Arthur Vick Prize 2017 of the German Society of Orthopaedic Rheumatology,Arthur-Vick-Preis der Deutschen Gesellschaft für Orthopädische Rheumatologie 2017

The German Society of Orthopaedic Rheumatology (DGORh) honored Prof. Dr. med. Veit Krenn (MVZ-ZHZMD-Trier) with the Arthur Vick Prize 2017. With this award, scientific results with high impact on the diagnosis, therapy and pathogenetic understanding of rheumatic diseases are honored. In cooperation with pathologists and colleagues from various clinical disciplines Prof. Dr. med. Veit Krenn developed several histopathologic scoring systems which contribute to the diagnosis and pathogenetic understanding of degenerative and rheumatic diseases. These scores include the synovitis score, the meniscal degeneration score, the classification of periprosthetic tissues (SLIM classification), the arthrofibrosis score, the particle score and the CD15 focus score. Of highest relevance for orthopedic rheumatology is the synovitis score which is a semiquantitative score for evaluating immunological and inflammatory changes of synovitis in a graded manner. Based on this score, it is possible to divide results into low-grade synovitis and high-grade synovitis: a synovitis score of 1-4 is called low-grade synovitis and occurs for example in association with osteoarthritis (OA), post-trauma, with meniscal lesions and hemochromatosis. A synovitis score of 5-9 is called high-grade synovitis, e.g. rheumatoid arthritis, psoriatic arthritis, Lyme arthritis, postinfection and reactive arthritis as well as peripheral arthritis with Bechterew's disease (sensitivity 61.7%, specificity 96.1%). The first publication (2002) and an associated subsequent publication (2006) of the synovitis score has led to national and international acceptance of this score as the standard for histopathological assessment of synovitis. The synovitis score provides a diagnostic, standardized and reproducible histopathological evaluation method for joint diseases, particularly when this score is applied in the context with the joint pathology algorithm.