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To investigate whether ultrasound findings of major salivary glands are correlated with serological markers, autoantibodies, patient- or doctor-reported disease activity in a Danish cohort of patients with primary Sjögren's Syndrome (pSS). In all, 49 patients at Odense University Hospital with pSS diagnosed according to the 2002 American-European Consensus Group (AECG) classification criteria were included. Patients were characterized using the EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI, score of systemic complications) and EULAR Sjögren's Syndrome Patient Reported Index (ESSPRI), serologic markers, Schirmer's test and salivary test. Salivary gland ultrasound (SGUS) was performed of the submandibular and parotid glands and scored according to the Outcome Measures in Rheumatoid Arthritis Clinical Trials (OMERACT) semi-quantitative scoring system. More patients with abnormal SGUS had antinuclear antibodies (ANA) (p = 0.002), anti-Ro52 (p = 0.001), anti-Ro60 (p<0.001), anti-La (p<0.001) and IgM-RF (p<0.001). Titers for ANA (p = 0.02) and anti-Ro52 (p = 0.03) were higher in patients with abnormal SGUS. Twenty-three of the pSS patients had no pathological findings on SGUS. There was no correlation between SGUS severity and ESSDAI- or ESSPRI-scores. Abnormal SGUS findings are associated with autoantibodies of high specificity for pSS but not with ESSDAI, ESSPRI or inflammatory markers.

BackgroundThe development of standardised methods for ultrasound (US) scanning and evaluation of synovitis activity by the OMERACT-EULAR Synovitis Scoring (OESS) system is a major step forward in the use of US in the diagnosis and monitoring of patients with inflammatory arthritis. The variation in interpretation of disease activity on US images can affect diagnosis, treatment and outcomes in clinical trials. We, therefore, set out to investigate if we could utilise neural network architecture for the interpretation of disease activity on Doppler US images, using the OESS scoring system.MethodsTwo state-of-the-art neural networks were used to extract information from 1342 Doppler US images from patients with rheumatoid arthritis (RA). One neural network divided images as either healthy (Doppler OESS score 0 or 1) or diseased (Doppler OESS score 2 or 3). The other to score images across all four of the OESS systems Doppler US scores (0–3). The neural networks were hereafter tested on a new set of RA Doppler US images (n=176). Agreement between rheumatologist’s scores and network scores was measured with the kappa statistic.ResultsFor the neural network assessing healthy/diseased score, the highest accuracies compared with an expert rheumatologist were 86.4% and 86.9% with a sensitivity of 0.864 and 0.875 and specificity of 0.864 and 0.864, respectively. The other neural network developed to four class Doppler OESS scoring achieved an average per class accuracy of 75.0% and a quadratically weighted kappa score of 0.84.ConclusionThis study is the first to show that neural network technology can be used in the scoring of disease activity on Doppler US images according to the OESS system.

To develop an artificial intelligence (AI) model able to perform both segmentation of hand joint ultrasound images for osteophytes, bone, and synovium and perform osteophyte severity scoring following the EULAR-OMERACT grading system (EOGS) for hand osteoarthritis (OA). One hundred sixty patients with pain or reduced function of the hands were included. Ultrasound images of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), distal interphalangeal (DIP), and first carpometacarpal (CMC1) joints were then manually segmented for bone, synovium and osteophytes and scored from 0 to 3 according to the EOGS for OA. Data was divided into a training, validation, and test set. The AI model was trained on the training data to perform bone, synovium, and osteophyte identification on the images. Based on the manually performed image segmentation, an AI was trained to classify the severity of osteophytes according to EOGS from 0 to 3. Percent Exact Agreement (PEA) and Percent Close Agreement (PCA) were assessed on individual joints and overall. PCA allows a difference of one EOGS grade between doctor assessment and AI. A total of 4615 ultrasound images were used for AI development and testing. The developed AI model scored on the test set for the MCP joints a PEA of 76% and PCA of 97%; for PIP, a PEA of 70% and PCA of 97%; for DIP, a PEA of 59% and PCA of 94%, and CMC a PEA of 50% and PCA of 82%. Combining all joints, we found a PEA between AI and doctor assessments of 68% and a PCA of 95%. The developed AI model can perform joint ultrasound image segmentation and severity scoring of osteophytes, according to the EOGS. As proof of concept, this first version of the AI model is successful, as the agreement performance is slightly higher than previously found agreements between experts when assessing osteophytes on hand OA ultrasound images. The segmentation of the image makes the AI explainable to the doctor, who can immediately see why the AI applies a given score. Future validation in hand OA cohorts is necessary though.

