Multiple Sclerosis Data Alliance - A global multi-stakeholder collaboration to scale-up real world data research

Liesbet M Peeters; Tina Parciak; Dipak Kalra; Yves Moreau; Elisabeth Kasilingam; Pieter van Galen; Christoph Thalheim; Bernard Uitdehaag; Patrick Vermersch; Niels Hellings; Piet Stinissen; Bart Van Wijmeersch; Amin Ardeshirdavani; Ashkan Pirmani; Edward De Brouwer; Christian Robert Bauer; Dagmar Krefting; Stephanie Ribbe; Rod Middleton; Alexander Stahmann; Giancarlo Comi

doi:10.1016/j.msard.2020.102634

Multiple Sclerosis Data Alliance - A global multi-stakeholder collaboration to scale-up real world data research

Mult Scler Relat Disord. 2021 Jan:47:102634. doi: 10.1016/j.msard.2020.102634. Epub 2020 Nov 21.

Authors

Liesbet M Peeters¹, Tina Parciak², Dipak Kalra³, Yves Moreau⁴, Elisabeth Kasilingam⁵, Pieter van Galen⁵, Christoph Thalheim⁵, Bernard Uitdehaag⁶, Patrick Vermersch⁷, Niels Hellings⁸, Piet Stinissen⁸, Bart Van Wijmeersch⁸, Amin Ardeshirdavani⁴, Ashkan Pirmani⁹, Edward De Brouwer⁴, Christian Robert Bauer², Dagmar Krefting¹⁰, Stephanie Ribbe¹¹, Rod Middleton¹², Alexander Stahmann¹³, Giancarlo Comi¹⁴

Affiliations

¹ University MS Center, Biomedical Research Institute (BIOMED), Hasselt University, Agoralaan Building C, 3590 Diepenbeek, Belgium; Data Science Institute (DSI), Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium. Electronic address: [email protected].
² Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Straße 3, 37075, Göttingen, Germany.
³ Unit of Medical Informatics and Statistics, University of Gent, Gent, 9000, Belgium.
⁴ Department of Electrical Engineering (ESAT-STADIUS), KULeuven, Kasteelpark Arenberg, 10 3001 Leuven, Belgium.
⁵ European Multiple Sclerosis Platform, Rue A. Lambiotte, 1030, Brussels, Belgium.
⁶ Amsterdam UMC, Vrije Universiteit Amsterdam, Dept. of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, po Box 7057, 1007, MB, Amsterdam, The Netherlands.
⁷ University of Lille, CHU Lille, LIRIC-INSERM U995, Lille, France.
⁸ University MS Center, Biomedical Research Institute (BIOMED), Hasselt University, Agoralaan Building C, 3590 Diepenbeek, Belgium.
⁹ University MS Center, Biomedical Research Institute (BIOMED), Hasselt University, Agoralaan Building C, 3590 Diepenbeek, Belgium; Data Science Institute (DSI), Hasselt University, Agoralaan Building D, 3590, Diepenbeek, Belgium; Department of Electrical Engineering (ESAT-STADIUS), KULeuven, Kasteelpark Arenberg, 10 3001 Leuven, Belgium.
¹⁰ Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Straße 3, 37075, Göttingen, Germany; University of Applied Sciences Berlin, Germany.
¹¹ Novartis International AG. Forum 1. Novartis Campus CH-4056, Basel. Switzerland.
¹² UK MS Register, Swansea University, DSB, Swansea University, Singleton Park, Swansea, United Kingdom.
¹³ German MS Register, MS Forschungs- und Projektentwicklungs - gGmbH, Krausenstraße 50, 30171, Hannover, Germany.
¹⁴ Institute of Experimental Neurology, Ospedale San Raffaele, Via Olgettina, 48, 20132, Milan, Italy.

PMID: 33278741
DOI: 10.1016/j.msard.2020.102634

Abstract

The Multiple Sclerosis Data Alliance (MSDA), a global multi-stakeholder collaboration, is working to accelerate research insights for innovative care and treatment for people with multiple sclerosis (MS) through better use of real-world data (RWD). Despite the increasing reliance on RWD, challenges and limitations complicate the generation, collection, and use of these data. MSDA aims to tackle sociological and technical challenges arising with scaling up RWD, specifically focused on MS data. MSDA envisions a patient-centred data ecosystem in which all stakeholders contribute and use big data to co-create the innovations needed to advance timely treatment and care of people with MS.

Keywords: Real world data; collaboration; learning health system; multiple sclerosis; patient engagement.

Publication types

Letter

MeSH terms

Ecosystem
Humans
Multiple Sclerosis* / epidemiology
Multiple Sclerosis* / therapy
Research Design