Advertisement

Advances in Therapy

, Volume 36, Issue 1, pp 175–186 | Cite as

Adherence to Subcutaneous IFN β-1a in Multiple Sclerosis: Final Analysis of the Non-Interventional Study READOUTsmart Using the Dosing Log and Readout Function of RebiSmart®

  • Peter Rieckmann
  • Matthias Schwab
  • Dieter Pöhlau
  • Iris-Katharina Penner
  • Torsten Wagner
  • Elke Schel
  • Antonios Bayas
Original Research

Abstract

Introduction

Patient adherence is a key determinant of treatment success in multiple sclerosis (MS). The RebiSmart® autoinjector facilitates patient self-injection of subcutaneous interferon β-1a (sc IFN β-1a) and allows quantitative measurement of adherence via its automated dosing log. We evaluated patient adherence and patient-reported cognitive and health-economic outcomes over 2 years in patients using RebiSmart®.

Methods

In this non-interventional, single-arm study, enrolled patients were 12–65 years of age, had relapsing–remitting MS or a single demyelinating event, and had been prescribed 44 or 22 μg sc IFN β-1a. Quantitative adherence (proportion of scheduled injections administered) and qualitative adherence (proportion of weeks with treatment schedule correctly followed) were monitored over 2 years. Other end points included self-assessed adherence, patient-reported outcomes (fatigue, depression and quality of life), cognitive outcomes and health-economic outcomes.

Results

A total of 368 of 392 (93.9%) enrolled patients were analyzed. Mean quantitative adherence was 85.3% overall (months 1–24), 89.6% for months 1–12 and 83.3% for months 13–24. No major impact on quantitative adherence was observed for sex, age (< 37 years vs. ≥ 37 years), prior medication or participation in the patient support program RebiSTAR®. Mean qualitative adherence was 67.0% overall (months 1–24). Self-assessed adherence was reported as being higher than RebiSmart®-monitored adherence. There was a trend toward more MS-related visits to physicians among patients with high adherence.

Conclusions

Patients using RebiSmart® demonstrated high adherence to treatment that was associated with a slight improvement in information processing speed and working memory and an overall tendency for more intensive self-management.

Funding

Merck Serono GmbH, Germany, an affiliate of Merck KGaA, Darmstadt, Germany.

Keywords

Health economic outcomes IFN β-1a Neurology Patient adherence Patient-reported outcomes Qualitative adherence Quantitative adherence RebiSmart 

Notes

Acknowledgements

The authors thank the patients and their families, investigators, co-investigators and study teams at each of the participating centers and at Merck KGaA, Darmstadt, Germany. The authors also thank Dr. Michael Obermeier (Senior Biostatistician, GKM Gesellschaft für Therapieforschung mbH) for his contribution as data analyst and statistical supervisor.

Funding

The study and analyses as well as funding of the journal’s article processing charges were supported by Merck Serono GmbH, Germany, an affiliate of Merck KGaA, Darmstadt, Germany. All authors had full access to all of the data in this study and take complete responsibility for the integrity of the data and accuracy of the data analysis.

Medical Writing and other Editorial Assistance

Medical writing assistance was provided by James Yates of inScience Communications, Chester, UK, and funded by Merck Serono GmbH, Darmstadt, Germany.

Authorship

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this manuscript, take responsibility for the integrity of the work as a whole and have given final approval to the version to be published.

Disclosures

Peter Rieckmann received honoraria for lectures/steering committee meetings from Merck Serono, Biogen Idec, Bayer Schering Pharma, Boehringer-Ingelheim, Sanofi-Aventis, Genzyme, Novartis, Teva Pharmaceutical Industries and Serono Symposia International Foundation. Matthias Schwab received personal compensation for activities with Biogen Idec, Bayer Healthcare, Genzyme, Merck Serono, Novartis and Teva Sanofi. He has received research support from Bayer Healthcare and Novartis. Dieter Poehlau received honoraria for lectures from Almirall, Biogen Idec, Genzyme, Schering, Merck Serono, Sanofi-Aventis, Teva, Aventis, Novartis, Roche and Boehringer. Iris-Katharina Penner received honoraria for speaking at scientific meetings, serving at scientific advisory boards and consulting activities from Adamas Pharma, Almirall, Bayer Pharma, Biogen, Desitin, Genzyme, Merck Serono, Novartis, Roche and Teva. She has received research support from the German MS Society and TEVA. Torsten Wagner is an employee of Merck Serono GmbH, Darmstadt, Germany. Elke Schel is an employee of Merck Serono GmbH, Darmstadt, Germany. Antonios Bayas received honoraria for consultancy and/or as a speaker from Merck Serono, Biogen, Bayer Vital, Novartis, TEVA, Roche and Sanofi/Genzyme; for trial activities from Biogen, Merck Serono and Novartis; and received grants for congress trips and participation from Biogen, Novartis, TEVA, Sanofi/Genzyme and Merck Serono.

