PT - JOURNAL ARTICLE AU - Matteo Bottai AU - Anna Tjärnlund AU - Giola Santoni AU - Victoria P Werth AU - Clarissa Pilkington AU - Marianne de Visser AU - Lars Alfredsson AU - Anthony A Amato AU - Richard J Barohn AU - Matthew H Liang AU - Jasvinder A Singh AU - Rohit Aggarwal AU - Snjolaug Arnardottir AU - Hector Chinoy AU - Robert G Cooper AU - Katalin Danko AU - Mazen M Dimachkie AU - Brian M Feldman AU - Ignacio García-De La Torre AU - Patrick Gordon AU - Taichi Hayashi AU - James D Katz AU - Hitoshi Kohsaka AU - Peter A Lachenbruch AU - Bianca A Lang AU - Yuhui Li AU - Chester V Oddis AU - Marzena Olesinka AU - Ann M Reed AU - Lidia Rutkowska-Sak AU - Helga Sanner AU - Albert Selva-O’Callaghan AU - Yeong Wook Song AU - Jiri Vencovsky AU - Steven R Ytterberg AU - Frederick W Miller AU - Lisa G Rider AU - Ingrid E Lundberg ED - , TI - EULAR/ACR classification criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups: a methodology report AID - 10.1136/rmdopen-2017-000507 DP - 2017 Nov 01 TA - RMD Open PG - e000507 VI - 3 IP - 2 4099 - http://rmdopen.bmj.com/content/3/2/e000507.short 4100 - http://rmdopen.bmj.com/content/3/2/e000507.full SO - RMD Open2017 Nov 01; 3 AB - Objective To describe the methodology used to develop new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIMs) and their major subgroups.Methods An international, multidisciplinary group of myositis experts produced a set of 93 potentially relevant variables to be tested for inclusion in the criteria. Rheumatology, dermatology, neurology and paediatric clinics worldwide collected data on 976 IIM cases (74% adults, 26% children) and 624 non-IIM comparator cases with mimicking conditions (82% adults, 18% children). The participating clinicians classified each case as IIM or non-IIM. Generally, the classification of any given patient was based on few variables, leaving remaining variables unmeasured. We investigated the strength of the association between all variables and between these and the disease status as determined by the physician. We considered three approaches: (1) a probability-score approach, (2) a sum-of-items approach criteria and (3) a classification-tree approach.Results The approaches yielded several candidate models that were scrutinised with respect to statistical performance and clinical relevance. The probability-score approach showed superior statistical performance and clinical practicability and was therefore preferred over the others. We developed a classification tree for subclassification of patients with IIM. A calculator for electronic devices, such as computers and smartphones, facilitates the use of the European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria.Conclusions The new EULAR/ACR classification criteria provide a patient’s probability of having IIM for use in clinical and research settings. The probability is based on a score obtained by summing the weights associated with a set of criteria items.