1
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Parciak T, Geys L, Helme A, van der Mei I, Hillert J, Schmidt H, Salter A, Zakaria M, Middleton R, Stahmann A, Dobay P, Hernandez Martinez-Lapiscina E, Iaffaldano P, Plueschke K, Rojas JI, Sabidó M, Magyari M, van der Walt A, Arickx F, Comi G, Peeters LM. Introducing a core dataset for real-world data in multiple sclerosis registries and cohorts: Recommendations from a global task force. Mult Scler 2024; 30:396-418. [PMID: 38140852 PMCID: PMC10935622 DOI: 10.1177/13524585231216004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/09/2023] [Accepted: 10/23/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND As of September 2022, there was no globally recommended set of core data elements for use in multiple sclerosis (MS) healthcare and research. As a result, data harmonisation across observational data sources and scientific collaboration is limited. OBJECTIVES To define and agree upon a core dataset for real-world data (RWD) in MS from observational registries and cohorts. METHODS A three-phase process approach was conducted combining a landscaping exercise with dedicated discussions within a global multi-stakeholder task force consisting of 20 experts in the field of MS and its RWD to define the Core Dataset. RESULTS A core dataset for MS consisting of 44 variables in eight categories was translated into a data dictionary that has been published and disseminated for emerging and existing registries and cohorts to use. Categories include variables on demographics and comorbidities (patient-specific data), disease history, disease status, relapses, magnetic resonance imaging (MRI) and treatment data (disease-specific data). CONCLUSION The MS Data Alliance Core Dataset guides emerging registries in their dataset definitions and speeds up and supports harmonisation across registries and initiatives. The straight-forward, time-efficient process using a dedicated global multi-stakeholder task force has proven to be effective to define a concise core dataset.
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Affiliation(s)
- Tina Parciak
- University MS Center (UMSC), Hasselt-Pelt, Belgium
- UHasselt, Biomedical Research Institute (BIOMED), Diepenbeek, Belgium
- UHasselt, Data Science Institute (DSI), Diepenbeek, Belgium
| | - Lotte Geys
- University MS Center (UMSC), Hasselt-Pelt, Belgium
- UHasselt, Biomedical Research Institute (BIOMED), Diepenbeek, Belgium
- UHasselt, Data Science Institute (DSI), Diepenbeek, Belgium
| | - Anne Helme
- Multiple Sclerosis International Federation, London, UK
| | - Ingrid van der Mei
- Menzies Institute for Medical Research, University of Tasmania, The Australian MS longitudinal study (AMSLS), Hobart, TAS, Australia
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Hollie Schmidt
- Accelerated Cure Project, iConquerMS People-Powered Research Network, Waltham, MA, USA
| | - Amber Salter
- Section on Statistical Planning and Analysis, UT Southwestern Medical Center, NARCOMS Registry, COViMS Registry, Dallas, TX, USA
| | - Magd Zakaria
- Department of Neurology, Ain Shams University, Cairo, Egypt
| | - Rodden Middleton
- Population Data Science, Swansea University Medical School, Swansea, UK
| | - Alexander Stahmann
- German MS Register by the German MS Society, MS Research and Project Development gGmbH (MSFP), Hanover, Germany
| | | | - Elena Hernandez Martinez-Lapiscina
- Office of Therapies for Neurological and Psychiatric Disorders (H-NEU), Human Medicines (H-Division), European Medicines Agency, Amsterdam, The Netherlands
| | - Pietro Iaffaldano
- Department of Translational Biomedicine and Neurosciences (DiBraiN), Università degli Studi di Bari Aldo Moro, Italian MS registry, Bari, Italy
| | - Kelly Plueschke
- Data Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands
| | - Juan I Rojas
- Neurology Department, Hospital Universitario de CEMIC, RelevarEM, Buenos Aires, Argentina
| | - Meritxell Sabidó
- Department of Epidemiology, Merck Healthcare KGaA, Darmstadt, Germany
| | - Melinda Magyari
- Danish Multiple Sclerosis Registry and Danish Multiple Sclerosis Center, Department of Neurology, Copenhagen University Hospital – Rigshospitalet, Glostrup, Denmark
| | - Anneke van der Walt
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Francis Arickx
- National Institute for Health and Disability Insurance, Brussels, Belgium
| | - Giancarlo Comi
- Department of Rehabilitation Neurosciences, Casa di Cura Igea, Milan, Italy
| | - Liesbet M Peeters
- University MS Center (UMSC), Hasselt-Pelt, Belgium
- UHasselt, Biomedical Research Institute (BIOMED), Diepenbeek, Belgium
- UHasselt, Data Science Institute (DSI), Diepenbeek, Belgium
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2
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van Velzen M, de Graaf-Waar HI, Ubert T, van der Willigen RF, Muilwijk L, Schmitt MA, Scheper MC, van Meeteren NLU. 21st century (clinical) decision support in nursing and allied healthcare. Developing a learning health system: a reasoned design of a theoretical framework. BMC Med Inform Decis Mak 2023; 23:279. [PMID: 38053104 PMCID: PMC10699040 DOI: 10.1186/s12911-023-02372-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/09/2023] [Indexed: 12/07/2023] Open
Abstract
