Skip to main content
SpringerLink
Log in

Exit strategies for “needle fatigue” in multiple sclerosis: a propensity score-matched comparison study

  • Original Communication
  • Published:
Journal of Neurology Aims and scope Submit manuscript

Abstract

Patients with multiple sclerosis on long-term injectable therapies may suffer from the so-called “needle fatigue”, i.e., a waning commitment to continue with the prescribed injectable treatment. Therefore, alternative treatment strategies to enhance patients’ adherence are warranted. In this independent, multicentre post-marketing study, we sought to directly compare switching to either teriflunomide (TFN), dimethyl fumarate (DMF), or pegylated interferon (PEG) on treatment persistence and time to first relapse over a 12-month follow-up. We analyzed a total of 621 patients who were free of relapses and gadolinium-enhancing lesions in the year prior to switching to DMF (n = 265), TFN (n = 160), or PEG (n = 196). Time to discontinuation and time to first relapse were explored in the whole population by Cox regression models adjusted for baseline variables and after a 1:1:1 ratio propensity score (PS)-based matching procedure. Treatment discontinuation was more frequent after switching to PEG (28.6%) than DMF (14.7%; hazard ratio [HR] = 0.25, p < 0.001) and TFN (16.9%; HR = 0.27, p < 0.001). We found similar results even in the re-sampled cohort of 222 patients (74 per group) derived by the PS-based matching procedure. The highest discontinuation rate observed in PEG recipient was mainly due to poor tolerability (p = 0.005) and pregnancy planning (p = 0.04). The low number of patients who relapsed over the 12-month follow-up (25 out of 621, approximately 4%) prevented any analysis on the short-term risk of relapse. This real-world study suggests that oral drugs are a better switching option than low-frequency interferon for promoting the short-term treatment persistence in stable patients who do not tolerate injectable drugs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Thompson AJ, Baranzini SE, Geurts J, Hemmer B, Ciccarelli O (2018) Multiple sclerosis. Lancet 391:1622–1636

    Article  Google Scholar 

  2. Sabaté E (2003) Adherence to long-term therapies: evidence for action. WHO, Geneva

    Google Scholar 

  3. Rieckmann P, Boyko A, Centonze D et al (2015) Achieving patient engagement in multiple sclerosis: a perspective from the multiple sclerosis in the 21st century steering group. Mult Scler Relat Disord 4:202–218

    Article  Google Scholar 

  4. Clerico M, Barbero P, Contessa G, Ferrero C, Durelli L (2007) Adherence to interferon-β treatment and results of therapy switching. J Neurol Sci 259:104–108

    Article  CAS  Google Scholar 

  5. McKay KA, Tremlett H, Patten SB, CIHR Team in the Epidemiology and Impact of Comorbidity on Multiple Sclerosis et al (2017) Determinants of non-adherence to disease-modifying therapies in multiple sclerosis: a cross-Canada prospective study. Mult Scler 23:588–596

    Article  Google Scholar 

  6. Klauer T, Zettl UK (2008) Compliance, adherence, and the treatment of multiple sclerosis. J Neurol 7(suppl 6):87–92

    Article  Google Scholar 

  7. Treadaway K, Cutter G, Salter A et al (2009) Factors that influence adherence with disease-modifying therapies in MS. J Neurol 256:568–576

    Article  Google Scholar 

  8. Devonshire V, Lapierre Y, Macdonell R, GAP Study Group et al (2011) The Global Adherence Project: a multicenter observational study on adherence to disease-modifying therapies in patients with relapsing-remitting multiple sclerosis. Eur J Neurol 18:69–77

    Article  CAS  Google Scholar 

  9. Fernandez O, Aguera E, Izquierdo G, Group on Adherence to IFNB-1β in Spain et al (2012) Adherence to interferon-β1b treatment in patients with multiple sclerosis in Spain. PLoS ONE 7:e35600

