Abstract
Rheumatoid Arthritis (RA) is a chronic inflammatory disease resulting in joint swelling and pain. Treatment options can be reliant on disease activity scores (DAS) incorporating patient global assessments, which are quantified via visual analogue scales (VAS). VAS can be subjective and not necessarily align with clinical symptoms, such as inflammation, resulting in a disconnect between the patient's and practitioners' experience. The development of more objective assessments of pain would enable a more targeted and personalised management of pain within individuals with RA and have the potential to improve the reliability of assessments in research. Using emerging light-based hyperspectral autofluorescence imaging (HAI) technology, we aimed to objectively differentiate disease and pain states based on the analysis of synovial tissue (ST) samples from RA patients. In total, 22 individuals with RA were dichotomised using the DAS in 28-joint counts (DAS-28) into an inactive (IA) or active disease (active-RA) group and then three sub-levels of pain (low, mid, high) based on VAS. HAI was performed on ST sections to identify and quantify the most prominent fluorophores. HAI fluorophore analysis revealed a distinct separation between the IA-RA and active-RA mid-VAS cohort, successfully determining disease state. Additionally, the separation between active-RA Mid-VAS and active RA High-VAS cohort suggests that HAI could be used to objectively separate individuals based on pain severity.