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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 Orcid Logo, 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 Orcid Logo, Anneke Van Der Walt Orcid Logo, Vladimir Bezlyak, Carol Lines, Rod Middleton Orcid Logo

Therapeutic Advances in Neurological Disorders, Volume: 16

Swansea University Author: Rod Middleton Orcid Logo

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DOI (Published version): 10.1177/17562864231198963

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 exa...

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Published in: Therapeutic Advances in Neurological Disorders
ISSN: 1756-2864 1756-2864
Published: SAGE Publications 2023
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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 &lt; 0.03) and female gender (p &lt; 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.</abstract><type>Journal Article</type><journal>Therapeutic Advances in Neurological Disorders</journal><volume>16</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>SAGE Publications</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1756-2864</issnPrint><issnElectronic>1756-2864</issnElectronic><keywords>big data; clinical audit; decision tree; disease registers; international collaboration; multiple sclerosis</keywords><publishedDay>1</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-12-01</publishedDate><doi>10.1177/17562864231198963</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>Not Required</apcterm><funders>This study was wholly funded by Novartis AG.</funders><projectreference/><lastEdited>2024-10-29T13:30:35.7041986</lastEdited><Created>2024-05-14T10:25:14.8680899</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Richard</firstname><surname>Nicholas</surname><order>1</order></author><author><firstname>Jeff</firstname><surname>Rodgers</surname><order>2</order></author><author><firstname>James</firstname><surname>Witts</surname><order>3</order></author><author><firstname>Annalaura</firstname><surname>Lerede</surname><order>4</order></author><author><firstname>Tim</firstname><surname>Friede</surname><orcid>0000-0001-5347-7441</orcid><order>5</order></author><author><firstname>Jan</firstname><surname>Hillert</surname><order>6</order></author><author><firstname>Lars</firstname><surname>Forsberg</surname><order>7</order></author><author><firstname>Anna</firstname><surname>Glaser</surname><order>8</order></author><author><firstname>Ali</firstname><surname>Manouchehrinia</surname><order>9</order></author><author><firstname>Ryan</firstname><surname>Ramanujam</surname><order>10</order></author><author><firstname>Tim</firstname><surname>Spelman</surname><order>11</order></author><author><firstname>Pernilla</firstname><surname>Klyve</surname><order>12</order></author><author><firstname>Jiri</firstname><surname>Drahota</surname><order>13</order></author><author><firstname>Dana</firstname><surname>Horakova</surname><order>14</order></author><author><firstname>Hanna</firstname><surname>Joensen</surname><order>15</order></author><author><firstname>Luigi</firstname><surname>Pontieri</surname><order>16</order></author><author><firstname>Melinda</firstname><surname>Magyari</surname><order>17</order></author><author><firstname>David</firstname><surname>Ellenberger</surname><order>18</order></author><author><firstname>Alexander</firstname><surname>Stahmann</surname><order>19</order></author><author><firstname>Helmut</firstname><surname>Butzkueven</surname><orcid>0000-0003-3940-8727</orcid><order>20</order></author><author><firstname>Anneke Van Der</firstname><surname>Walt</surname><orcid>0000-0002-4278-7003</orcid><order>21</order></author><author><firstname>Vladimir</firstname><surname>Bezlyak</surname><order>22</order></author><author><firstname>Carol</firstname><surname>Lines</surname><order>23</order></author><author><firstname>Rod</firstname><surname>Middleton</surname><orcid>0000-0002-2130-4420</orcid><order>24</order></author></authors><documents><document><filename>66408__30348__115a36b71885460587db50598fcf9d7b.pdf</filename><originalFilename>66408.pdf</originalFilename><uploaded>2024-05-14T10:32:52.3382957</uploaded><type>Output</type><contentLength>1231513</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© The Author(s), 2023. 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spelling v2 66408 2024-05-14 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 005518f819ef1a2a13fdf438529bdfcd 0000-0002-2130-4420 Rod Middleton Rod Middleton true false 2024-05-14 MEDS 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. Journal Article Therapeutic Advances in Neurological Disorders 16 SAGE Publications 1756-2864 1756-2864 big data; clinical audit; decision tree; disease registers; international collaboration; multiple sclerosis 1 12 2023 2023-12-01 10.1177/17562864231198963 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University Not Required This study was wholly funded by Novartis AG. 2024-10-29T13:30:35.7041986 2024-05-14T10:25:14.8680899 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Richard Nicholas 1 Jeff Rodgers 2 James Witts 3 Annalaura Lerede 4 Tim Friede 0000-0001-5347-7441 5 Jan Hillert 6 Lars Forsberg 7 Anna Glaser 8 Ali Manouchehrinia 9 Ryan Ramanujam 10 Tim Spelman 11 Pernilla Klyve 12 Jiri Drahota 13 Dana Horakova 14 Hanna Joensen 15 Luigi Pontieri 16 Melinda Magyari 17 David Ellenberger 18 Alexander Stahmann 19 Helmut Butzkueven 0000-0003-3940-8727 20 Anneke Van Der Walt 0000-0002-4278-7003 21 Vladimir Bezlyak 22 Carol Lines 23 Rod Middleton 0000-0002-2130-4420 24 66408__30348__115a36b71885460587db50598fcf9d7b.pdf 66408.pdf 2024-05-14T10:32:52.3382957 Output 1231513 application/pdf Version of Record true © The Author(s), 2023. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License. true eng http://creativecommons.org/licenses/by-nc/4.0/
title 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
spellingShingle 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
Rod Middleton
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
author_id_str_mv 005518f819ef1a2a13fdf438529bdfcd
author_id_fullname_str_mv 005518f819ef1a2a13fdf438529bdfcd_***_Rod Middleton
author Rod Middleton
author2 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
format Journal article
container_title Therapeutic Advances in Neurological Disorders
container_volume 16
publishDate 2023
institution Swansea University
issn 1756-2864
1756-2864
doi_str_mv 10.1177/17562864231198963
publisher SAGE Publications
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
active_str 0
description 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.
published_date 2023-12-01T13:30:34Z
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