ObjectiveTo evaluate the agreement and repeatability of an automated robotic ultrasound system (ARTHUR V.2.0) combined with an AI model (DIANA V.2.0) in assessing synovial hypertrophy (SH) and Doppler activity in rheumatoid arthritis (RA) patients, using an expert rheumatologist’s assessment as the reference standard.Methods30 RA patients underwent two consecutive ARTHUR V.2.0 scans and rheumatologist assessment of 22 hand joints, with the rheumatologist blinded to the automated system’s results. Images were scored for SH and Doppler by DIANA V.2.0 using the EULAR-OMERACT scale (0–3). The agreement was evaluated by weighted Cohen’s kappa, percent exact agreement (PEA), percent close agreement (PCA) and binary outcomes using Global OMERACT-EULAR Synovitis Scoring (healthy ≤1 vs diseased ≥2). Comparisons included intra-robot repeatability and agreement with the expert rheumatologist and a blinded independent assessor.ResultsARTHUR successfully scanned 564 out of 660 joints, corresponding to an overall success rate of 85.5%. Intra-robot agreement for SH: PEA 63.0%, PCA 93.0%, binary 90.5% and for Doppler, PEA 74.8%, PCA 93.7%, binary 88.1% and kappa values of 0.54 and 0.49. Agreement between ARTHUR+DIANA and the rheumatologist: SH (PEA 57.9%, PCA 92.9%, binary 87.3%, kappa 0.38); Doppler (PEA 77.3%, PCA 94.2%, binary 91.2%, kappa 0.44) and with the independent assessor: SH (PEA 49.0%, PCA 91.2%, binary 80.0%, kappa 0.39); Doppler (PEA 62.6%, PCA 94.4%, binary 88.1%, kappa 0.48).ConclusionsARTHUR V.2.0 and DIANA V.2.0 demonstrated repeatability on par with intra-expert agreement reported in the literature and showed encouraging agreement with human assessors, though further refinement is needed to optimise performance across specific joints.

High-risk patients with antiphospholipid syndrome (APS) experience increased risk of thrombosis when treated with direct oral anticoagulant (DOAC) therapy compared with warfarin. It is essential to establish the APS diagnosis to choose therapy and determine treatment duration. It requires testing for antiphospholipid antibodies, including lupus anticoagulant (LAC). In this viewpoint, we discuss the options for timing of LAC testing, which includes testing before starting anticoagulant treatment (DOAC or warfarin), after switching to heparin or after withdrawal of anticoagulant treatment. DOACs interfere with LAC testing and recommendations emerge stating not to conduct on-therapy LAC testing. All approaches are to some extent currently practised, but have limitations and the area is therefore seemingly a catch 22. We put forward that the anticoagulant effect of DOAC can be eliminated in the laboratory and therefore patients can be tested on-therapy. While it may not eliminate all cases of interference, it could aid the interpretation in these situations and this approach is attractive from the patient and clinician’s perspective. Nevertheless, to prevent misdiagnosis the diagnostic workup for APS requires collaboration between the clinician and the laboratory. We advocate for standardisation in laboratory and clinical practice when diagnosing APS.

ObjectivesAlthough causality remains to be established, targeting dysbiosis of the intestinal microbiota by faecal microbiota transplantation (FMT) has been proposed as a novel treatment for inflammatory diseases. In this exploratory, proof-of-concept study, we evaluated the safety and efficacy of FMT in psoriatic arthritis (PsA).MethodsIn this double-blind, parallel-group, placebo-controlled, superiority trial, we randomly allocated (1:1) adults with active peripheral PsA (≥3 swollen joints) despite ongoing treatment with methotrexate to one gastroscopic-guided FMT or sham transplantation into the duodenum. Safety was monitored throughout the trial. The primary efficacy endpoint was the proportion of participants experiencing treatment failure (ie, needing treatment intensification) through 26 weeks. Key secondary endpoints were change in Health Assessment Questionnaire Disability Index (HAQ-DI) and American College of Rheumatology (ACR20) response at week 26.ResultsOf 97 screened, 31 (32%) underwent randomisation (15 allocated to FMT) and 30 (97%) completed the 26-week clinical evaluation. No serious adverse events were observed. Treatment failure occurred more frequently in the FMT group than in the sham group (9 (60%) vs 3 (19%); risk ratio, 3.20; 95% CI 1.06 to 9.62; p=0.018). Improvement in HAQ-DI differed between groups (0.07 vs 0.30) by 0.23 points (95% CI 0.02 to 0.44; p=0.031) in favour of sham. There was no difference in the proportion of ACR20 responders between groups (7 of 15 (47%) vs 8 of 16 (50%)).ConclusionsIn this first preliminary, interventional randomised controlled trial of FMT in immune-mediated arthritis, we did not observe any serious adverse events. Overall, FMT appeared to be inferior to sham in treating active peripheral PsA.Trial registration number NCT03058900.

