SORA

Advancing, promoting and sharing knowledge of health through excellence in teaching, clinical practice and research into the prevention and treatment of illness

The Use of Technology in the Subcategorisation of Osteoarthritis: a Delphi Study Approach

Mennan, C; Hopkins, T; Channon, A; Elliott, M; Johnstone, B; Kadir, T; Loughlin, J; Peffers, M; Pitsillides, A; Sofat, N; et al. Mennan, C; Hopkins, T; Channon, A; Elliott, M; Johnstone, B; Kadir, T; Loughlin, J; Peffers, M; Pitsillides, A; Sofat, N; Stewart, C; Watt, FE; Zeggini, E; Holt, C; Roberts, S (2020) The Use of Technology in the Subcategorisation of Osteoarthritis: a Delphi Study Approach. Osteoarthritis and Cartilage Open, 2 (3). p. 100081. ISSN 2665-9131 https://doi.org/10.1016/j.ocarto.2020.100081
SGUL Authors: Sofat, Nidhi

[img]
Preview
PDF Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (590kB) | Preview
[img]
Preview
PDF Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (702kB) | Preview

Abstract

Objective This UK-wide OATech Network + consensus study utilised a Delphi approach to discern levels of awareness across an expert panel regarding the role of existing and novel technologies in osteoarthritis research. To direct future cross-disciplinary research it aimed to identify which could be adopted to subcategorise patients with osteoarthritis (OA). Design An online questionnaire was formulated based on technologies which might aid OA research and subcategorisation. During a two-day face-to-face meeting concordance of expert opinion was established with surveys (23 questions) before, during and at the end of the meeting (Rounds 1, 2 and 3, respectively). Experts spoke on current evidence for imaging, genomics, epigenomics, proteomics, metabolomics, biomarkers, activity monitoring, clinical engineering and machine learning relating to subcategorisation. For each round of voting, ≥80% votes led to consensus and ≤20% to exclusion of a statement. Results Panel members were unanimous that a combination of novel technological advances have potential to improve OA diagnostics and treatment through subcategorisation, agreeing in Rounds 1 and 2 that epigenetics, genetics, MRI, proteomics, wet biomarkers and machine learning could aid subcategorisation. Expert presentations changed participants’ opinions on the value of metabolomics, activity monitoring and clinical engineering, all reaching consensus in Round 2. X-rays lost consensus between Rounds 1 and 2; clinical X-rays reached consensus in Round 3. Conclusion Consensus identified that 9 of the 11 technologies should be targeted towards OA subcategorisation to address existing OA research technology and knowledge gaps. These novel, rapidly evolving technologies are recommended as a focus for emergent, cross-disciplinary osteoarthritis research programmes.

Item Type: Article
Additional Information: © 2020 Osteoarthritis Research Society International (OARSI). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
SGUL Research Institute / Research Centre: Academic Structure > Infection and Immunity Research Institute (INII)
Journal or Publication Title: Osteoarthritis and Cartilage Open
ISSN: 2665-9131
Language: en
Dates:
DateEvent
September 2020Published
9 June 2020Published Online
28 May 2020Accepted
Projects:
Project IDFunderFunder ID
EP/N027264/1Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
21156Versus ArthritisUNSPECIFIED
18450Versus ArthritisUNSPECIFIED
MR/L0104531/1Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
20771Versus ArthritisUNSPECIFIED
JXR 10641Centre for Integrated Research into Musculoskeletal AgeingUNSPECIFIED
MR/P020941/1Centre for Integrated Research into Musculoskeletal AgeingUNSPECIFIED
MR/R502182/1Centre for Integrated Research into Musculoskeletal AgeingUNSPECIFIED
20205Versus ArthritisUNSPECIFIED
21621Versus ArthritisUNSPECIFIED
URI: https://openaccess.sgul.ac.uk/id/eprint/112039
Publisher's version: https://doi.org/10.1016/j.ocarto.2020.100081

Actions (login required)

Edit Item Edit Item