies were conceived on treatment over this time
Treatment exposure during pregnancy was short, with a median of 30 days
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Incidence of pregnancy and disease-modifying therapy exposure trends in women with multiple sclerosis: a contemporary cohort
study
Authors & Affiliations
1. Ai-Lan Nguyen; Department of Medicine, University of Melbourne, Melbourne, Australia;
Department of Neurology, Royal Melbourne Hospital, Melbourne, Australia
2. Eva Kubala Havrdova; Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
3. Dana Horakova; Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
4. Guillermo Izquierdo; Hospital Universitario Virgen Macarena, Sevilla, Spain
5. Tomas Kalincik; CORe, Department of Medicine, University of Melbourne, Melbourne, Australia; Department of Neurology, Royal Melbourne Hospital, Melbourne, Australia 6. Anneke van der Walt; Department of Neuroscience, Central Clinical School, Monash
University, Melbourne, Australia; Department of Neurology, Alfred Hospital, Melbourne, Australia; Department of Medicine (Royal Melbourne Hospital), University of
Melbourne, Melbourne, Australia
7. Murat Terzi; Medical Faculty, 19 Mayis University, Samsun, Turkey
8. Raed Alroughani; Division of Neurology, Department of Medicine, Amiri Hospital, Kuwait City, Kuwait
9. Pierre Duquette; Centre Hospitalier de l’Universite de Montreal, Montreal, Canada 10. Marc Girard; Centre Hospitalier de l’Universite de Montreal, Montreal, Canada 11. Alexandre Prat; Centre Hospitalier de l’Universite de Montreal, Montreal, Canada 12. Cavit Boz; KTU Medical Faculty Farabi Hospital, Trabzon, Turkey
13. Patrizia Sola; Ospedale Civile, Azienda Ospedaliero-Universitaria, Modena, Italy 14. Diana Ferraro; Ospedale Civile, Azienda Ospedaliero-Universitaria,Modena, Italy 15. Alessandra Lugaresi; Dipartimento di Scienze Biomediche e Neuromotorie, Università
“Alma Mater Studiorum, Bologna, Italy; IRCCS “Istituto delle Scienze Neurologiche di Bologna”, Bologna, Italy
16. Jeannette Lechner-Scott; University Newcastle, Newcastle, Australia; John Hunter Hospital, Newcastle, Australia
17. Michael Barnett; Brain and Mind Centre, Sydney, Australia 18. Francois Grand'Maison; Neuro Rive-Sud, Quebec, Canada 19. Pierre Grammond; CISSS Chaudière-Appalache, Levis, Canada 20. Cristina Ramo-Tello; Hospital Germans Trias i Pujol, Spain
21. Recai Turkoglu; Haydarpasa Numune Training and Research Hospital, Istanbul, Turkey 22. Pamela McCombe; University of Queensland, Brisbane, Australia; Royal Brisbane and
Women's Hospital, Brisbane, Australia
23. Eugenio Pucci; UOC Neurologia, Azienda Sanitaria Unica Regionale Marche - AV3, Macerata, Italy
24. Maria Trojano; Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari, Bari, Italy
25. Franco Granella; Department of Medicine and Surgery, University of Parma, Parma, Italy 26. Daniele Spitaleri; Azienda Ospedaliera di Rilievo Nazionale San Giuseppe Moscati
Avellino, Avellino, Italy
27. Vincent Van Pesch; Cliniques Universitaires Saint-Luc, Brussels, Belgium; Université Catholique de Louvain, Louvain-la-Neuve, Belgium
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28. Aysun Soysal; Bakirkoy Education and Research Hospital for Psychiatric and Neurological Diseases, Turkey
29. Celia Oreja-Guevara; Hospital Universitario La Paz, Madrid, Spain; Hospital Clínico San Carlos (IdISCC), Madrid, Spain
30. Freek Verheul; Groene Hart ziekenhuis, Gouda, The Netherlands 31. Steve Vucic; Westmead Hospital, Sydney, Australia
32. Suzanne Hodgkinson; Liverpool Hospital, Sydney, Australia
33. Mark Slee; Flinders University and Medical Centre, Adelaide, Australia 34. Radek Ampapa; Nemocnice Jihlava, Jihlava, Czech Republic
35. Julie Prevost; CSSS Saint-Jérôme, Saint-Jerome, Canada
36. Jose Luis Sanchez Menoyo; Hospital de Galdakao-Usansolo, Galdakao, Spain 37. Olga Skibina; Box Hill Hospital, Melbourne, Australia
38. Claudio Solaro; Department of Neurology ASL3 Genovese, Genova, Italy; Department of Rehabilitation M.L. Novarese Hospital Moncrivello, Italy
39. Javier Olascoaga; Hospital Donostia, Spain
40. Cameron Shaw; Geelong Hospital, Geelong, Australia 41. Klaus Gregaard Madsen; Biogen, Copenhagen, Denmark 42. Kerisha Naidoo; Biogen, Sydney, Australia
43. Robert Hyde; Biogen, Zug, Switzerland
44. Helmut Butzkueven; Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Department of Neurology, Alfred Hospital, Melbourne, Australia; Department of Medicine (Royal Melbourne Hospital), University of
Melbourne, Melbourne, Australia
45. Vilija Jokubaitis; Department of Neuroscience, Central Clinical School, Monash
University, Melbourne, Australia; Department of Neurology, Alfred Hospital, Melbourne, Australia; Department of Medicine (Royal Melbourne Hospital), University of
Melbourne, Melbourne, Australia On Behalf of the MSBase Study Group
Corresponding author
Vilija Jokubaitis; Email: [email protected]; Telephone: +61 3 9903 0880.