Compliance with Ethics Guidelines

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964, as revised in 2013. Informed consent was obtained from all patients for being included in the study. The study was approved by the ethics committee of the Regional Medical board Hessen, Germany.

Data Availability

The data sets analyzed during the current study are available from the corresponding author on reasonable request.

Supplementary material

12325_2018_839_MOESM1_ESM.docx (30 kb)
Supplementary material 1 (DOCX 29 kb)

References

  1. 1.
    Hansen K, Schussel K, Kieble M, et al. Adherence to disease modifying drugs among patients with multiple sclerosis in Germany: a retrospective cohort study. PLoS One. 2015;10(7):e0133279.CrossRefGoogle Scholar
  2. 2.
    Sabbagh A, Bennett R, Kozma C, Dickson M, Meletiche D. Medication gaps in disease-modifying drug therapy for multiple sclerosis are associated with an increased risk of relapse: findings from a national managed care database. J Neurol. 2008;255:79.Google Scholar
  3. 3.
    Hupperts R, Ghazi-Visser L, Martins Silva A, et al. The STAR Study: a real-world, international, observational study of the safety and tolerability of, and adherence to, serum-free subcutaneous interferon beta-1a in patients with relapsing multiple sclerosis. Clin Ther. 2014;36(12):1946–57.CrossRefGoogle Scholar
  4. 4.
    Tan H, Cai QA, Agarwal S, Stephenson JJ, Kamat S. Impact of adherence to disease-modifying therapies on clinical and economic outcomes among patients with multiple sclerosis. Adv Ther. 2011;28(1):51–61.CrossRefGoogle Scholar
  5. 5.
    Singer B, Wray S, Miller T, et al. Patient-rated ease of use and functional reliability of an electronic autoinjector for self-injection of subcutaneous interferon beta-la for relapsing multiple sclerosis. Mult Scler Relat Dis. 2012;1(2):87–94.CrossRefGoogle Scholar
  6. 6.
    Pozzilli C, Schweikert B, Ecari U, Czekalla J, Oentrich W. Factors influencing adherence, quality of life and depression symptoms in multiple sclerosis patients. J Neurol. 2011;258:95.Google Scholar
  7. 7.
    Roche J, McCarry Y, Mellors K. Enhanced patient support services improve patient persistence with multiple sclerosis treatment. Patient Prefer Adherence. 2014;8:805–11.CrossRefGoogle Scholar
  8. 8.
    Bayas A, Ouallet JC, Kallmann B, Hupperts R, Fulda U, Marhardt K. Adherence to, and effectiveness of, subcutaneous interferon beta-1a administered by RebiSmart(R) in patients with relapsing multiple sclerosis: results of the 1-year, observational SMART study. Expert Opin Drug Deliv. 2015;12(8):1239–50.CrossRefGoogle Scholar
  9. 9.
    Lugaresi A. RebiSmart (version 1.5) device for multiple sclerosis treatment delivery and adherence. Expert Opin Drug Deliv. 2013;10(2):273.Google Scholar
  10. 10.
    Depont F, Berenbaum F, Filippi J, et al. Interventions to improve adherence in patients with immune-mediated inflammatory disorders: a systematic review. PLoS One. 2015;10(12):e0145076.CrossRefGoogle Scholar
  11. 11.
    Steinberg SC, Faris RJ, Chang CF, Chan A, Tankersley MA. Impact of adherence to interferons in the treatment of multiple sclerosis a non-experimental, retrospective. Cohort Study. Clin Drug Invest. 2010;30(2):89–100.CrossRefGoogle Scholar
  12. 12.
    Polman CH, Wolinsky JS, Reingold SC. Multiple sclerosis diagnostic criteria: three years later. Mult Scler. 2005;11(1):5–12.CrossRefGoogle Scholar
  13. 13.
    Penner IK, Raselli C, Stocklin M, Opwis K, Kappos L, Calabrese P. The Fatigue Scale for Motor and Cognitive Functions (FSMC): validation of a new instrument to assess multiple sclerosis-related fatigue. Mult Scler. 2009;15(12):1509–17.CrossRefGoogle Scholar
  14. 14.
    Benedict RHB, Fishman I, McClellan MM, Bakshi R, Weinstock-Guttman B. Validity of the beck depression inventory-fast screen in multiple sclerosis. Mult Scler J. 2003;9(4):393–6.CrossRefGoogle Scholar
  15. 15.
    Simeoni MC, Auquier P, Fernandez O, et al. Validation of the multiple sclerosis international quality of life questionnaire. Mult Scler J. 2008;14(2):219–30.CrossRefGoogle Scholar
  16. 16.
