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Points to Consider for Big Data in Rheumatic and Musculoskeletal Diseases

This overview provides the first European League Against Rheumatism (EULAR)-endorsed points to consider (PTC) for the use of big data in rheumatic and musculoskeletal diseases (RMDs). It covers key issues including ethics, data sources, storage, analyses, artificial intelligence, benchmarking, reporting, and implementation of findings. The target population includes researchers, data collection organizations, data analysts, patients, clinicians, and other stakeholders. Methods involved a literature review, task force meetings, and determination of recommendations.

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Points to Consider for Big Data in Rheumatic and Musculoskeletal Diseases

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  1. European League Against Rheumatism points to consider for the use of big data in rheumatic and musculoskeletal diseases

  2. Overview and target population Overview • The use of big data by artificial intelligence, computational modelling and machine learning is a rapidly evolving field with the potential to profoundly modify RMD research and patient care. • These are the first European League Against Rheumatism (EULAR)-endorsed points to consider (PTC) for the use of big data in RMDs. These points address key issues including: ethics, data sources, data storage, data analyses, artificial intelligence (eg, computational modelling, machine learning), the need for benchmarking, adequate reporting of methods, and implementation of findings into clinical practice. Target population • Researchers in the field of big data in RMDs, researchers outside the field of RMDs; data collection organisations and/or groups collecting data (e.g. registries, hospitals, telecom operators, search engines, genetic sequencing teams, institutions which collect images etc); data analysts and organisations; people with RMDs, people at risk of developing RMDs, patient associations; clinicians involved in the management of people with RMDs; other stakeholders such as research organisations and funding agencies, policy makers, authorities, governments and medical societies outside of RMDs.

  3. Methods • Methods: adapted from the EULAR Standardized Operating Procedures (1) • Meeting ofsteeringgroup (October 2018): decisions on scopeoftheliteraturereview • Systematicliteraturereviewofthestatusofbigdata in RMD publicationsandmirrorreview in othermedicalfields (2) • Taskforcefullmeeting (February 2019): • Presentationofliteraturereview • Elaboration ofoverarchingprinciplesand PTC • Elaboration ofresearchagenda • Finalisationof PTC by online discussions, takingintoaccountconcomitantpublication (3) • Determination oflevelofstrengthand grade ofrecommendations(4) • Votesby email on levelofagreementofTaskforcemembers (0-10 where 10 isfullagreement) van der Heijdeet al Ann Rheum Dis 2016,75:3-15. 2. Kedra J et al, RMD Open (submitted). 3. https://www.ema.europa.eu/en/documents/minutes/hma/ema-joint-task-force-big-data-summary-report_en.pdf Accessed Feb 16, 2019 4. OCEBM Levels of Evidence Working Group. The Oxford Levels of Evidence 2. Oxford Centre for Evidence-Based Medicine. https://www.cebm.net/index.aspx?o=5653

  4. Overarchingprinciples Definitions: The term ‘big data’ refers to extremely large datasets which may be complex, multi-dimensional, unstructured and from heterogeneous sources, and which accumulate rapidly. Computational technologies, including artificial intelligence (e.g. machine learning), are often applied to big data.Big data may arise from multiple data sources including clinical, biological, social and environmental data sources. • A.For all big data use, ethical issues related to privacy, confidentiality, identity and transparency are key principles to consider. B. Big data provides unprecedented opportunities to deliver transformative discoveries in RMD research and practice. • C. The ultimate goal of using big data in RMDs is to improve the health, lives and care of people including health promotion and assessment, prevention, diagnosis, treatment and monitoring of disease.

  5. Points to consider

  6. Points to consider

  7. Summary Table Oxford Level of Evidence

  8. Summary of EULAR points to consider for the use of big data in RMDs • [Secretariat will add link of recommendation once available online on BMJ portal.]

  9. Summary of EULAR points to consider for the use of big data in RMDs: lay format Read the full lay summary (add hyperlink if provided) 1 star (*) means it is a weak recommendation with limited scientific evidence; 2 stars (**) means it is a weak recommendation with some scientific evidence; 3 stars (***) means it is a strong recommendation with quite a lot of scientific evidence; 4 stars (****) means it is a strong recommendation supported with a lot of scientific evidence. Recommendations with just 1 or 2 stars are based mainly on expert opinion and not backed up by appropriate clinical studies, but may be as important as those with 3 and 4 stars.

  10. Acknowledgements • Convenors: Laure Gossec, Timothy Radstake • Epidemiologist: L. Gossec • Fellow: Joanna Kedra • Other task force members: XenofonBaraliakos, Francis Berenbaum, GerdBurmester, RémyChoquet, , Axel Finckh, David Gomez-Cabrero, AridamanPandit, Christian Pristipino, HervéServy, TanjaStamm, Simon Stones

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