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

Peeters, Liesbet M.; Parciak, Tina; Moreau, Yves; Kasilingam, Elisabeth; Galen, Pieter van; Thalheim, Christoph; Uitdehaag, Bernard M. J.; Vermersch, Patrick; Hellings, Niels; Stinissen, Piet; Wijmeersch, Bart Van; Ardeshirdavani, Amin; Pirmani, Ashkan; Brouwer, Edward De; Bauer, Christian R.; Krefting, Dagmar; Ribbe, Stephanie; Middleton, Rod; Stahmann, Alexander; Comı, Gıancarlo

doi:10.1016/j.msard.2020.102634

Cited by 14 publications

(8 citation statements)

References 5 publications

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“…The MS Data Alliance has already developed digital tools for aggregating, harmonizing, and sharing real-world data from multiple sources by creating a common data model. EHR also play a critical role in standardized and accurate digital documentation of clinical data, and several of these already exist, such as the MS BRIDGE, RC2NB, MSDS3D and MSBase EHR systems ( 168 ).…”

Section: Concept Of Digital Twins In the Management Of Multiple Sclerosismentioning

confidence: 99%

Digital Twins for Multiple Sclerosis

et al. 2021

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An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient’s characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters – including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient’s life circumstances and plans, and medical procedures – a digital twin paired to the patient’s characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients’ well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.

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Section: Concept Of Digital Twins In the Management Of Multiple Sclerosismentioning

confidence: 99%

Digital Twins for Multiple Sclerosis

et al. 2021

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“…Fortunately, advances in clinical practice and clinical data acquisition standards now facilitate the collection of large amounts of longitudinal data, both in terms of number of patients, but also in the number of clinical variables collected on a systematic basis. The MS community is particularly prolific in this regard with multiple international consortia, such as the MS Data Alliance [14,15], MSBase [16,17], Multiple MS, or Big MS Network, as well as large registries, such as the Danish, Swedish and Italian registries [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

Brouwer

Becker

Moreau

et al. 2021

Computer Methods and Programs in Biomedicine

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Background and Objectives.Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem. MethodsWe use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6,682 patients from the MSBase registry is used. ResultsWe can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.67, which represents a 11% decrease in the ranking pair error (1-AUC) and a compared to reference methods using static clinical features. ConclusionCompared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS.

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“…We also see an orange cluster made of other European countries, e.g., the UK, Italy, and the Netherlands. Over the last decade, several MS societies around the globe have been collaborating in data collection to accelerate research insights into innovative care and treatment for people with MS through better use of real-world data ( 16 , 17 ).…”

Section: Resultsmentioning

confidence: 99%

A Quantitative Synthesis of Eight Decades of Global Multiple Sclerosis Research Using Bibliometrics

Ismail

Saqr

2022

Front. Neurol.

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Bibliometric studies on the field of multiple sclerosis (MS) research are scarce. The aim of this study is to offer an overarching view of the body of knowledge about MS research over eight decades–from 1945 to 2021–by means of a bibliometric analysis. We performed a quantitative analysis of a massive dataset based on Web of Science. The analysis included frequencies, temporal trends, collaboration networks, clusters of research themes, and an in-depth qualitative analysis. A total of 48,356 articles, with 1,766,086 citations were retrieved. Global MS research showed a steady increase with an annual growth rate of 6.4%, with more than half of the scientific production published in the last decade. Published articles came from 98 different countries by 123,569 authors in 3,267 journals, with the United States ranking first in a number of publications (12,770) and citations (610,334). A co-occurrence network analysis formed four main themes of research, covering the pathophysiological mechanisms, neuropsychological symptoms, diagnostic modalities, and treatment of MS. A noticeable increase in research on cognition, depression, and fatigue was observed, highlighting the increased attention to the quality of life of patients with MS. This bibliometric analysis provided a comprehensive overview of the status of global MS research over the past eight decades. These results could provide a better understanding of this field and help identify new directions for future research.

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

Cited by 14 publications

References 5 publications

Digital Twins for Multiple Sclerosis

Digital Twins for Multiple Sclerosis

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

A Quantitative Synthesis of Eight Decades of Global Multiple Sclerosis Research Using Bibliometrics

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