In this paper, we present a framework for developing a Learning Health System (LHS) to provide means to a computerized clinical decision support system for allied healthcare and/or nursing professionals. LHSs are well suited to transform healthcare systems in a mission-oriented approach, and is being adopted by an increasing number of countries. Our theoretical framework provides a blueprint for organizing such a transformation with help of evidence based state of the art methodologies and techniques to eventually optimize personalized health and healthcare. Learning via health information technologies using LHS enables users to learn both individually and collectively, and independent of their location. These developments demand healthcare innovations beyond a disease focused orientation since clinical decision making in allied healthcare and nursing is mainly based on aspects of individuals' functioning, wellbeing and (dis)abilities. Developing LHSs depends heavily on intertwined social and technological innovation, and research and development. Crucial factors may be the transformation of the Internet of Things into the Internet of FAIR data & services. However, Electronic Health Record (EHR) data is in up to 80% unstructured including free text narratives and stored in various inaccessible data warehouses. Enabling the use of data as a driver for learning is challenged by interoperability and reusability.To address technical needs, key enabling technologies are suitable to convert relevant health data into machine actionable data and to develop algorithms for computerized decision support. To enable data conversions, existing classification and terminology systems serve as definition providers for natural language processing through (un)supervised learning.To facilitate clinical reasoning and personalized healthcare using LHSs, the development of personomics and functionomics are useful in allied healthcare and nursing. Developing these omics will be determined via text and data mining. This will focus on the relationships between social, psychological, cultural, behavioral and economic determinants, and human functioning.Furthermore, multiparty collaboration is crucial to develop LHSs, and man-machine interaction studies are required to develop a functional design and prototype. During development, validation and maintenance of the LHS continuous attention for challenges like data-drift, ethical, technical and practical implementation difficulties is required.
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Affiliation(s)
- Mark van Velzen
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands.
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands.
| | - Helen I de Graaf-Waar
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Tanja Ubert
- Institute for Communication, media and information Technology, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Robert F van der Willigen
- Institute for Communication, media and information Technology, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Lotte Muilwijk
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
- Institute for Communication, media and information Technology, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Maarten A Schmitt
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Mark C Scheper
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Allied Health professions, faculty of medicine and science, Macquarrie University, Sydney, Australia
| | - Nico L U van Meeteren
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Top Sector Life Sciences and Health (Health~Holland), The Hague, the Netherlands
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3
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Pirmani A, De Brouwer E, Geys L, Parciak T, Moreau Y, Peeters LM. The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research. JMIR Med Inform 2023; 11:e48030. [PMID: 37943585 PMCID: PMC10667980 DOI: 10.2196/48030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/25/2023] [Accepted: 09/30/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Investigating low-prevalence diseases such as multiple sclerosis is challenging because of the rather small number of individuals affected by this disease and the scattering of real-world data across numerous data sources. These obstacles impair data integration, standardization, and analysis, which negatively impact the generation of significant meaningful clinical evidence. OBJECTIVE This study aims to present a comprehensive, research question-agnostic, multistakeholder-driven end-to-end data analysis pipeline that accommodates 3 prevalent data-sharing streams: individual data sharing, core data set sharing, and federated model sharing. METHODS A demand-driven methodology is employed for standardization, followed by 3 streams of data acquisition, a data quality enhancement process, a data integration procedure, and a concluding analysis stage to fulfill real-world data-sharing requirements. This pipeline's effectiveness was demonstrated through its successful implementation in the COVID-19 and multiple sclerosis global data sharing initiative. RESULTS The global data sharing initiative yielded multiple scientific publications and provided extensive worldwide guidance for the community with multiple sclerosis. The pipeline facilitated gathering pertinent data from various sources, accommodating distinct sharing streams and assimilating them into a unified data set for subsequent statistical analysis or secure data examination. This pipeline contributed to the assembly of the largest data set of people with multiple sclerosis infected with COVID-19. CONCLUSIONS The proposed data analysis pipeline exemplifies the potential of global stakeholder collaboration and underlines the significance of evidence-based decision-making. It serves as a paradigm for how data sharing initiatives can propel advancements in health care, emphasizing its adaptability and capacity to address diverse research inquiries.