Mailing Address: Level 6, Alfred Centre, 99 Commercial Rd, Melbourne, Australia 3004.
Keywords
Multiple sclerosis; Pregnancy; Incidence; Therapy; Outcomes
Word count
Abstract: 192, Main Text: 3053, Figures: 2, Tables: 4, References: 29.
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Abstract
BACKGROUND:
Exposure to disease-modifying therapy (DMT) during early pregnancy in women with relapsing-remitting MS (RRMS) may be increasing.
OBJECTIVE:
To retrospectively determine incidence of pregnancy, DMT exposure and pregnancy outcomes in women with RRMS.
METHODS:
We identified all women with RRMS aged 15-45 years in the MSBase Registry between 2005- 2016. Annualised pregnancy incidence rates were calculated using Poisson regression models.
DMT exposures and pregnancy outcomes were assessed.
RESULTS:
Of 9,098 women meeting inclusion criteria, 1,178 (13%) women recorded 1,521 pregnancies.
The annualised incidence rate of pregnancy was 0.042 (95% CI 0.040, 0.045). A total of 635 (42%) reported pregnancies were conceived on DMT, increasing from 27% in 2006 to 62% in 2016. The median duration of DMT exposure during pregnancy was 30 days (IQR: 9, 50). There were a higher number of induced abortions on FDA pregnancy class C/D drugs compared with pregnancy class B and no DMT (p=0.010); but no differences in spontaneous abortions, term or preterm births.
CONCLUSIONS:
We report low pregnancy incidence rates, with increasing number of pregnancies conceived on DMT over the past 12-years. The median duration of DMT exposure in pregnancy was relatively short at one month.
1. Introduction
Over the last 50 years, the prevalence and incidence of MS has risen.1 Women are three times more likely to develop MS than men,2 and we see an increasing female-male sex ratio in
relapsing-remitting MS (RRMS) at higher latitudes.3 This has resulted in a growing burden of MS among women of childbearing age, and family planning discussions are frequent in clinical practice.
The last two decades have seen a rise in the availability and use of disease-modifying therapies (DMTs) for RRMS. Unfortunately, there is limited information on the safety of DMT use during pregnancy, and no recognised guidelines are available; although a general recommendation is to discontinue DMT before conception in order to minimise risk of foetal harm. Anecdotally, physicians are increasingly maintaining their patients on DMT until pregnancies are confirmed.
Large registries can provide crucial information on DMT use during pregnancy as they collect long-term information obtained during routine clinical practice.
The MSBase Registry is a large, international observational cohort study, with long-term prospectively collected data. It was established in 2004 and has proven itself to be a fruitful platform for collaboration and evaluation of outcomes in MS.4 The aims of this study were to 1) retrospectively investigate the incidence of pregnancy in RRMS patients in the real-world setting; 2) report pregnancy incidence rates on DMT; 3) report duration of therapeutic exposure during pregnancy and 4) retrospectively report pregnancy outcomes.
2. Methods
2.1 MSBase Registry
Data in the MSBase registry, including prospective pregnancy data, is entered in real time or near real time, as part of routine clinical visits. The MSBase protocol mandates minimum annual updates of a minimum dataset,4 together with the date of pregnancy onset or last menstrual period, and delivery date or abortion date reported as (DD/MM/YYYY) for all pregnancies
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recorded. Portals for data entry were either the iMed patient record system or the MSBase online data entry system.4
MSBase (registered with WHO International Clinical Trials Registry Platform ID
ACTRN12605000455662), was approved by the Melbourne Health Human Research Ethics Committee and by the local ethics committees in all participating centres (or exemptions granted, according to local regulations). Written informed consent was obtained from all enrolled patients.
2.2 Study population & design
Data from the global MSBase registry dataset were extracted on 5 October 2016. Inclusion criteria comprised women of child-bearing age (15-45 years inclusive), prospectively enrolled in MSBase between 1 January 2005 and 5 October 2016, with a diagnosis of RRMS (2005 or 2010 revised McDonald criteria).5, 6 Women who converted to secondary progressive MS (SPMS) were retained in the analysis. Patients with Clinically Isolated Syndrome (CIS) were excluded. Pregnancies prior to 2005 or prior to RRMS diagnosis were also excluded. As data collection in MSBase commenced in 2004, inclusion of pregnancies from 2005 meant only prospective data was included.
Study entry date was on or after 1 January 2005, defined as the date the patient enrolled in MSBase, or the date when the participant turned 15. Censor date was the most recently recorded visit up to 5 October 2016 or when the participant turned 45. Two time epochs were chosen: 1 January 2005 to 31 December 2010, and 1 January 2011 to 6 October 2016, a period of almost 6 years each. Information collected included: demographics, clinical information, DMT exposure before and during pregnancy, and pregnancy outcomes. The pregnancy outcomes comprised: term deliveries, pre-term deliveries (<37 weeks), spontaneous abortions
(miscarriages), induced abortions and unknown outcomes (not reported, lost to follow-up, or ongoing pregnancy at data extract). Expanded Disability Status Scale (EDSS) at pregnancy onset was taken as the closest EDSS to pregnancy start date +/- 6 months, in the absence of a
concurrent relapse.