    Article  CAS  Google Scholar 

  10. Nichols C, White LA, Friedman M, Pill MW (2013) Narrative review of the literature on adherence to disease-modifying therapies among patients with multiple sclerosis. J Manag Care Pharm 19(1 Suppl A):S24–S40

    PubMed  Google Scholar 

  11. Conway DS, Cecilia Vieira M, Thompson NR et al (2018) Patient-reported disease-modifying therapy adherence in the clinic: a reliable metric? Mult Scler J Exp Transl Clin 4:2055217318777894

    PubMed  PubMed Central  Google Scholar 

  12. Steinberg SC, Faris RJ, Chang CF, Chan A, Tankersley MA (2010) Impact of adherence to interferons in the treatment of multiple sclerosis: a non-experimental, retrospective, cohort study. Clin Drug Investig 30:89–100

    Article  CAS  Google Scholar 

  13. Tan H, Cai Q, Agarwal S, Stephenson JJ, Kamat S (2011) Impact of adherence to disease-modifying therapies on clinical and economic outcomes among patients with multiple sclerosis. Adv Ther 28:51–61

    Article  Google Scholar 

  14. Barbero P, Verdun E, Bergui M et al (2004) High-dose, frequently administered interferon-β therapy for relapsing-remitting multiple sclerosis must be maintained over the long term: the interferon-β dose-reduction study. J Neurol Sci 222:13–19

    Article  CAS  Google Scholar 

  15. Prosperini L, Mancinelli CR, Pozzilli C et al (2014) From high-to low-frequency administered interferon-beta for multiple sclerosis: a multicenter study. Eur Neurol 71:233–241

    Article  CAS  Google Scholar 

  16. Zhornitsky S, Greenfield J, Koch MW et al (2015) Long-term persistence with injectable therapy in relapsing-remitting multiple sclerosis: an 18-year observational cohort study. PLoS ONE 10:e0123824

    Article  Google Scholar 

  17. Caporro M, Disanto G, Gobbi C, Zecca C (2014) Two decades of subcutaneous glatiramer acetate injection: current role of the standard dose, and new high-dose low-frequency glatiramer acetate in relapsing-remitting multiple sclerosis treatment. Patient Prefer Adherence 8:1123–1134

    PubMed  PubMed Central  Google Scholar 

  18. Lanzillo R, Prosperini L, Gasperini C, R.I. Re.MS study group et al (2018) A multicentRE observational analysiS of PErsistenCe to Treatment in the new multiple sclerosis era: the RESPECT study. J Neurol 265:1174–1183

    Article  Google Scholar 

  19. Saccà F, Lanzillo R, Signori A, iMUST Group et al (2018) Determinants of therapy switch in multiple sclerosis treatment-naïve patients: a real-life study. Mult Scler. https://doi.org/10.1177/1352458518790390

    Article  PubMed  Google Scholar 

  20. Setayeshgar S, Kingwell E, Zhu F et al (2019) Persistence and adherence to the new oral disease-modifying therapies for multiple sclerosis: a population-based study. Mult Scler Relat Disord 27:364–369

    Article  Google Scholar 

  21. Percorso Diagnostico Terapeutico Assistenziale (PDTA) Regione Lazio. regione.lazio.it/binary/rl_sanita/tbl_normativa/SAN_DCA_U00386_13_11_2014.pdf. Accessed 28 Sep 2018

  22. Thompson AJ, Banwell BL, Barkhof F et al (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17:162–173

    Article  Google Scholar 

  23. Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology 1983(33):1444–1452

    Article  Google Scholar 

  24. Trojano M, Pellegrini F, Paolicelli D, Fuiani A, Di Renzo V (2009) Observational studies: propensity score analysis of non-randomized data. Int MS J 16:90–97

    CAS  PubMed  Google Scholar 

  25. Rassen JA, Solomon DH, Glynn RJ, Schneeweiss S (2011) Simultaneously assessing intended and unintended treatment effects of multiple treatment options: a pragmatic ‘‘matrix design’’. Pharmacoepidemiol Drug Saf 20:675–683