ObjectivesWe have previously shown that neural network technology can be used for scoring arthritis disease activity in ultrasound images from rheumatoid arthritis (RA) patients, giving scores according to the EULAR-OMERACT grading system. We have now further developed the architecture of this neural network and can here present a new idea applying cascaded convolutional neural network (CNN) design with even better results. We evaluate the generalisability of this method on unseen data, comparing the CNN with an expert rheumatologist.MethodsThe images were graded by an expert rheumatologist according to the EULAR-OMERACT synovitis scoring system. CNNs were systematically trained to find the best configuration. The algorithms were evaluated on a separate test data set and compared with the gradings of an expert rheumatologist on a per-joint basis using a Kappa statistic, and on a per-patient basis using a Wilcoxon signed-rank test.ResultsWith 1678 images available for training and 322 images for testing the model, it achieved an overall four-class accuracy of 83.9%. On a per-patient level, there was no significant difference between the classifications of the model and of a human expert (p=0.85). Our original CNN had a four-class accuracy of 75.0%.ConclusionsUsing a new network architecture we have further enhanced the algorithm and have shown strong agreement with an expert rheumatologist on a per-joint basis and on a per-patient basis. This emphasises the potential of using CNNs with this architecture as a strong assistive tool for the objective assessment of disease activity of RA patients.

BackgroundInflammation suppresses cytochrome P450 (CYP) enzyme activity, and single-dose interleukin 6 receptor antagonists (anti-IL-6R) reverse this effect. Here, we assess the impact of continuous anti-IL-6R therapy in patients with rheumatoid arthritis.MethodsIn a clinical pharmacokinetic trial, the Basel cocktail was administered before and after 3 and 12 weeks of anti-IL-6R therapy to assess CYP enzyme activity (registered in the ClinicalTrials.gov database (identifier NCT04842981) on April 13th, 2021). In a retrospective study, the 4β-hydroxycholesterol/cholesterol ratio was measured as a biomarker for CYP3A4 activity before and after 3 and 6 months of anti-IL-6R therapy. The control group was patients initiating a tumor necrosis factor alfa (TNF-α) inhibitor.ResultsIn the clinical pharmacokinetic trial (n = 3), midazolam metabolic ratio (CYP3A4) was inconclusive due to the limited sample size. Midazolam AUC and Cmax indicate a weak impact on CYP3A4 activity after 3 weeks of anti-IL-6R therapy compared to baseline (AUC geometric mean ratio (GMR): 0.80, 95% CI: 0.64–0.99 and Cmax GMR: 0.58, 95% CI: 0.37–0.91), which returns to baseline levels after 12 weeks of therapy (AUC GMR 1.02, 95% CI: 0.72–1.46 and Cmax GMR 1.03, 95% CI 0.72–1.47). No effect on the 4β-hydroxycholesterol/cholesterol ratio was observed in the retrospective study.ConclusionBased on sparse data from three patients, continuous anti-IL-6R therapy seems to cause an acute but transient increase in CYP3A4 activity in rheumatoid arthritis patients, which may be due to a normalization of the inflammation-suppressed CYP activity. Further studies are warranted to understand the mechanism behind this putative transient effect.Trial registration Registered in the ClinicalTrials.gov database (identifier NCT04842981) on April 13th, 2021.

The objective of this pilot study was to assess the impact of a salivary gland ultrasound (SGUS) atlas for scoring parenchymal changes in Sjögren’s syndrome by assessing the reliability of the scoring system (0–3), without and with the use of the SGUS atlas. Ten participants with varying experience in SGUS contributed to the reliability exercise. Thirty SGUS images of the submandibular and parotid gland with abnormalities ranging from 0 to 3 were scored using the written definitions of the OMERACT SGUS scoring system and using the SGUS atlas based on the OMERACT scoring system. For intra-reader reliability, two rounds were performed without and with the atlas—in the 2nd round the 30 images were rearranged in random order by a physician not included in the scoring. Inter-reader reliability was also determined in both rounds. Without using the atlas, the SGUS OMERACT scoring system showed fair inter-reader reliability in round 1 (mean kappa 0.36; range 0.06–0.69) and moderate intra-reader reliability (mean kappa 0.55; range 0.28–0.81). With the atlas, inter-reader reliability improved in round 1 to moderate (mean kappa 0.52; range 0.31–0.77) and intra-reader reliability to good (mean kappa 0.69; range 0.46–0.86). Higher intra-reader reliability was noted in participants with previous SGUS experience. The SGUS atlas increased both intra- and inter-reader reliability for scoring gland pathology in participants with varying SGUS experience suggesting a possible future role in clinical practice and trials.Key Points• Ultrasonography can detect parenchymal changes in salivary glands in patients with Sjögren’s disease.• An ultrasound atlas may improve reliability of scoring parenchymal changes in salivary glands.