TY - JOUR
T1 - EULAR/ACR classification criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups
T2 - A methodology report
AU - International Myositis Classification Criteria Project consortium, the Euromyositis register and the Juvenile Dermatomyositis Cohort Biomarker Study and Repository (JDRG) (UK and Ireland)
AU - Bottai, Matteo
AU - Tjärnlund, Anna
AU - Santoni, Giola
AU - Werth, Victoria P.
AU - Pilkington, Clarissa
AU - De Visser, Marianne
AU - Alfredsson, Lars
AU - Amato, Anthony A.
AU - Barohn, Richard J.
AU - Liang, Matthew H.
AU - Singh, Jasvinder A.
AU - Aggarwal, Rohit
AU - Arnardottir, Snjolaug
AU - Chinoy, Hector
AU - Cooper, Robert G.
AU - Danko, Katalin
AU - Dimachkie, Mazen M.
AU - Feldman, Brian M.
AU - García-De La Torre, Ignacio
AU - Gordon, Patrick
AU - Hayashi, Taichi
AU - Katz, James D.
AU - Kohsaka, Hitoshi
AU - Lachenbruch, Peter A.
AU - Lang, Bianca A.
AU - Li, Yuhui
AU - Oddis, Chester V.
AU - Olesinka, Marzena
AU - Reed, Ann M.
AU - Rutkowska-Sak, Lidia
AU - Sanner, Helga
AU - Selva-O'Callaghan, Albert
AU - Song, Yeong Wook
AU - Vencovsky, Jiri
AU - Ytterberg, Steven R.
AU - Miller, Frederick W.
AU - Rider, Lisa G.
AU - Lundberg, Ingrid E.
AU - Amoruso, Maria
AU - Andersson, Helena
AU - Bayat, Nastaran
AU - Bhansing, Kavish J.
AU - Bucher, Sara
AU - Champbell, Richard
AU - Charles-Schoeman, Christina
AU - Chaudhry, Vinay
AU - Christopher-Stine, Lisa
AU - Chung, Lorinda
AU - Cronin, Mary
AU - Curry, Theresa
N1 - Publisher Copyright:
© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
PY - 2017
Y1 - 2017
N2 - Objective T o describe the methodology used to develop new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIMs) and their major subgroups. Methods A n 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-ofitems approach criteria and (3) a classification-tree approach. Results T he approaches yielded several candidate models that were scrutinised with respect to statistical performance and clinical relevance. The probabilityscore 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.
AB - Objective T o describe the methodology used to develop new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIMs) and their major subgroups. Methods A n 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-ofitems approach criteria and (3) a classification-tree approach. Results T he approaches yielded several candidate models that were scrutinised with respect to statistical performance and clinical relevance. The probabilityscore 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.
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U2 - 10.1136/rmdopen-2017-000507
DO - 10.1136/rmdopen-2017-000507
M3 - Review article
AN - SCOPUS:85049801528
SN - 2056-5933
VL - 3
JO - RMD Open
JF - RMD Open
IS - 2
M1 - e000507
ER -