MS therapy exposures in this cohort included injectable therapies: interferon- (IFN),
glatiramer acetate (GA); the monoclonal antibodies: natalizumab, rituximab; the oral therapies:
fingolimod, dimethyl fumarate (DMF); and azathioprine. According to the U.S. Food and Drug Administration (FDA), pregnancy categories for the above MS therapies are category B: GA;
category C: IFN, natalizumab, rituximab, fingolimod, DMF; and category D: azathioprine.7 2.3 Statistical analysis
Continuous variables were assessed for normality using the Shapiro-Wilk test, and reported as mean ± standard deviation (SD) or median with interquartile range (IQR) and range, as
appropriate. Categorical variables were summarised using frequencies and percentages.
Standardised differences between groups were assessed using Cohen’s d. Annualised pregnancy incidence rates were calculated using Poisson regression models, with 95% confidence intervals (CIs) both overall and by epoch (2005-2010; 2011-2016). Annualised pregnancy incidence rates were then further adjusted for age at pregnancy start, EDSS at or near pregnancy start, and geographic region to account for regional heterogeneity of clinical practice. To account for multiple pregnancies, all models were clustered by patient with a robust estimation of variance.
Differences in therapy use over time, and pregnancy outcomes were compared using Pearson’s
2 or Fisher’s Exact Test. All statistical analyses were performed using Stata v14 (StataCorp, College Station, TX). All analyses were 2-tailed and p<0.05 was considered significant.
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3. Results
3.1 Pregnancy incidence
Across 33 countries, a total of 18,767 females aged 15-45 years inclusive with an initial
diagnosis of RRMS from the MSBase registry were screened. 9,098 (48%) women met the study inclusion criteria (Figure 1). These 9,098 women recorded a total of 36,043 patient-years follow-up. Of this cohort, 1,178 (13%) women had 1,521 pregnancies recorded, a total of 7,445 patient-years of observation including both pre and post pregnancy periods. We further identified 207 primary progressive MS (PPPMS) patients meeting the inclusion criteria, and recorded 8 pregnancies for 8 (3.9%) women with PPMS during this observation period (Supplement S3, S4).
9,669 patients were excluded (Figure 1) and the baseline demographics and clinical characteristics of the included and excluded cohorts are shown in Table 1. Included and excluded cohorts were comparable on all baseline measures with the exception of follow-up duration (Cohen’s d =0.32).
The characteristics of patients with pregnancies across the two epochs of interest are shown in Table 2. In the first epoch (2005-2010), the annual unadjusted incidence rate of pregnancy was 0.040 (95% CI 0.036, 0.044). In the second epoch (2011-2016), the annual unadjusted incidence rate of pregnancy was 0.044 (95% CI 0.041, 0.047). Overall, the unadjusted incidence rate of pregnancy was 0.042 (95% CI 0.040, 0.045) between 2005 and 2016. Unadjusted pregnancy incidence was highest in those aged 25-30 (annualised incidence rate 0.072, 95% CI 0.065, 0.079) and those aged 30-35 years (annualised incidence rate 0.071, 95% CI 0.065, 0.077).
Adjusted analyses showed that higher EDSS scores were associated with reduced pregnancy incidence, with a relative incidence risk ratio of 0.96 per EDSS point (95% CI 0.93, 0.98;
p<0.001); whereas age was not significantly associated with incidence of pregnancy when also accounting for disability and geographical region (p=0.65).
3.2 Disease-modifying therapy (DMT) exposure
Of the 1,521 pregnancies recorded: 42% reported DMT treatment at conception, 20% occurred within a year of DMT discontinuation, and 39% had no DMT exposure in the prior year (Figure 1). Of those pregnancies conceived on DMT, the median treatment exposure prior to conception was 1.46 years (IQR: 0.61, 2.82; range 0.003-13.1); while for those who stopped DMT in the prior year to conception, the median treatment exposure was 1.36 years (IQR: 0.70, 2.52; range 0.003-11.4). For pregnancies that occurred when DMTs were ceased in the prior year, the median time to conception was 91 days (IQR: 36-179; range 1-365).
Figure 2 shows the proportion of pregnancies that occurred on or off therapy during the follow- up period. An increasing trend was seen in the number of reported pregnancies conceived on therapy over time, with 27% of pregnancies conceived on therapy in 2006 compared to 62% in 2016. Exposure to specific MS therapies during each epoch is shown in Table 3. During the first epoch, 145 of 478 (30%) pregnancies occurred on therapy, which increased in the second epoch to 490 of 1043 (47%) pregnancies (p<0.001). Of the total 635 pregnancies conceived on
therapy, 487 (77%) were on injectables (55% IFNβ, 22% GA), 104 (16%) on natalizumab, 38 (6%) on oral therapy (3% each for fingolimod and dimethyl fumarate), and the remaining few on rituximab and azathioprine. No pregnancies were conceived on teriflunomide.
The annualised incidence rates of pregnancy by DMT class were similar: injectables 0.23 (95%
CI 0.22, 0.24), oral therapies 0.26 (95% CI 0.22, 0.32) and monoclonal antibodies 0.25 (95% CI 0.23, 0.28). The median duration of DMT exposure during pregnancy was 30 days (IQR: 9, 50;
range 1-300) and was comparable between DMT classes: i.e. the injectable, monoclonal
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antibody and oral therapies (Table 3). Seventy-six pregnancies had DMTs continued for the duration of the pregnancy (12% of pregnancies on therapy): IFN-1a i.m.=14, IFN-1b=10, IFN-1a i.m.=21, GA=14, natalizumab=13, fingolimod=2, rituximab=2.