    Article  Google Scholar 

  26. Austin PC (2009) Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med 28:3083–3107

    Article  Google Scholar 

  27. Cummings P, McKnight B, Greenland S (2003) Matched cohort methods for injury research. Epidemiol Rev 25:43–50

    Article  Google Scholar 

  28. Greenland S (1996) Basic methods for sensitivity analysis of biases. Int J Epidemiol 25:1107–1116

    Article  CAS  Google Scholar 

  29. Girouard N, Soucy N (2011) Patient considerations in the management of multiple sclerosis: development and clinical utility of oral agents. Patient Prefer Adherence 5:101–108

    PubMed  PubMed Central  Google Scholar 

  30. Verdugo RM, Herráiz ER, Fernández-Del Olmo R, Bonet MR, García MV (2019) Adherence to disease-modifying treatments in patients with multiple sclerosis in Spain. Patient Prefer Adherence 13:261–272

    Article  Google Scholar 

  31. Hendin BA (2018) Peg-interferon beta-1a for the treatment of relapsing multiple sclerosis: a case series. Mult Scler Relat Disord 26:33–36

    Article  Google Scholar 

  32. Duquette P, Yeung M, Mouallif S et al (2019) A retrospective claims analysis: compliance and discontinuation rates among Canadian patients with multiple sclerosis treated with disease-modifying therapies. PLoS ONE 14:e0210417

    Article  CAS  Google Scholar 

  33. D’Amico E, Zanghì A, Callari G et al (2018) Comparable efficacy and safety of dimethyl fumarate and teriflunomide treatment in Relapsing-Remitting Multiple Sclerosis: an Italian real-word multicenter experience. Ther Adv Neurol Disord. 10(11):1756286418796404

    Google Scholar 

  34. D’Amico E, Zanghì A, Sciandra M et al (2019) Discontinuation of teriflunomide and dimethyl fumarate in a large Italian multicentre population: a 24-month real-world experience. J Neurol 266:411–416

    Article  Google Scholar 

  35. Kalincik T, Havrdova E, Horakova D et al (2019) Comparison of fingolimod, dimethyl fumarate and teriflunomide for multiple sclerosis. J Neurol Neurosurg Psychiatry 90:458–468

    Article  Google Scholar 

  36. Laplaud DA, Casey R, Barbin L, SFSEP and OFSEP groups et al (2019) Comparative effectiveness of teriflunomide vs dimethyl fumarate in multiple sclerosis. Neurology. https://doi.org/10.1212/WNL.0000000000007938

    Article  PubMed  PubMed Central  Google Scholar 

  37. Kalincik T, Sormani MP (2017) Reporting treatment outcomes in observational data: a fine balance. Mult Scler 23:21–22

    Article  Google Scholar 

  38. Rosenbaum PR, Rubin DB (1985) Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat 39:33–38

    Google Scholar 

  39. Austin PC, Grootendorst P, Anderson GM (2007) A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med 26:734–753

    Article  Google Scholar 

  40. Bergstra SA, Sepriano A, Ramiro S, Landewé R (2019) Three handy tips and a practical guide to improve your propensity score models. RMD Open 5:e000953

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Neurosciences, S. Camillo-Forlanini Hospital, C.ne Gianicolense 87, 00152, Rome, Italy

    Luca Prosperini, Simonetta Galgani, Claudio Gasperini, Shalom Haggiag, Serena Ruggieri & Carla Tortorella