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Affiliation(s)
- Ashkan Pirmani
- ESAT, STADIUS, KU Leuven, Leuven, Belgium
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
- Data Science Institute, Hasselt University, Diepenbeek, Belgium
- University Multiple Sclerosis Center, Hasselt University, Diepenbeek, Belgium
| | | | - Lotte Geys
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
- Data Science Institute, Hasselt University, Diepenbeek, Belgium
- University Multiple Sclerosis Center, Hasselt University, Diepenbeek, Belgium
| | - Tina Parciak
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
- Data Science Institute, Hasselt University, Diepenbeek, Belgium
- University Multiple Sclerosis Center, Hasselt University, Diepenbeek, Belgium
| | | | - Liesbet M Peeters
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
- Data Science Institute, Hasselt University, Diepenbeek, Belgium
- University Multiple Sclerosis Center, Hasselt University, Diepenbeek, Belgium
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4
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Altunisik E, Cengiz EK, Keceli YK. A bibliometric evaluation of the top 100 cited articles on ocrelizumab. Mult Scler Relat Disord 2023; 77:104856. [PMID: 37413856 DOI: 10.1016/j.msard.2023.104856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/26/2023] [Accepted: 06/22/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND To analyze the 100 most cited articles (T100) on ocrelizumab using bibliometric methods to determine the current situation and identify research hotspots. METHODS Articles with "ocrelizumab" in their title were searched for in the Web of Science (WoS) database, identifying 900 articles. After the exclusion criteria were applied, 183 original articles and reviews were obtained. The T100 were selected from among these articles. Data on these articles (author, source, institution, country, scientific category, citation number, and citation density) were analyzed. RESULTS The number of articles showed a fluctuating upward trend from 2006 to 2022. The total number of citations for the T100 ranged from two to 923. The average number of citations per article was 45.11. The most articles were published in 2021 (n = 31). The "Ocrelizumab versus Placebo in Primary Progressive Multiple Sclerosis" study (T1) was the most cited article among the T100 and had the highest annual average number of citations. T1, T2, and T3 were clinical trials on treating multiple sclerosis. The USA was the most productive and influential research country, with 44 articles. Multiple Sclerosis and Related Disorders was the most productive journal (n = 22). Clinical neurology ranked first among the WoS categories (n = 70). Hauser Stephen and Kappos Ludwig were the most influential authors, with 10 articles each. Biotechnology company Roche was at the top of the publication list, with 36 articles. CONCLUSION This study's results can give researchers an idea about current developments and research collaborations on ocrelizumab. These data can help researchers easily obtain publications that have become classics. We conclude that the clinical and academic communities have shown a growing interest in ocrelizumab for treating primary progressive multiple sclerosis in recent years.
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Affiliation(s)
- Erman Altunisik
- Department of Neurology, Faculty of Medicine, Adiyaman University, Adiyaman, Turkey.