Of the 2 patients who conceived ‘on’ rituximab, the first patient had a single dose of rituximab 36 days before estimated date of conception, while the second patient had rituximab 18 days before conception. The second patient had been on rituximab for 2 years and 11 months pre- pregnancy, and recommenced therapy postpartum. Neither patient had rituximab dosing during pregnancy.
3.3 Pregnancy outcomes
The proportions of pregnancies with term deliveries, pre-term deliveries, spontaneous abortions, induced abortions or unknown outcomes are shown stratified by DMT identity in Table 4. Pregnancy outcomes were reported for 1,285 (84%) pregnancies. These included 1,134 livebirths in 1,019 women (median 1 (IQR 1,1); mean 1.11 0.49 children/woman). We separated therapeutic exposure during pregnancy into three groups: no therapy, FDA
pregnancy category B medications or FDA pregnancy category C & D medications. There were no differences between these three groups for term deliveries (2(2)=1.39, p=0.50), pre-term deliveries (2(2)=3.99, p=0.14) or spontaneous abortions (2(2)=2.02, p=0.90). More patients on category C & D medications had induced abortions compared to those on no therapy or category B medications (2(2)=9.14, p=0.01).
We also compared pregnancy outcomes on individual DMTs (IFN, GA, natalizumab, DMF, fingolimod) to the untreated group (Table 4). Our cohort of IFN-treated patients did not have increased preterm births or spontaneous abortions, although a trend towards more induced abortions was seen (p=0.076). The natalizumab cohort had a higher proportion of induced abortions (p<0.001).
4. Discussion
In this retrospective analysis of women with pregnancies recorded in the MSBase dataset, we report an overall annualised incidence of pregnancy of 4.2%, with a minor increase from 4%
between 2005-2010 to 4.4% between 2011-2016. To the author’s knowledge, no previous study has explored the incidence of pregnancy in women with relapse-onset MS.
In our study, there was an average of 1.38 pregnancies or 1.11 livebirths per parous woman.
This is lower than the United Nations reported global fertility rate of 2.52 children per woman from 2010 to 2015.8 It is also slightly lower than the rates reported in a recent French cohort, which found a mean number of 1.37 children per woman with MS.9 However, the French cohort included pregnancies that occurred before MS diagnosis, which were excluded in our study. The current literature does not suggest fertility is reduced in women with MS,9-11 but our results do add to pre-existing reports of women with MS having fewer children.9 It is unclear whether this reflects a biological or behavioural effect, and warrants further investigation.12
The minor increase in pregnancy incidence over time accompanied by a rising proportion of pregnancies with DMT exposure could be a consequence of increased clinician surveillance and reporting, especially with the rapid expansion of DMTs in the market with largely unknown foetal effects. It is also likely that practices are changing, and clinicians are increasingly comfortable discontinuing DMTs once patients become pregnant rather than beforehand, especially if patients exhibit high disease activity.
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maximum of 62% in 2016, in line with a Brazilian study of 142 pregnancies that reported up to 70% had exposure to medications, predominantly injectables.15 Past observational studies have reported therapeutic exposure during pregnancy in the range of 7-9 weeks.13, 14 In our cohort, the median duration of therapy exposure during pregnancy was 30 days and similar across the injectable, monoclonal antibody and oral therapies, suggesting that most patients ceased treatment once pregnancy was confirmed.
Of the patients who conceived on therapy, 12% continued treatment throughout pregnancy. The injectable therapies accounted for the majority (78%) of these, although 17% were on
natalizumab, which likely represents a more active or aggressive subset of MS patients.
Interestingly, the two patients on rituximab both had dosing about a month before conceiving.
One patient had a single dose of rituximab 36 days before conception and a term delivery;
whilst the other patient had been on rituximab for 3 years, was dosed 18 days before conception and had a pre-term delivery at 35 weeks. Limited data exists on pregnancy outcomes after rituximab exposure in MS. A study using the rituximab global drug safety database identified 153 pregnancies associated with maternal rituximab exposure and known pregnancy outcomes, of which only three pregnancies occurred in patients with MS. Overall, 90 (59%) were livebirths and 22 (24%) were premature.16
With regard to pregnancy outcomes, the rate of spontaneous abortions in our study (6%) is lower than the 8-20% reported in the general population17, 18 and in the MS literature.9 This may be due to under-reporting of abortions, which are not mandated in the MSBase registry. Almost 20% of deliveries in our cohort were pre-term. This is higher than the 11% reported globally in 2010,19 although it should be noted that there was no matched non-MS control group and a proportion (15%) of pregnancies in our group had no outcome reported. There are varying reports on the association of MS on pre-term delivery. A Taiwanese study reported a
significantly increased risk of preterm birth in 174 MS women (13%) compared to a matched group without chronic disease (7%).20 Conversely, a Norwegian study showed no difference in preterm birth in 649 MS women (8%) compared to over 2 million controls (6%).21 This was further supported by a Canadian study that showed no difference in the mean gestational age between MS and non-MS women.22 In a systemic review of 22 papers, Finkelstjn et al (2011) calculated the rate of prematurity amongst pregnant women with MS to be 10%,23 although it should be noted that the definition of prematurity used in this paper was <38 weeks as opposed to <37 weeks in our study, and therefore, 10% may be an underestimate of the true value.