  2. San Filippo Neri Hospital, ASL Roma 1, Via G. Martinotti 20, 00135, Rome, Italy

    Antonio Cortese, Elisabetta Ferraro & Giancarlo Di Battista

  3. Department of Human Neurosciences, Sapienza University, Viale dell’Università 30, 00185, Rome, Italy

    Antonio Cortese, Enrico Millefiorini, Simona Pontecorvo & Serena Ruggieri

  4. Fondazione Policlinico Universitario ‘A. Gemelli’ IRCCS, Largo A. Gemelli 8, 00168, Rome, Italy

    Matteo Lucchini, Chiara De Fino, Massimiliano Mirabella & Viviana Nociti

  5. Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, 00168, Rome, Italy

    Matteo Lucchini, Massimiliano Mirabella & Viviana Nociti

  6. MS Clinical and Research Unit, Department of Systems Medicine, Tor Vergata University, Viale Oxford 81, 00133, Rome, Italy

    Laura Boffa, Doriana Landi, Girolama Marfia, Giorgia Mataluni & Fabrizia Monteleone

  7. MS Centre, S. Andrea Hospital, Viale di Grottarossa 1035, 00189, Rome, Italy

    Giovanna Borriello

  8. Department of Neurosciences, Mental Health and Sensory Organs (NESMOS), S. Andrea Hospital, Sapienza University, Viale di Grottarossa 1035, 00189, Rome, Italy

    Maria Chiara Buscarinu, Silvia Romano & Marco Salvetti

  9. Unit of Neurology Neurophysiology, Department of Medicine, Campus Bio-Medico University, Via Álvaro del Portillo 21, 00128, Rome, Italy

    Fioravante Capone

  10. NeXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit, Campus Bio-Medico University, Via Álvaro del Portillo 21, 00128, Rome, Italy

    Fioravante Capone

  11. IRCCS Neuromed, Via Atinense 18, 86077, Pozzilli, IS, Italy

    Diego Centonze, Roberta Fantozzi, Girolama Marfia & Marco Salvetti

  12. Laboratory of Synaptic Immunopathology, Department of Systems Medicine, Tor Vergata University, Viale Oxford 81, 00133, Rome, Italy

    Diego Centonze

  13. Neurology Unit/Stroke Unit, Department of Emergency, MS Centre, S. Eugenio Hospital, P.le dell’Umanesimo 10, 00144, Rome, Italy

    Daniela De Pascalis

  14. Fatebenefratelli Foundation for Health Research and Education, AFaR Division, Via di Ponte Quattro Capi 39, 00186, Rome, Italy

    Maria Filippi

  15. Neurology Unit/Stroke Unit, S. Giovanni Addolorata Hospital, Via dell’Amba Aradam 9, 00184, Rome, Italy

    Simona Pontecorvo

  16. Department of Clinical Experimental Oncology, IRCCS Regina Elena National Cancer Institute, Via Elio Chianesi 53, 00128, Rome, Italy

    Silvana Zannino

Authors
  1. Luca Prosperini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonio Cortese
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matteo Lucchini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laura Boffa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Giovanna Borriello
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maria Chiara Buscarinu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Fioravante Capone
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Diego Centonze
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Chiara De Fino
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Daniela De Pascalis
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Roberta Fantozzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Elisabetta Ferraro
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Maria Filippi
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Simonetta Galgani
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Claudio Gasperini
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Shalom Haggiag
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Doriana Landi
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Girolama Marfia
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Giorgia Mataluni
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Enrico Millefiorini
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Massimiliano Mirabella
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Fabrizia Monteleone
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Viviana Nociti
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Simona Pontecorvo
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Silvia Romano
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Serena Ruggieri
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Marco Salvetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Carla Tortorella
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Silvana Zannino
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Giancarlo Di Battista
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luca Prosperini.

Ethics declarations

Conflicts of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical statement

The present study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The local ethical committee boards provided exemption of approval for non-interventional studies. We obtained an informed consent from each participant prior to any study procedure. In no way, this study did interfere in the care received by patients.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 538 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prosperini, L., Cortese, A., Lucchini, M. et al. Exit strategies for “needle fatigue” in multiple sclerosis: a propensity score-matched comparison study. J Neurol 267, 694–702 (2020). https://doi.org/10.1007/s00415-019-09625-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00415-019-09625-1

Keywords

Search

Navigation