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5
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Ismail II, Saqr M. A Quantitative Synthesis of Eight Decades of Global Multiple Sclerosis Research Using Bibliometrics. Front Neurol 2022; 13:845539. [PMID: 35280299 PMCID: PMC8907526 DOI: 10.3389/fneur.2022.845539] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 01/24/2022] [Indexed: 12/14/2022] Open
Abstract
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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Affiliation(s)
| | - Mohammed Saqr
- School of Computing, University of Eastern Finland, Joensuu, Finland
- *Correspondence: Mohammed Saqr
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6
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Geys L, Parciak T, Pirmani A, McBurney R, Schmidt H, Malbaša T, Ziemssen T, Bergmann A, Rojas JI, Cristiano E, García-Merino JA, Fernández Ó, Kuhle J, Gobbi C, Delmas A, Simpson-Yap S, Nag N, Yamout B, Steinemann N, Seeldrayers P, Dubois B, van der Mei I, Stahmann A, Drulovic J, Pekmezovic T, Brola W, Tintore M, Kalkers N, Ivanov R, Zakaria M, Naseer MA, Van Hecke W, Grigoriadis N, Boziki M, Carra A, Pawlak MA, Dobson R, Hellwig K, Gallagher A, Leocani L, Dalla Costa G, de Carvalho Sousa NA, Van Wijmeersch B, Peeters LM. The Multiple Sclerosis Data Alliance Catalogue: Enabling Web-Based Discovery of Metadata from Real-World Multiple Sclerosis Data Sources. Int J MS Care 2022; 23:261-268. [PMID: 35035297 DOI: 10.7224/1537-2073.2021-006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background One of the major objectives of the Multiple Sclerosis Data Alliance (MSDA) is to enable better discovery of multiple sclerosis (MS) real-world data (RWD). Methods We implemented the MSDA Catalogue, which is available worldwide. The current version of the MSDA Catalogue collects descriptive information on governance, purpose, inclusion criteria, procedures for data quality control, and how and which data are collected, including the use of e-health technologies and data on collection of COVID-19 variables. The current cataloguing procedure is performed in several manual steps, securing an effective catalogue. Results Herein we summarize the status of the MSDA Catalogue as of January 6, 2021. To date, 38 data sources across five continents are included in the MSDA Catalogue. These data sources differ in purpose, maturity, and variables collected, but this landscaping effort shows that there is substantial alignment on some domains. The MSDA Catalogue shows that personal data and basic disease data are the most collected categories of variables, whereas data on fatigue measurements and cognition scales are the least collected in MS registries/cohorts. Conclusions The Web-based MSDA Catalogue provides strategic overview and allows authorized end users to browse metadata profiles of data cohorts and data sources. There are many existing and arising RWD sources in MS. Detailed cataloguing of MS RWD is a first and useful step toward reducing the time needed to discover MS RWD sets and promoting collaboration.
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Affiliation(s)
- Lotte Geys
- University MS Center, Hasselt-Pelt, Belgium (LG, TParciak, AP, BVW, LMP).,Biomedical Research Institute (BIOMED) (LG, TParciak, AP, BVW, LMP), University of Hasselt, Diepenbeek, Belgium.,Data Science Institute (LG, TParciak, AP, LMP), University of Hasselt, Diepenbeek, Belgium
| | - Tina Parciak
- University MS Center, Hasselt-Pelt, Belgium (LG, TParciak, AP, BVW, LMP).,Biomedical Research Institute (BIOMED) (LG, TParciak, AP, BVW, LMP), University of Hasselt, Diepenbeek, Belgium.,Data Science Institute (LG, TParciak, AP, LMP), University of Hasselt, Diepenbeek, Belgium.,University Medical Center Göttingen, Department of Medical Informatics, Germany (TParciak)
| | - Ashkan Pirmani
- University MS Center, Hasselt-Pelt, Belgium (LG, TParciak, AP, BVW, LMP).,Biomedical Research Institute (BIOMED) (LG, TParciak, AP, BVW, LMP), University of Hasselt, Diepenbeek, Belgium.,ESAT-STADIUS, KU Leuven, Leuven, Belgium (AP)
| | | | - Hollie Schmidt
- Accelerated Cure Project for MS, Waltham, MA, USA (RM, HS)
| | - Tanja Malbaša
- Association of Multiple Sclerosis Societies of Croatia, Zagreb (TM)
| | - Tjalf Ziemssen
- Center for Clinical Neuroscience, University Hospital Dresden, Germany (TZ)
| | | | - Juan I Rojas
- Neurology Department, Hospital Universitario de CEMIC, Buenos Aires, Argentina (JIR)
| | | | - Juan Antonio García-Merino
- Department of Neurology, Universidad Autonoma de Madrid, Spain (JAG-M).,Neurology Service, Puerta de Hierro Hospital, Majadahonda, Madrid, Spain (JAG-M)
| | - Óscar Fernández
- University of Malaga, Department of Pharmacology, Spain (OF)
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland (JK)
| | - Claudio Gobbi