Our study assessed differences in pregnancy outcomes and found a difference only for FDA pregnancy category C & D medications having a higher rate of induced abortions compared to those on category B medications or no therapy. Women on IFN therapy made up the majority of our patients on category C & D medications (70%), and further analyses of the IFN-treated patients did not show increased preterm births or spontaneous abortions compared to the untreated group, however a trend towards increased induced abortions was seen (p=0.076) (Table 3). Early reports suggested that IFN exposure was associated with a higher risk of foetal loss24 although subsequent studies do not support this.25, 26 One systemic literature review of fifteen studies concluded that IFN was associated with more preterm deliveries but not spontaneous abortions.25 There is also evidence that GA and natalizumab do not cause preterm birth or spontaneous abortions,1, 2, 13, 14, 25, 27 although a recent paper demonstrated that
natalizumab exposure resulted in an increased risk of spontaneous abortions but this
proportion (17.4%) was comparable to that expected in the general Italian population.28 Our study did not demonstrate an increased risk of preterm births or spontaneous abortions in either the GA or natalizumab groups. There are unfortunately few studies evaluating pregnancy outcomes on the newer DMT’s, and our study had relatively low numbers of fingolimod and DMF exposure during pregnancy.
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We acknowledge the limitations to our study. As previously mentioned, pregnancy data is not mandated in the MSBase registry and there may be under-reporting of pregnancies, especially miscarriages and induced abortions which may have occurred in between clinic visits. This is less likely the case for livebirths as data collection is prospective and patients are frequently assessed every 6-12 months. There may also be a bias to report pregnancies that are exposed to DMTs, due to more vigilant follow-up and this may represent a selection bias of pregnancy data being entered in the registry. Another limitation is that there are a proportion of pregnancy outcomes that were unknown (15%), most notably for the fingolimod subgroup, and definitive conclusions should not be drawn. Our study was not specifically designed to address pregnancy outcomes in MS patients, and this would require stricter data capture.
Further studies are necessary to explore the long-term developmental effects of DMT exposure in-utero. A study from the Brazilian database followed 180 pregnancies in patients with MS, 85 of whom had exposure to DMTs for at least 2 weeks (mean 13 weeks). The 180 children had a mean age of almost 7 years (range 1-39), and no specific long-term adverse events or
complications were seen in the offspring of women who had DMT exposure during pregnancy.29 These findings are reassuring, but need to be validated in much larger population studies across a broader range of DMTs.
In summary, we report a low annualised incidence of pregnancy in the MSBase MS cohort, together with an increasing proportion of pregnancies conceived on DMT over time, which likely reflects changing clinician and patient attitudes. The incidence rates of pregnancy by therapy type were similar and the median duration of exposure was short for all the DMT classes. We did not find differences in the proportion of term, pre-term births or miscarriages between pregnancies conceived on or off DMT, although the caveat is that a proportion of pregnancy outcomes were unknown. Our study’s main strength is that it utilises a large pool of prospectively collected data and is the largest reported observational study of DMT exposure in pregnancy to date, including the more recent oral therapies for which limited data are available.
Plans are underway in the MSBase registry to improve capture of pregnancy outcomes, together with maternal and foetal outcomes, and this will be reported in the future.
Author Contribution
VGJ, HB, KGM, KN, and RH conceived and designed the study. VGJ was responsible for the statistical analysis. AN, VGJ, HB, KGM, KN, RH contributed to the interpretation of the data. AN drafted the manuscript. EKH, DH, GI, HB, TK, AVDW, MT, RA, PD, MG, AP, CB, PS, DF, AL, JLS, MB, FGM, PG, CRT, RT, PMC, EP, MT, FG, DS, VVP, AS, COG, FV, SV, SH, MS, RA, JP, JLSM, OS, CS, JO, CS contributed to data acquisition. All authors revised the manuscript for intellectual content.
Acknowledgements
The list of MSBase Study Group co-investigators and contributors is given in Supplement S1 and S2. We would also like to thank the MSBase Registry Team (Ms Sabah Quddus, Ms Charlotte Sartori and Ms Eloise Hinson) for their administrative support.
Conflict of Interest
1. Ai-Lan Nguyen received research grants from Novartis, Biogen, Merck Serono and MS Research Australia; consulting fees from EMD Serono; travel support from Genzyme- Sanofi, Biogen and Roche.
2. Eva Kubala Havrdova received speaker honoraria and consultant fees from Actelion, Biogen, Celgene, Merck, Novartis, Roche, Sanofi and Teva, and support for research
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3. Dana Horakova received speaker honoraria and consulting fees from Biogen, Merck, Teva and Novartis; support for research activities from Biogen; research grants from Charles University in Prague.
4. Guillermo Izquierdo received speaking honoraria from Bayer, Biogen, Novartis, Sanofi, Merck, Roche, TG therapeutics, Oryzon and Teva.
5. Tomas Kalincik served on scientific advisory boards for Roche, Genzyme-Sanofi, Novartis, Merck and Biogen, steering committee for Brain Atrophy Initiative by Genzyme, received conference travel support and/or speaker honoraria from WebMD Global, Novartis, Biogen, Genzyme-Sanofi, Teva, BioCSL and Merck and received research support from Biogen.
6. Anneke van der Walt received travel support, speaker honoraria and served on advisory boards for Biogen, Merck, Genzyme, Novartis and Teva.
