Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/42481
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dc.contributor.authorKHAN, Hamza-
dc.contributor.authorGEYS, Lotte-
dc.contributor.authorBaneke, Peer-
dc.contributor.authorComi, Giancarlo-
dc.contributor.authorPEETERS, Liesbet-
dc.date.accessioned2024-02-27T12:48:31Z-
dc.date.available2024-02-27T12:48:31Z-
dc.date.issued2024-
dc.date.submitted2024-02-27T12:23:49Z-
dc.identifier.citationScientific Data, 11 (1) (Art N° 149)-
dc.identifier.issn-
dc.identifier.urihttp://hdl.handle.net/1942/42481-
dc.description.abstractMultiple Sclerosis (MS) is an inflammatory autoimmune disease of the central nervous system, causing increased vulnerability to infections and disability among young adults. Ever since the outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infections, there have been concerns among people with MS (PwMS) about the potential interactions between various disease-modifying therapies and COVID-19. The COVID-19 in MS Global Data Sharing Initiative (GDSI) was initiated in 2020 with the aim of addressing these concerns. This paper focuses on the anonymisation and publicly releasing of a GDSI sub-dataset, comprising data entered by PwMS and clinicians using a fast data entry tool. The dataset includes information on demographics, comorbidities and hospital stay and COVID-19 symptoms of PwMS. The dataset can be used to perform different statistical analyses to improve our understanding of COVID-19 in MS. Furthermore, this dataset can also be used within the context of educational activities to educate different stakeholders on the complex data science topics that were used within the GDSI.-
dc.description.sponsorshipAcknowledgements We would like to thank all people with MS and clinicians for their time invested in providing the data within the context of the GDSI. We are grateful for the continued support of the sponsors of the Multiple Sclerosis Data Alliance and Multiple Sclerosis International Federation. Furthermore, this work was supported by the Flemish government through the Onderzoeksprogramma Artifciële Intelligentie Vlaanderen program. Finally, we thank QMENTA for the use of the central data platform.-
dc.language.isoen-
dc.publisherNATURE PORTFOLIO-
dc.rightsThe Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.-
dc.subject.otherHumans-
dc.subject.otherYoung Adult-
dc.subject.otherCentral Nervous System-
dc.subject.otherData Science-
dc.subject.otherDisease Outbreaks-
dc.subject.otherCOVID-19-
dc.subject.otherMultiple Sclerosis-
dc.titlePatient level dataset to study the effect of COVID-19 in people with Multiple Sclerosis-
dc.typeJournal Contribution-
dc.identifier.issue1-
dc.identifier.volume11-
local.format.pages11-
local.bibliographicCitation.jcatA1-
dc.description.notesPeeters, LM (corresponding author), Univ MS Ctr UMSC, Hasselt, Pelt, Belgium.; Peeters, LM (corresponding author), UHasselt, Biomed Res Inst BIOMED, B-3590 Diepenbeek, Belgium.; Peeters, LM (corresponding author), UHasselt, Data Sci Inst DSI, B-3590 Diepenbeek, Belgium.-
dc.description.notesliesbet.peeters@uhasselt.be-
local.publisher.placeHEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.bibliographicCitation.artnr149-
dc.identifier.doi10.1038/s41597-024-02978-x-
dc.identifier.pmid38297080-
dc.identifier.isi001155587100002-
dc.contributor.orcidKHAN, Hamza/0000-0002-0398-4848-
local.provider.typewosris-
local.description.affiliation[Khan, Hamza; Geys, Lotte; Peeters, Liesbet M.] Univ MS Ctr UMSC, Hasselt, Pelt, Belgium.-
local.description.affiliation[Khan, Hamza; Geys, Lotte; Peeters, Liesbet M.] UHasselt, Biomed Res Inst BIOMED, B-3590 Diepenbeek, Belgium.-
local.description.affiliation[Khan, Hamza; Geys, Lotte; Peeters, Liesbet M.] UHasselt, Data Sci Inst DSI, B-3590 Diepenbeek, Belgium.-
local.description.affiliation[Khan, Hamza] Maastricht Univ, GROW Sch Oncol, Dept Precis Med, D Lab, Maastricht, Netherlands.-
local.description.affiliation[Baneke, Peer] Multiple Sclerosis Int Federat, London, England.-
local.description.affiliation[Comi, Giancarlo] Sci Inst S Raffaele, Dept Neurol, Via Olgettina 48, I-20132 Milan, Italy.-
local.dataset.doihttps://doi.org/10.13026/77ta-1866.-
local.uhasselt.internationalyes-
item.accessRightsOpen Access-
item.fullcitationKHAN, Hamza; GEYS, Lotte; Baneke, Peer; Comi, Giancarlo & PEETERS, Liesbet (2024) Patient level dataset to study the effect of COVID-19 in people with Multiple Sclerosis. In: Scientific Data, 11 (1) (Art N° 149).-
item.fulltextWith Fulltext-
item.contributorKHAN, Hamza-
item.contributorGEYS, Lotte-
item.contributorBaneke, Peer-
item.contributorComi, Giancarlo-
item.contributorPEETERS, Liesbet-
crisitem.journal.eissn2052-4463-
Appears in Collections:Research publications
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