The impact of healthcare systems on the clinical diagnosis and disease-modifying treatment usage in relapse-onset multiple sclerosis: a real-world perspective in five registries across Europe

Richard Nicholas; Jeff Rodgers; James Witts; Annalaura Lerede; Tim Friede; Jan Hillert; Lars Forsberg; Anna Glaser; Ali Manouchehrinia; Ryan Ramanujam; Tim Spelman; Pernilla Klyve; Jiri Drahota; Dana Horakova; Hanna Joensen; Luigi Pontieri; Melinda Magyari; David Ellenberger; Alexander Stahmann; Helmut Butzkueven; Anneke Van Der Walt; Vladimir Bezlyak; Carol Lines; Rod Middleton

doi:10.1177/17562864231198963

The impact of healthcare systems on the clinical diagnosis and disease-modifying treatment usage in relapse-onset multiple sclerosis: a real-world perspective in five registries across Europe

Ther Adv Neurol Disord. 2023 Sep 26:16:17562864231198963. doi: 10.1177/17562864231198963. eCollection 2023.

Authors

Richard Nicholas^{1

2

3}, Jeff Rodgers⁴, James Witts⁴, Annalaura Lerede², Tim Friede⁵, Jan Hillert⁶, Lars Forsberg⁶, Anna Glaser⁶, Ali Manouchehrinia⁷, Ryan Ramanujam^{7

8}, Tim Spelman⁹, Pernilla Klyve⁶, Jiri Drahota^{10

11}, Dana Horakova¹¹, Hanna Joensen¹², Luigi Pontieri¹², Melinda Magyari^{12

13}, David Ellenberger¹⁴, Alexander Stahmann¹⁴, Helmut Butzkueven¹⁵, Anneke Van Der Walt¹⁵, Vladimir Bezlyak¹⁶, Carol Lines¹⁶, Rod Middleton¹⁷

Affiliations

¹ Swansea University Medical School, Swansea, UK.
² Department of Cellular and Molecular Neuroscience, Imperial College London, London, UK.
³ Department of Visual Neuroscience, UCL Institute of Ophthalmology, London, UK.
⁴ Faculty of Medicine Health and Life Science, Swansea University Medical School, Swansea, UK.
⁵ Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
⁶ Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden.
⁷ Department of Clinical Neurosciences, Centre for Molecular Medicine (CMM), Karolinska Institute, Stockholm, Sweden.
⁸ Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden.
⁹ Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden, MS-Register.
¹⁰ Czech National Multiple Sclerosis Patient Registry ReMuS, IMPULS Endowment Fund, Kateřinská, CZ, Prague.
¹¹ Department of Neurology and Center of Clinical Neuroscience, Charles University in Prague, First Faculty of Medicine and General University Hospital, Prague, Czech Republic.
¹² Danish Multiple Sclerosis Registry, Department of Neurology, University Hospital Copenhagen, Rigshospitalet, Denmark.
¹³ Department of Neurology, Danish Multiple Sclerosis Center, University Hospital Copenhagen, Rigshospitalet, Denmark.
¹⁴ German MS Registry, MS Forschungs- und Projektentwicklungs-gGmbH, Hannover, Germany.
¹⁵ Department of Neuroscience, Central Clinical School, Monash University, Australia.
¹⁶ Novartis Pharma AG, Basel, Switzerland.
¹⁷ Faculty of Medicine Health and Life Science, Swansea University Medical School, Singleton Campus, Swansea SA2 8PP, UK.

Abstract

Introduction: Prescribing guidance for disease-modifying treatment (DMT) in multiple sclerosis (MS) is centred on a clinical diagnosis of relapsing-remitting MS (RRMS). DMT prescription guidelines and monitoring vary across countries. Standardising the approach to diagnosis of disease course, for example, assigning RRMS or secondary progressive MS (SPMS) diagnoses, allows examination of the impact of health system characteristics on the stated clinical diagnosis and treatment access.

Methods: We analysed registry data from six cohorts in five countries (Czech Republic, Denmark, Germany, Sweden and United Kingdom) on patients with an initial diagnosis of RRMS. We standardised our approach utilising a pre-existing algorithm (DecisionTree, DT) to determine patient diagnoses of RRMS or secondary progressive MS (SPMS). We identified five global drivers of DMT prescribing: Provision, Availability, Funding, Monitoring and Audit, data were analysed against these concepts using meta-analysis and univariate meta-regression.

Results: In 64,235 patients, we found variations in DMT use between countries, with higher usage in RRMS and lower usage in SPMS, with correspondingly lower usage in the UK compared to other registers. Factors such as female gender (p = 0.041), increasing disability via Expanded Disability Status Scale (EDSS) score (p = 0.004), and the presence of monitoring (p = 0.029) in SPMS influenced the likelihood of receiving DMTs. Standardising the diagnosis revealed differences in reclassification rates from clinical RRMS to DT-SPMS, with Sweden having the lowest rate Sweden (Sweden 0.009, range: Denmark 0.103 - UK portal 0.311). Those with higher EDSS at index (p < 0.03) and female gender (p < 0.049) were more likely to be reclassified from RRMS to DT-SPMS. The study also explored the impact of diagnosis on DMT usage in clinical SPMS, finding that the prescribing environment and auditing practices affected access to treatment.

Discussion: This highlights the importance of a healthcare system's approach to verifying the clinical label of MS course in facilitating appropriate prescribing, with some flexibility allowed in uncertain cases to ensure continued access to treatment.

Keywords: big data; clinical audit; decision tree; disease registers; international collaboration; multiple sclerosis.