What are the topics you care about making trials in lupus more effective? Results of an Open Space meeting of international lupus experts

The meeting was funded by the 'Rheumazentrum Rhein-Ruhr'.Despite promising candidates for new therapeutic options in the treatment of systemic lupus erythematosus (SLE), many clinical trials have failed in the past few years. The disappointing results have been at least partly be attributed to trial designs. With the aim of stimulating new developments in SLE trial design, an international open space meeting was held on occasion of the European Lupus Meeting 2018 in Duesseldorf, Germany about ‘What are the topics you care about for making trials in lupus more effective?’. The Open Space is a participant-driven technology, where the discussion topics and schedule are selected during the meeting by all participants and discussion rounds are led by the people attending encouraging active contributions. Eleven topics were selected for further discussion, of which 6 were voted to be more intensively discussed in two consecutive rounds. Major topics were the optimal handling of glucocorticoids in clinical trials, the improvement of outcome measures, reducing or controlling the placebo response and the identification of biomarkers and stratification parameters. Further, the importance of local and international networks was emphasised. By networking, collaborations are facilitated, patient recruitment is more efficient and treatment can be harmonised thus lead to more successful SLE trials. Further discussions are needed to substantiate the results and develop new trial designs.Rheumazentrum Rhein-Ruh

Sicherheit antirheumatischer medikamentöser Therapien

BACKGROUND Drug therapy for rheumatic diseases has changed fundamentally in recent decades with the introduction of many new agents. As these drugs may have to be taken for many years, and many of them are of similar efficacy, their safety profiles play an important role in therapeutic decisionmaking. METHODS This review is based on pertinent literature retrieved by a selective search on the safety profiles of selected antirheumatic drugs. RESULTS Non-steroidal antirheumatic drugs, glucocorticoids, conventional disease-modifying drugs such as methotrexate, biological agents, and janus kinase (JAK) inhibitors are all used to treat rheumatic diseases. Register and trial data show that antirheumatic treatments are relatively safe. Infections, in particular, are much less common than initially expected. Cortisone administration is an exception because of its severe long-term sequelae. Biological agents are associated with severe infectious events at a rate of 4-5 events per 100 patient years. Screening before treatment with biological agents has been shown to lower the rate of tuberculosis from 564 to 95 cases per 100 000 patient years. JAK inhibitors have a good safety profile, with respect to infections as well, but there is evidence of their association with cardiovascular problems, malignancies, and thrombosis. CONCLUSION A suitable, safe antirheumatic drug can be chosen for each patient in consideration of individual risk profiles. Regular monitoring enables the early detection of adverse effects. The risk profile of JAK inhibitors, in particular, will be studied in further trials

A message passing framework with multiple data integration for miRNA-disease association prediction

Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach’s superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption.Multimedia Computin

Inhomogeneity of immune cell composition in the synovial sublining: linear mixed modelling indicates differences in distribution and spatial decline of CD68+ macrophages in osteoarthritis and rheumatoid arthritis

BACKGROUND: Inhomogeneity of immune cell distribution in the synovial sublining layer was analyzed in order to improve our mechanistic understanding of synovial inflammation and explore potential refinements for histological biomarkers in rheumatoid arthritis (RA) and osteoarthritis (OA). METHODS: Synovial tissue of 20 patients (11 RA, 9 OA) was immunohistochemically stained for macrophages (CD68), synovial fibroblasts (CD55), T cells (CD3), plasma cells (CD38), endothelial cells (vWF) and mast cells (MCT). The synovial sublining layer was divided into predefined adjacent zones and fractions of the stained area (SA) were determined by digital image analysis for each cell marker. RESULTS: Distribution of CD68, CD55, CD38 and MCT staining of the sublining area was heterogeneous (Friedman ANOVA p < 0.05). The highest expression for all markers was observed in the upper layer close to the lining layer with a decrease in the middle and lower sublining. The SA of CD68, CD55 and CD38 was significantly higher in all layers of RA tissue compared to OA (p < 0.05), except the CD38 fraction of the lower sublining. Based on receiver operating characteristics analysis, CD68 SA of the total sublining resulted in the highest area under the curve (AUC 0.944, CI 95 % 0.844–1.0, p = 0.001) followed by CD68 in the upper and middle layer respectively (both AUC 0.933, CI 95 % 0.816–1.0, p = 0.001) in both RA and OA. Linear mixed modelling confirmed significant differences in the SA of sublining CD68 between OA and RA (p = 0.0042) with a higher concentration of CD68+ towards the lining layer and more rapid decline towards the periphery of the sublining in RA compared to OA (p = 0.0022). CONCLUSIONS: Immune cells are inhomogeneously distributed within the sublining layer. RA and OA tissue display differences in the number of CD68 macrophages and differences in CD68 decline within the synovial sublining

