2017 EULAR recommendations for a core data set to support observational research and clinical care in rheumatoid arthritis.
Helga RadnerKaterina ChatzidionysiouElena NikiphorouLaure GossecKimmie L HyrichCondruta ZabalanYvonne van Eijk-HustingsPaula R WilliamsonAndra BalanescuGerd R BurmesterLoreto CarmonaMaxime DougadosAxel FinckhGlenn HaugebergMerete Lund HetlandSusan OliverDuncan PorterKarim RazaPatrick RyanMaria Jose SantosAnnette van der Helm-van MilPiet van RielGabrielle von KrauseJakub ZavadaWilliam Gregory DixonJohan AsklingPublished in: Annals of the rheumatic diseases (2018)
Personalised medicine, new discoveries and studies on rare exposures or outcomes require large samples that are increasingly difficult for any single investigator to obtain. Collaborative work is limited by heterogeneities, both what is being collected and how it is defined. To develop a core set for data collection in rheumatoid arthritis (RA) research which (1) allows harmonisation of data collection in future observational studies, (2) acts as a common data model against which existing databases can be mapped and (3) serves as a template for standardised data collection in routine clinical practice to support generation of research-quality data. A multistep, international multistakeholder consensus process was carried out involving voting via online surveys and two face-to-face meetings. A core set of 21 items ('what to collect') and their instruments ('how to collect') was agreed: age, gender, disease duration, diagnosis of RA, body mass index, smoking, swollen/tender joints, patient/evaluator global, pain, quality of life, function, composite scores, acute phase reactants, serology, structural damage, treatment and comorbidities. The core set should facilitate collaborative research, allow for comparisons across studies and harmonise future data from clinical practice via electronic medical record systems.
Keyphrases
- rheumatoid arthritis
- clinical practice
- electronic health record
- big data
- body mass index
- disease activity
- healthcare
- quality improvement
- machine learning
- data analysis
- ankylosing spondylitis
- social media
- physical activity
- artificial intelligence
- spinal cord
- air pollution
- spinal cord injury
- pain management
- patient reported outcomes
- molecularly imprinted
- weight gain
- replacement therapy