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Jack Arnold, MBBS: Honing in on ANA-RMD Classifications to Improve Research
Arnold discussed his research in analyzing and improving current classifications of ANA-RMDs and how it could help improve research in the field.
Researchers from the University of Leeds are aiming to improve current classification of ANA-associated rheumatic and musculoskeletal diseases (ANA-RMDs), and by doing so, improve the precision of research in the field.
Data from an analysis of disease characteristics in patients included in the European PRECISESADS cohort were presented by Jack Arnold, MBBS, clinical research fellow, University of Leeds, Leeds Institute of Rheumatic and Musculoskeletal Medicine, at the American College of Rheumatology (ACR) Convergence 2024, held November 14-19 in Washington, DC.
“At the moment, current disease classifications partially capture the heterogeneity [of ANA-RMDs], but maybe we can improve on that in the future and sort of take things forward. And it's more about trying to take that into further research, and that's what we're sort of working on at the moment,” Arnold told HCPLive® during the meeting.
Arnold and colleagues used deep learning to group ANA-RMDs into 5 classes stratified by disease activity, gene expression, inflammation, pain, and healthcare utilization, among other factors. Notably, differences in hospital admission rates) and emergency department attendance were significantly different among the new classes (both P <.01) but not the legacy classes.
Arnold discussed how ANA-RMD classifications could be improved and emphasized that the classifications were informed by factors important to both experts working with ANA-RMDs as well as patients with the diseases themselves. He also shared that the ultimate goal with the research in reclassifying ANA-RMDs more precisely and accurately is to help improve research by looking at specific subgroups of patients and hone in on treating each patient in the most appropriate way.
Arnold’s relevant disclosures include Alumis.
