A47: Progress Report on the Development of New Classification Criteria for Adult and Juvenile Idiopathic Inflammatory Myopathies. Issue 11 (March 2014)

• Record Type: Journal Article
• Authors: Pilkington, Clarissa; Tjärnlund, Anna; Bottai, Matteo; Rider, Lisa G; Werth, Victoria P; Visser, Marianne de; Alfredsson, Lars; Amato, Anthony A; Barohn, Richard J; Liang, Matthew H; Singh, Jasvinder A; Miller, Frederick W; Lundberg, Ingrid E.
• Is Part Of: Arthritis & rheumatology. Volume 66:Issue 11(2014)supplement
• Page Start: S70
• Page End: S71
• Publication Date: 2014-03
• Subjects: Arthritis -- Periodicals; Rheumatism -- Periodicals; 616.72
• DOI: 10.1002/art.38463
• Languages: English
• ISSNs: 2326-5191

Abstract

Abstract : Background/Purpose: Inadequate classification criteria for IIM are a fundamental limitation in clinical studies. An international, multidisciplinary collaboration, the International Myositis Classification Criteria Project (IMCCP), supported by ACR and EULAR, was established to address this problem. Methods: Identification and definition of potential criterion: Candidate variables to be included in classification criteria were assembled from published criteria and inclusion criteria in controlled trials of myositis and refined using Nominal Group Technique. Comparator groups confused with IIM were defined. Data collection: Within this retrospective case control study, clinical and laboratory data from IIM and comparator patients were collected from 47 rheumatology, dermatology, neurology and pediatrics clinics worldwide from 2008–2011. Analysis: Crude pair‐wise associations among all variables measured and between each variable and clinician's diagnosis were assessed. Three approaches for derivation of classification criteria were explored: Traditional: case defined by specified number of items from a set Risk score: patient assigned a probability risk score by summing score‐points associated with the variables (Probability model 1 and 2) Classification tree: case defined by a decision tree A random forest algorithm explored the most important variables. Results obtained with each approach were utilized to improve others iteratively. Validation: Internal validation using bootstrap methods was performed. External validation using extracted data from the Euromyositis register and an UK juvenile myositis register was performed. Results: Data from 973 IIM patients (74% adults;26% children), representing subgroups of IIM (245 polymyositis, 239 dermatomyositis, 176 inclusion body myositis and 246 juvenile dermatomyositis cases) and 629 comparators (81% adults; 19% children) were obtained. The comparators include other myopathies and systemic rheumatic diseases. Two probability score models were developed (Table 1 ): Model 1 comprised clinical variables on muscles, skin, and laboratory measures; Model 2 additionally comprised muscle biopsy variables. Model 1 performed nearly as well as Model 2 and both models performed as well as and often better than, the classification tree that was developed and published criteria. External validation using data on 2363 myositis patients in the Euromyositis register resulted in >99% sensitivity, and using 332 juvenile myositis cases resulted in 100% sensitivity for both probability models.2 Conclusion: New classification criteria for IIM with readily clinically assessable measurements and symptoms have been developed. They generally show superior performance compared with existing criteria.