The relationship between clinical phenotype and kallikrein-kinin bioregulation in different forms of arthritis.

OBJECTIVE: Patients with rheumatoid arthritis (RA) have shown increased levels of neutrophils generating kallikrein-kinin peptides in blood which are potent mediators of inflammation. This study investigated the association between the bioregulation of kinin-mediated inflammation with the clinical, quality of life, and imaging characteristics (e.g. ultrasonography) of different arthritides. METHODS: Patients with osteoarthritis (OA, n = 29), gout (n = 10) and RA (n = 8) were recruited and screened for clinical symptoms, quality of life, and ultrasonographical assessment of arthritis. Blood neutrophils were assessed for the expression of bradykinin receptors (B1R and B2R), kininogens and kallikreins by immunocytochemistry with visualization by bright field microscopy. Levels of plasma biomarkers were measured by ELISA and cytometric bead array. RESULTS: Quality of life (SF-36 domains and summary scores; including pain; and, HAQ) was similar across OA, gout and RA patients; with the exception of worse physical functioning scores between OA and gout patients. Synovial hypertrophy (on ultrasound) differed between groups (p = 0.001), and the dichotomised Power Doppler (PD) score of greater than or equal to 2 (PD-GE2) was marginally significant (p = 0.09). Plasma IL-8 were highest in patients with gout followed by RA and OA (both, P < 0.05). Patients with RA had higher plasma levels of sTNFR1, IL-1ß, IL-12p70, TNF and IL-6, compared to OA and gout patients (all, P < 0.05). Patients with OA had higher expression of K1B and KLK1 on blood neutrophils followed by RA and gout patients (both, P < 0.05). Bodily pain correlated with B1R expression on blood neutrophils (r = 0.334, p = 0.05), and inversely with plasma levels of CRP (r = -0.55), sTNFR1 (r = -0.352) and IL-6 (r = -0.422), all P < 0.05. Expression of B1R on blood neutrophils also correlated with Knee PD (r = 0.403) and PD-GE2 (r = 0.480), both P < 0.05. CONCLUSIONS: Pain levels and quality of life were similar between patients with OA, RA and gout with knee arthritis. Plasma inflammatory biomarkers and B1R expression on blood neutrophils correlated with pain. Targeting B1R to modulate the kinin-kallikrein system may pose as a new therapeutic target in the treatment of arthritis.

A multicentre validation study of a smartphone application to screen hand arthritis.

BACKGROUND: Arthritis is a common condition, and the prompt and accurate assessment of hand arthritis in primary care is an area of unmet clinical need. We have previously developed and tested a screening tool combining machine-learning algorithms, to help primary care physicians assess patients presenting with arthritis affecting the hands. The aim of this study was to assess the validity of the screening tool among a number of different Rheumatologists. METHODS: Two hundred and forty-eight consecutive new patients presenting to 7 private Rheumatology practices across Australia were enrolled. Using a smartphone application, each patient had photographs taken of their hands, completed a brief 9-part questionnaire, and had a single examination result (wrist irritability) recorded. The Rheumatologist diagnosis was entered following a 45-minute consultation. Multiple machine learning models were applied to both the photographic and survey/examination results, to generate a screening outcome for the primary diagnoses of osteoarthritis, rheumatoid and psoriatic arthritis. RESULTS: The combined algorithms in the application performed well in identifying and discriminating between different forms of hand arthritis. The algorithms were able to predict rheumatoid arthritis with accuracy, precision, recall and specificity of 85.1, 80.0, 88.1 and 82.7% respectively. The corresponding results for psoriatic arthritis were 95.2, 76.9, 90.9 and 95.8%, and for osteoarthritis were 77.4, 78.3, 80.6 and 73.7%. The results were maintained when each contributor was excluded from the analysis. The median time to capture all data across the group was 2 minutes and 59 seconds. CONCLUSIONS: This multicentre study confirms the results of the pilot study, and indicates that the performance of the screening tool is maintained across a group of different Rheumatologists. The smartphone application can provide a screening result from a combination of machine-learning algorithms applied to hand images and patient symptom responses. This could be used to assist primary care physicians in the assessment of patients presenting with hand arthritis, and has the potential to improve the clinical assessment and management of such patients.