Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/42542
Title: | Patient-level dataset to study the effect of COVID-19 in people with Multiple Sclerosis | Data Creator - person: | KHAN, Hamza GEYS, Lotte baneke, peer Comi, Giancarlo PEETERS, Liesbet |
Data Creator - organization: | Hasselt University Multiple Sclerosis International Federation, London, United Kingdom Department of Neurology, Scientific Institute S. Raffaele Multiple Sclerosis International Federation, London, United Kingdom Department of Neurology, Scientific Institute S. Raffaele |
Data Curator - person: | KHAN, Hamza | Data Curator - organization: | Hasselt University Multiple Sclerosis International Federation, London, United Kingdom Department of Neurology, Scientific Institute S. Raffaele Multiple Sclerosis International Federation, London, United Kingdom Department of Neurology, Scientific Institute S. Raffaele |
Rights Holder - person: | KHAN, Hamza | Rights Holder - organization: | Hasselt University | Publisher: | PhysioNet | Issue Date: | 2024 | Abstract: | Multiple 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 coronavirus disease 2019 (COVID-19) outbreak, 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 to address these concerns. This paper focuses on the anonymisation and open-sourcing of a GDSI sub-dataset, comprising data entered by people with MS and clinicians using a fast data entry tool. The dataset includes demographics, comorbidities, 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. | Research Discipline: | Medical and health sciences > Basic sciences > Immunology > Autoimmunity (03011404) | Keywords: | multiple sclerosis;covid-19;viral infection | DOI: | 10.13026/77ta-1866 | Link to publication/dataset: | https://physionet.org/content/patient-level-data-covid-ms/1.0.1/ | Source: | PhysioNet. 10.13026/77ta-1866 https://physionet.org/content/patient-level-data-covid-ms/1.0.1/ | Publications related to the dataset: | 10.1038/s41597-024-02978-x | License: | Creative Commons Attribution 4.0 International (CC-BY-4.0) | Access Rights: | Open Access | Version: | 1.0.1 | Category: | DS | Type: | Dataset |
Appears in Collections: | Datasets |
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.