Digital Health Transition in Rheumatology: A Qualitative Study

The global COVID-19 pandemic has led to drastic changes in the management of patients with rheumatic diseases. Due to the imminent risk of infection, monitoring intervals of rheumatic patients have prolonged. The aim of this study is to present insights from patients, rheumatologists, and digital product developers on the ongoing digital health transition in rheumatology. A qualitative and participatory semi-structured fishbowl approach was conducted to gain detailed insights from a total of 476 participants. The main findings show that digital health and remote care are generally welcomed by the participants. Five key themes emerged from the qualitative content analysis: (1) digital rheumatology use cases, (2) user descriptions, (3) adaptation to different environments of rheumatology care, and (4) potentials of and (5) barriers to digital rheumatology implementation. Codes were scaled by positive and negative ratings as well as on micro, meso, and macro levels. A main recommendation resulting from the insights is that both patients and rheumatologists need more information and education to successfully implement digital health tools into clinical routine

Comparison of physician and artificial intelligence-based symptom checker diagnostic accuracy

Graf M, Knitza J, Leipe J, et al. Comparison of physician and artificial intelligence-based symptom checker diagnostic accuracy. Rheumatology International. 2022.Symptom checkers are increasingly used to assess new symptoms and navigate the health care system. The aim of this study was to compare the accuracy of an artificial intelligence (AI)-based symptom checker (Ada) and physicians regarding the presence/absence of an inflammatory rheumatic disease (IRD). In this survey study, German-speaking physicians with prior rheumatology working experience were asked to determine IRD presence/absence and suggest diagnoses for 20 different real-world patient vignettes, which included only basic health and symptom-related medical history. IRD detection rate and suggested diagnoses of participants and Ada were compared to the gold standard, the final rheumatologists' diagnosis, reported on the discharge summary report. A total of 132 vignettes were completed by 33 physicians (mean rheumatology working experience 8.8 (SD 7.1) years). Ada's diagnostic accuracy (IRD)was significantly higher compared to physicians (70 vs 54%, p=0.002) according to top diagnosis. Ada listed the correct diagnosis more often compared to physicians (54 vs 32%, p<0.001) as top diagnosis as well as among the top 3 diagnoses (59 vs 42%, p<0.001). Work experience was not related to suggesting the correct diagnosis or IRD status. Confined to basic health and symptom-related medical history, the diagnostic accuracy of physicians was lower compared to an AI-based symptom checker. These results highlight the potential of using symptom checkers early during the patient journey and importance of access to complete and sufficient patient information to establish a correct diagnosis. © 2022. The Author(s)

A Symptom Checker App Is Better at Classifying Inflammatory Disease Than Physicians Who Are Presented Identical Information

Graef M, Knitza J, Leipe J, et al. A Symptom Checker App Is Better at Classifying Inflammatory Disease Than Physicians Who Are Presented Identical Information. In: American College of Rheumatology, ed. ABSTRACT SUPPLEMENT ACR Convergence 2022, November 10–14, 2022, Philadelphia, PA. Arthritis &amp; Rheumatology. Vol 74. Hoboken: Wiley; 2022: 3511-3513