7. Murat Terzi received travel grants from Merck, Novartis, Bayer-Schering, Merck and Teva; participated in clinical trials by Sanofi Aventis, Roche and Novartis.
8. Raed Alroughani received honororia from Bayer, Biogen, Merck, GSK, Novartis, Roche &
Sanofi-Genzyme; served on advisory board for Bayer, Biogen, Novartis, Merck, Roche and Sanofi-Genzyme.
9. Pierre Duquette served on editorial boards and was supported to attend meetings by EMD, Biogen, Novartis, Genzyme and TEVA; holds grants from the CIHR and the MS Society of Canada; received funding for investigator-initiated trials from Biogen, Novartis Genzyme.
10. Marc Girard received consulting fees from Teva Canada, Biogen, Novartis and Genzyme Sanofi; lecture payments from Teva Canada, Novartis and EMD; received a research grant from CIHR.
11. Alexandre Prat: nothing to disclose.
12. Cavit Boz received conference travel support from Biogen, Novartis, Bayer-Schering, Merck and Teva; participated in clinical trials by Sanofi Aventis, Roche and Novartis.
13. Patrizia Sola served on scientific advisory boards for Biogen Idec and TEVA; received funding for travel and speaker honoraria from Biogen Idec, Merck, Teva, Sanofi Genzyme, Novartis and Bayer and research grants for her Institution from Bayer, Biogen, Merck, Novartis, Sanofi, Teva.
14. Diana Ferraro received travel grants and/or speaker honoraria from Merck, TEVA, Novartis, Biogen and Sanofi-Genzyme.
15. Alessandra Lugaresi served on scientific advisory boards for Bayer, Biogen, Merck, Novartis, Roche, Sanofi/Genzyme and Teva. She received travel grants and honoraria from Bayer, Biogen, Merck, Novartis, Sanofi/Genzyme, Teva and FISM. Her institution received research grants from Bayer, Biogen, Merck, Novartis, Sanofi/Genzyme, Teva and FISM.
16. Jeannette Lechner-Scott accepted travel compensation from Novartis, Biogen and Merck.
Her institution receives the honoraria for talks and advisory board commitment from Bayer Health Care, Biogen, Genzyme Sanofi, Merck, Novartis and Teva; was involved in clinical trials with Biogen, Novartis and Teva.
17. Michael Barnett served on scientific advisory boards for Biogen, Novartis and Genzyme;
received conference travel support from Biogen and Novartis; serves on steering committees for trials conducted by Novartis. His institution received research support from Biogen, Merck and Novartis.
18. Francois Grand´Maison received honoraria or research funding from Biogen, Genzyme, Novartis, Teva, Mitsubishi and ONO Pharmaceuticals.
19. Pierre Grammond is a Merck, Novartis, Teva, Biogen and Genzyme advisory board member, consultant for Merck; received payments for lectures by Merck, Teva and Canadian Multiple sclerosis society; received grants for travel from Teva and Novartis.
20. Cristina Ramo-Tello received research funding, compensation for travel or speaker honoraria from Biogen, Novartis, Genzyme, Sanofi and Almirall.
21. Recai Turkoglu: nothing to disclose.
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22. Pamela McCombe received honoraria and consulting fees from Novartis, Bayer Schering and Sanofi and travel grants from Novartis, Biogen and Bayer Schering.
23. Eugenio Pucci served on scientific advisory boards for Merck, Genzyme and Biogen; he has received honoraria and travel grants from Sanofi Aventis, Novartis, Biogen, Merck, Genzyme and Teva; he has received travel grants and equipment from "Associazione Marchigiana Sclerosi Multipla e altre malattie neurologiche".
24. Maria Trojano received speaker honoraria from Biogen-Idec, Bayer-Schering, Sanofi Aventis, Merck, Teva, Novartis and Almirall; received research grants for her Institution from Biogen-Idec, Merck Novartis.
25. Franco Granella received research grant from Biogen, served on scientific advisory boards for Biogen, Novartis, Merck, and Sanofi-Aventis and received funding for travel and speaker honoraria from Biogen, Merck, Sanofi-Aventis, and Almirall.
26. Daniele Spitaleri received honoraria as a consultant on scientific advisory boards by Bayer-Schering, Novartis and Sanofi-Aventis and compensation for travel from Novartis, Biogen, Sanofi Aventis, Teva and Merck.
27. Vincent Van Pesch received travel grants from Biogen, Bayer Schering, Genzyme, Merck, Teva and Novartis. His institution receives honoraria for consultancy and lectures from Biogen, Bayer Schering, Genzyme, Merck, Roche, Teva and Novartis and research grants from Novartis and Bayer Schering.
28. Aysun Soysal received conference travel support from Biogen, Novartis, Bayer-Schering, Merck, Teva and Sanovel; has participated in clinical trials by Astra Zenica and Novartis.
29. Celia Oreja-Guevara received speaker honoraria from Biogen-Idec, Roche, Sanofi- Aventis, Merck, Teva, Novartis and Actelion.
30. Freek Verheul is an advisory board member for Teva Biogen Merck and Novartis.
31. Steve Vucic: nothing to disclose.
32. Suzanne Hodgkinson received honoraria and consulting fees from Novartis, Bayer Schering and Sanofi; travel grants from Novartis, Biogen Idec and Bayer Schering.
33. Mark Slee participated in, but not received honoraria for, advisory board activity for Biogen, Merck, Bayer Schering, Sanofi Aventis and Novartis.