- Multiple Sclerosis Center, Department of Neurology, Neurocenter of Southern Switzerland, Lugano, Switzerland (CG).,Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland (CG)
| | - Amber Delmas
- Life Sciences Department, EHealthLine.com, Inc (AD)
| | - Steve Simpson-Yap
- Neuroepidemiology Unit, Melbourne School of Population and Global Health, The University of Melbourne, Australia (SS-Y, NN)
| | - Nupur Nag
- Neuroepidemiology Unit, Melbourne School of Population and Global Health, The University of Melbourne, Australia (SS-Y, NN)
| | - Bassem Yamout
- Multiple Sclerosis Center, American University of Beirut Medical Center, Lebanon (BY)
| | - Nina Steinemann
- Data Center of the Swiss Multiple Sclerosis Registry, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Switzerland (NS)
| | | | - Bénédicte Dubois
- Department of Neurosciences, Laboratory for Neuroimmunology, KU Leuven, Leuven, Belgium (BD).,Leuven Brain Institute KU Leuven, Leuven, Belgium (BD).,Department of Neurology, University Hospitals Leuven, Leuven, Belgium (BD)
| | - Ingrid van der Mei
- Menzies Institute for Medical Research, University of Tasmania, Hobart TAS, Australia (IvdM)
| | - Alexander Stahmann
- German MS-Registry, MS Forschungs- und Projektentwicklungs-gGmbH, Hannover, Germany (AS)
| | - Jelena Drulovic
- Clinic of Neurology, Clinical Center of Serbia, Belgrade, Serbia (JD)
| | - Tatjana Pekmezovic
- Institute of Epidemiology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia (TPekmezovic)
| | - Waldemar Brola
- Collegium Medicum, Jan Kochanowski University, Kielce, Poland (WB)
| | - Mar Tintore
- Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Edifici Cemcat, Hospital Universitari Vall d'Hebron, Barcelona, Spain (MT)
| | - Nynke Kalkers
- Department of Neurology, OLVG, and Department of Neurology, Amsterdam UMC, Location VUMC, Amsterdam, the Netherlands (NK)
| | - Rumen Ivanov
- PMA - Pharma Marketing Advisors, Ltd, Sofia, Bulgaria (RI)
| | - Magd Zakaria
- Department of Neurology, Ain Shams University, Egypt (MZ)
| | | | | | - Nikolaos Grigoriadis
- Second Neurological University Department, Multiple Sclerosis Center, Aristotle University of Thessaloniki, AHEPA General University Hospital, Thessaloniki Greece (NG, MB)
| | - Marina Boziki
- Second Neurological University Department, Multiple Sclerosis Center, Aristotle University of Thessaloniki, AHEPA General University Hospital, Thessaloniki Greece (NG, MB)
| | - Adriana Carra
- MS Center Hospital Britanico, Buenos Aires, Argentina (AC)
| | - Mikolaj A Pawlak
- Department of Neurology and Cerebrovascular Disorders, Poznan University of Medical Sciences, Poznan, Poland (MAP)
| | - Ruth Dobson
- Wolfson Institute of Preventive Medicine, Charterhouse Square, London, UK (RD)
| | - Kerstin Hellwig
- Department of Neurology, Katholisches Klinikum, St Josef Hospital, Ruhr University Bochum, Bochum Germany (KH)
| | - Arlene Gallagher
- Clinical Practice Research Datalink (CPRD), Medicines and Healthcare Products Regulatory Agency (MHRA), London, UK (AG)
| | - Letizia Leocani
- Clinical Neurology Unit, San Raffaele University, Milan, Italy (LL, GDC)
| | | | | | - Bart Van Wijmeersch
- University MS Center, Hasselt-Pelt, Belgium (LG, TParciak, AP, BVW, LMP).,Biomedical Research Institute (BIOMED) (LG, TParciak, AP, BVW, LMP), University of Hasselt, Diepenbeek, Belgium.,Noorderhart, Rehabilitation and MS Center, Pelt, Belgium (BVW)
| | - Liesbet M Peeters
- University MS Center, Hasselt-Pelt, Belgium (LG, TParciak, AP, BVW, LMP).,Biomedical Research Institute (BIOMED) (LG, TParciak, AP, BVW, LMP), University of Hasselt, Diepenbeek, Belgium.,Data Science Institute (LG, TParciak, AP, LMP), University of Hasselt, Diepenbeek, Belgium
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7
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De Brouwer E, Becker T, Moreau Y, Havrdova EK, Trojano M, Eichau S, Ozakbas S, Onofrj M, Grammond P, Kuhle J, Kappos L, Sola P, Cartechini E, Lechner-Scott J, Alroughani R, Gerlach O, Kalincik T, Granella F, Grand'Maison F, Bergamaschi R, José Sá M, Van Wijmeersch B, Soysal A, Sanchez-Menoyo JL, Solaro C, Boz C, Iuliano G, Buzzard K, Aguera-Morales E, Terzi M, Trivio TC, Spitaleri D, Van Pesch V, Shaygannejad V, Moore F, Oreja-Guevara C, Maimone D, Gouider R, Csepany T, Ramo-Tello C, Peeters L. Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression. Comput Methods Programs Biomed 2021; 208:106180. [PMID: 34146771 DOI: 10.1016/j.cmpb.2021.106180] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 05/08/2021] [Indexed: 05/23/2023]
Abstract
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. METHODS We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used. RESULTS We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. CONCLUSIONS Compared 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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Affiliation(s)
| | - Thijs Becker
- I-Biostat, Data Science Institute, Hasselt University, Diepenbeek, Belgium.