    Parmenter BA, Weinstock-Guttman B, Garg N, Munschauer F, Benedict RH. Screening for cognitive impairment in multiple sclerosis using the Symbol digit Modalities Test. Mult Scler. 2007;13(1):52–7.CrossRefGoogle Scholar
  17. 17.
    Lugaresi A, De Robertis F, Clerico M, et al. Long-term adherence of patients with relapsing-remitting multiple sclerosis to subcutaneous self-injections of interferon beta-1a using an electronic device: the RIVER study. Expert Opin Drug Deliv. 2016;13(7):931–5.CrossRefGoogle Scholar
  18. 18.
    Fernandez O, Arroyo R, Martinez-Yelamos S, et al. Long-term adherence to IFN beta-1a treatment when using RebiSmart(R) device in patients with relapsing-remitting multiple sclerosis. PLoS One. 2016;11(8):e0160313.CrossRefGoogle Scholar
  19. 19.
    Johnson KM, Zhou H, Lin F, Ko JJ, Herrera V. Real-world adherence and persistence to oral disease-modifying therapies in multiple sclerosis patients over 1 year. J Manag Care Spec Pharmacy. 2017;23(8):844–52.CrossRefGoogle Scholar
  20. 20.
    Caon C, Saunders C, Smrtka J, Baxter N, Shoemaker J. Injectable disease-modifying therapy for relapsing-remitting multiple sclerosis: a review of adherence data. J Neurosci Nurs. 2010;42(5):S5–9.CrossRefGoogle Scholar
  21. 21.
    Stirratt MJ, Dunbar-Jacob J, Crane HM, et al. Self-report measures of medication adherence behavior: recommendations on optimal use. Transl Behav Med. 2015;5(4):470–82.CrossRefGoogle Scholar
  22. 22.
    Devonshire V, Arbizu T, Borre B, et al. Patient-rated suitability of a novel electronic device for self-injection of subcutaneous interferon beta-1a in relapsing multiple sclerosis: an international, single-arm, multicentre, phase IIIb study. BMC Neurol. 2010;10:28.CrossRefGoogle Scholar
  23. 23.
    Treadaway K, Cutter G, Salter A, et al. Factors that influence adherence with disease-modifying therapy in MS. J Neurol. 2009;256(4):568–76.CrossRefGoogle Scholar
  24. 24.
    Devonshire V, Lapierre Y, Macdonell R, et al. The Global Adherence Project (GAP): a multicenter observational study on adherence to disease-modifying therapies in patients with relapsing-remitting multiple sclerosis. Eur J Neurol. 2011;18(1):69–77.CrossRefGoogle Scholar
  25. 25.
    Fernández O, Agüera J, Izquierdo G, et al. Adherence to interferon β-1b treatment in patients with multiple sclerosis in Spain. PLoS One. 2012;7(5):e35600.CrossRefGoogle Scholar
  26. 26.
    Remington G, Rodriguez Y, Logan D, Williamson C, Treadaway K. Facilitating medication adherence in patients with multiple sclerosis. Int J MS Care. 2013;15(1):36–45.CrossRefGoogle Scholar
  27. 27.
    Benedict RH, Duquin JA, Jurgensen S, et al. Repeated assessment of neuropsychological deficits in multiple sclerosis using the Symbol Digit Modalities Test and the MS Neuropsychological Screening Questionnaire. Mult Scler. 2008;14(7):940–6.CrossRefGoogle Scholar
  28. 28.
    Svenningsson A, Falk E, Celius EG, et al. Natalizumab treatment reduces fatigue in multiple sclerosis. results from the TYNERGY trial; a study in the real life setting. PLoS One. 2013;8(3):e58643.Google Scholar
  29. 29.
    Benedict RH, DeLuca J, Phillips G, LaRocca N, Hudson LD, Rudick R. Validity of the Symbol Digit Modalities Test as a cognition performance outcome measure for multiple sclerosis. Mult Scler J. 2017;23(5):721–33.CrossRefGoogle Scholar

Copyright information

© Springer Healthcare Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Peter Rieckmann
    • 1
  • Matthias Schwab
    • 2
  • Dieter Pöhlau
    • 3
  • Iris-Katharina Penner
    • 4
    • 5
  • Torsten Wagner
    • 6
  • Elke Schel
    • 6
  • Antonios Bayas
    • 7
  1. 1.Hospital for Nervous DiseasesMedical Park LoiplBischofswiesenGermany
  2. 2.Hans Berger Department of NeurologyUniversity Hospital JenaJenaGermany
  3. 3.Kamillus KlinikAsbachGermany
  4. 4.COGITO Center for Applied Neurocognition and Neuropsychological ResearchDüsseldorfGermany
  5. 5.Department of Neurology, Medical FacultyHeinrich-Heine UniversityDüsseldorfGermany
  6. 6.Merck Serono GmbHDarmstadtGermany
  7. 7.Department of NeurologyKlinikum AugsburgAugsburgGermany

Personalised recommendations