34. Radek Ampapa received conference travel support from Novartis, Teva, Biogen, Bayer and Merck; participated in clinical trials by Biogen, Novartis, Teva and Actelion.
35. Julie Prevost accepted travel compensation from Novartis, Biogen, Genzyme, Teva speaking honoraria from Biogen, Novartis, Genzyme and Teva.
36. Jose Luis Sanchez-Menoyo accepted travel compensation from Novartis and Biogen;
speaking honoraria from Biogen, Novartis, Sanofi, Merck, Almirall, Bayer and Teva;
participated in a clinical trial by Biogen.
37. Olga Skibina received travel support, speaker honoraria for Biogen, Merck, Genzyme and Novartis and served on scientific advisory board of Merck and Biogen.
38. Claudio Solaro: nothing to disclose.
39. Javier Olascoaga has served on scientific advisory boards for Biogen Idec, Genzyme and Novartis and has received speaker honoraria from Biogen Idec, Bayer-Schering,
Genzyme, Merck-Serono, Novartis and Teva.
40. Cameron Shaw received travel assistance from Biogen and Novartis.
41. Klaus Gregaard Madsen is a Biogen employee.
42. Kerisha Naidoo is a Biogen employee.
43. Robert Hyde is a Biogen employee.
44. Helmut Butzkueven served on scientific advisory boards for Biogen, Novartis and Sanofi-Aventis and received conference travel support from Novartis, Biogen and Sanofi Aventis. He serves on steering committees for trials conducted by Biogen and Novartis received research support from Merck, Novartis and Biogen.
45. Vilija Jokubaitis received conference travel support from Biogen, Novartis and Teva; and
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Funding
This study was financially supported by a National Health and Medical Research Council of Australia centre for research excellence grant [1001216]. The MSBase Foundation is a not-for- profit organisation that receives support from Biogen, Merck, Novartis, Roche and Sanofi- Genzyme. The study was conducted separately and apart from the guidance of the sponsors.
References
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10.1080/00016340500494887.
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26. Amato MP, Portaccio E, Ghezzi A, et al. Pregnancy and fetal outcomes after interferon- beta exposure in multiple sclerosis. Neurology 2010; 75: 1794-1802. 2010/11/17. DOI:
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Tables & Figures
Figure 1. Flowchart for study inclusion and breakdown of pregnancies and DMT exposure. Patients excluded due to incomplete minimum dataset had at least one of the
following information missing: date of birth, sex, MS diagnosis date, MS phenotype, minimum of one EDSS assessment. RRMS, relapsing-remitting MS; DMT, disease-modifying therapy; i.m., intramuscular; s.c., subcutaneous.
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15 Table 1. Baseline and clinical characteristics of included cohort, pregnancies and excluded patients occurring during study period (2005- 2016).
Baseline features Included cohort
(n=9098) Pregnancies
(n=1521) Excluded cohort~
(n=9669) Cohen’s d
(included vs excluded) Follow-up period#, y; median (IQR; range) 3.4
(1.5, 6.0; 0-11.6) 6.4
(4.1, 8.6; 0.2-11.5) 3.1
(0, 10.7; 0-11.6) -0.321
Age*, y; median (IQR, range) 32.7
(26.8, 38.7; 15.0-45.0) 31.8
(28.5, 34.8; 15.4-43.8) 31.8
(26.2, 37.7; 15-45) 0.061 Disease duration*, y; median (IQR; range) 2.1
(0.4, 6.8; 0-38.1) 5.1
(2.9, 8.3; 0.1-24.7) 2.4
(0.6, 6.9; 0-35) -0.062
EDSS*^; median (IQR; range) 2.0
(1, 3; 0-9.5) 1.5
(1, 2; 0-7.5) 2.0
(1, 3; 0-9.5) -0.075
ARR 1y prior to pregnancy+; median (IQR) NA 0
(0,1) NA NA
Pregnancies per woman; median (IQR;
range); mean ± SD NA 1 (1, 2; 1-5)
1.38 0.63 NA NA
EDSS, Expanded Disability Status Scale; ARR, annualised relapse rate; IQR, interquartile range; SD, standard deviation.
~ Excluded cohort included those, whose 1st visit was prior to 2005, had no follow-up, no visit data, date incongruities, pregnancy prior to MS diagnosis
#total follow-up period during study eligibility
*at start of observation period for total cohort and excluded patients or start of pregnancy
^whole cohort n=8407, pregnant cohort n=1263, excluded patients n=6704 +n=1376
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16 Table 2. Baseline and clinical characteristics of pregnancies between 2005-2010 & 2011-2016.
Baseline features at time of pregnancy 2005-2010 2011-2016
Number of women 410 867
Number of pregnancies 478 1043
Pregnancies per woman; median (IQR;
range); mean ± SD 1 (1,1; 1-4)
1.17 ± 0.44 1 (1,1; 1-5) 1.20 ± 0.48
Age, y; median (IQR; range) 31.3
(28.2, 34.5; 18.6-42.5) 31.9 (28.6, 35.0; 15.4-43.8) Disease duration, y; median (IQR; range) 4.0
(2.2, 7.3; 0.1-24.7) 5.6 (3.2, 8.5; 0.2-22.7) MS phenotype at pregnancy:
RRMS; n (%)
SPMS; n (%) 461 (96.4)
17 (3.6) 1038 (99.5)
5 (0.5)
EDSS^; median (IQR; range) 1.5
(1, 2; 0-6.5) 1.5
(1, 2; 0-7.5) ARR 1y prior to pregnancy+; median (IQR) 0
(0,1) 0
(0,1)
EDSS, Expanded Disability Status Scale; ARR, annualised relapse rate; IQR, interquartile range; SD, standard deviation; RRMS, relapsing-remitting MS; SPMS, secondary-progressive MS.