| | - Yves Moreau
- ESAT-STADIUS, KU Leuven, Leuven 3001, Belgium.
| | | | - Maria Trojano
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari, Bari, Italy
| | - Sara Eichau
- Hospital Universitario Virgen Macarena, Sevilla, Spain
| | | | | | | | - Jens Kuhle
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | | | | | | | | | | | - Tomas Kalincik
- Melbourne MS Centre, Department of Neurology, Royal Melbourne Hospital, Melbourne, Australia; CORe, Department of Medicine, University of Melbourne, Melbourne, Australia
| | | | | | | | - Maria José Sá
- Department of Neurology, Centro Hospitalar Universitario de So Joo and University Fernando Pessoa, Porto, Portugal
| | | | - Aysun Soysal
- Bakirkoy Education and Research Hospital for Psychiatric and Neurological Diseases, Istanbul, Turkey
| | | | - Claudio Solaro
- Dept of Rehabilitation mons L Novarese Hospital, Moncrivello, Italy
| | - Cavit Boz
- KTU Medical Faculty Farabi Hospital, Trabzon, Turkey
| | | | | | | | | | | | - Daniele Spitaleri
- Azienda Ospedaliera di Rilievo Nazionale San Giuseppe Moscati Avellino, Avellino, Italy
| | | | - Vahid Shaygannejad
- Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | | | | | | | | | | | | | - Liesbet Peeters
- I-Biostat, Data Science Institute, Hasselt University, Diepenbeek, Belgium; Department of Immunology, Biomedical Research Institute, Hasselt University, Diepenbeek 3590, Belgium; Department of Immunology, Biomedical Research Institute, Hasselt University, Diepenbeek 3590, Belgium; I-Biostat, Data Science Institute, Hasselt University, Diepenbeek, Belgium.
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8
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Abstract
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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Affiliation(s)
| | | | | | | | | | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, Technical University of Dresden, Dresden, Germany
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9
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D'Souza M, Papadopoulou A, Girardey C, Kappos L. Standardization and digitization of clinical data in multiple sclerosis. Nat Rev Neurol 2021; 17:119-125. [PMID: 33452493 DOI: 10.1038/s41582-020-00448-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/11/2020] [Indexed: 12/12/2022]
Abstract
Standardization is necessary to ensure the reliability of clinical data and to enable longitudinal and cross-sectional comparisons of data obtained in different centres and countries. In patients with multiple sclerosis (MS), standardized clinical data are needed for monitoring of disability and for collecting real-world evidence for use in research. This Perspective describes attempts to improve the standardization and digitization of clinical data in MS, including digital electronic health recording systems and applications that attempt to offer a comprehensive assessment of patients' neurological deficits and their effects on daily life. Despite the challenges raised by regulatory, ethical and data-privacy considerations, the standardization and digitization of clinical data in MS is expected to generate new insights into the pathophysiology of the disease and to contribute to personalized patient care.
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Affiliation(s)
- Marcus D'Souza
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, Basel, Switzerland. .,Research Center for Clinical Neuroimmunology and Neuroscience, University of Basel, Basel, Switzerland. .,Office of the Chief Medical Informatics Officer, Digitalisierung & Information and Communication Technology Department, University Hospital Basel, Basel, Switzerland.
| | - Athina Papadopoulou
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience, University of Basel, Basel, Switzerland
| | | | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital Basel, Basel, Switzerland.,Research Center for Clinical Neuroimmunology and Neuroscience, University of Basel, Basel, Switzerland
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