^n=376 (2005-2010), n=896 (2011-2016) +n=409 (2005-2010), n=967 (2011-2016)
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Figure 2. Proportion of pregnancies that occurred whilst on or off therapy between 2005- 2016.
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18 Table 3. Pregnancies occurring on DMT (n=635) & duration of DMT use prior to and during pregnancy in each epoch: 2005-2010 & 2011- 2016
DMT Pregnancies on DMT
2005-2010 (n=145; 30% of pregnancies) Pregnancies on DMT
2011-2016 (n=490; 47% of pregnancies) n (%) Time on therapy
prior to pregnancy, y;
median (IQR)
Time on therapy during
pregnancy, d;
median (IQR)
n (%) Time on therapy prior to pregnancy, y;
median (IQR)
Time on therapy during
pregnancy, d;
median (IQR) IFN-1a i.m. (n=125) 41 (28) 1.3 (0.8, 2.3) 33 (9,58) 84 (17) 2.6 (1.4, 4.2) 29 (9,40) IFN-1b* (n=61) 19 (13) 1.7 (0.6, 3.7) 38 (15,227) 42 (9) 1.4 (0.4, 2.3) 21 (8,42) IFN-1a s.c. (n=164) 39 (27) 1.9 (0.6, 2.7) 31 (16,85) 125 (26) 1.9 (0.8, 3.1) 30 (11,47) GA (n=137) 36 (25) 1.3 (0.8, 2.3) 52 (18,101) 101 (21) 1.4 (0.5, 2.8) 29 (5,42) Natalizumab (n=104) 6 (4) 0.3 (0.1, 0.8) 9 (1,68) 98 (20) 1.1 (0.4, 2.0) 24 (9,41)
DMF (n=17) 1 (1) 0.9 30 16 (3) 0.5 (0.3, 0.9) 21 (1,31)
Fingolimod (n=21) - - - 21 (4) 0.7 (0.5, 2.1) 25 (7,52)
Azathioprine (n=4) 3 (2) 2.4 (1.5, 3.1) 42 (33, 112) 1 (<1) 4.2 149
Rituximab (n=2) - - - 2 (<1) ^ +
IFN, interferon-; i.m., intramuscular; s.c., subcutaneous; GA, glatiramer acetate; DMF, dimethyl fumarate; IQR, interquartile range.
* Betaferon & Extavia
^ No median: 1 patient was on therapy for 3 years and the other patient had a single dose, pre-pregnancy
+ In these 2 patients, conception occurred 18 and 36 days after rituximab. No rituximab was given during pregnancy
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Table 4. DMT use during pregnancy & pregnancy outcomes
Pregnancies Pregnancy outcomes
Term
(37 weeks) Pre-term
(<37 weeks) Spontaneous abortion/
Miscarriage
Induced
abortion Unknown All (n=1521);
n (%*) 847
(56) 287
(19) 87
(6) 64
(4) 236
(15) No DMT (n=886);
n (%) 504
(57) 182
(20) 50
(6) 27
(3) 123
(14) On any DMT (635);
n (%)
343 (54)
105 (16)
37 (6)
37 (6)
113 (18) Pregnancies disaggregated by DMT identity
IFN-1a i.m. (n=125);
n (%)
80
(40) 11
(9) 10
(8) 5
(4) 19
(15) IFN-1b^ (n=61);
n (%)
35 (57)
10 (16)
3 (5)
4 (7)
9 (15) IFN-1a s.c. (n=164);
n (%)
96
(59) 36
(22) 3
(2) 9
(5) 20
(12) All IFN (350);
n (%)
211
(60.3) 57
(16.3) 16
(4.6) 181
(5.1) 48
(13.7) GA (n=137);
n (%) 72
(53) 24
(17) 9
(6) 5
(4) 272
(20) Natalizumab (n=104);
n (%) 41**
(39) 16
(15) 8
(8) 13***
(13) 26**
(25) DMF (n=17);
n (%)
8 (47)
3 (18)
33 (18)
0 3
(18) Fingolimod (n=21);
n (%) 7
(33) 3#
(14) 1
(5) 1
(5) 9##
(43) Azathioprine (n=4);
n (%)
3 (75)
1 (25)
0 0 0
Rituximab (n=2)+;
n (%) 1
(50) 1
(50) 0 0 0
IFN, interferon-; i.m., intramuscular; s.c., subcutaneous; GA, glatiramer acetate; DMF, dimethyl fumarate.
* Percentage of pregnancy outcome
^ Betaferon & Extavia
+ The patient with a single (pre-pregnancy) dose of rituximab had a term delivery, whilst the patient on rituximab for three years had a pre-term delivery at 35 weeks.
12 ptrend=0.076 compared to no DMT exposure
22 ptrend=0.073 compared to no DMT exposure
**2 p<0.01 compared to no DMT exposure
***2 p<0.001 compared to no DMT exposure
3Fisher’s Exact ptrend=0.072 compared to no DMT exposure
#Fisher’s Exact p<0.05 compared to no DMT exposure
##Fisher’s Exact p<0.01 compared to no DMT exposure
