Predictors of juvenile idiopathic arthritis course.

Reumatologia

State Institution "Institute of Paediatrics, Midwifery and Gynaecology of the National Academy of Medical Sciences (NAMSc) of Ukraine", Kyiv, Ukraine.

Published: July 2016

Introduction: Juvenile idiopathic arthritis (JIA) is a heterogeneous group of inflammatory diseases of joints in children with various and often unfavourable prognosis. It is possible to improve the outcome of the disease in patients with JIA by a correct therapeutic choice made at disease onset - one that enables fast achievement of an inactive disease state and remission. The aim of the investigation was to develop a model/application for automatic calculation of risk of treatment-refractory JIA taking into account the combined action of clinical and cytokine factors.

Material And Methods: Disease subtype was determined in 105 patients with JIA, as well as the number of poor prognostic factors and disease activity level. Blood serum cytokine levels (IL-1β, IL-4, IL-6, IL-8, IL-10, IL-17, TNF-α, IFN-γ) and their soluble receptors and agonists - interleukin 1 receptor agonist (IL-1Ra), soluble interleukin 2 receptor (sCD25), interleukin 6 (sIL6R), soluble tumour necrosis factor receptor 1 (sTNFR1) - were determined in these patients using immunoenzymatic laboratory methods.

Results: The following prognostic factors were taken into account in the study: JIA subtype, disease activity, presence of clinically unfavourable factors and cytokine characteristics. We determined that systemic subtype of JIA, moderate and high disease activity, presence of factors of poor disease course and sCD25 and IL-6 levels are statistically significant factors of treatment-refractory disease course. A Microsoft Excel application was developed for automatic calculation of risk of treatment-refractory JIA in a specific patient based on 10 factors.

Conclusions: Use of an application for automatic calculation of risk of treatment-refractory JIA enables prediction of JIA disease course in patients at disease onset and personalization of the treatment protocol.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4847301PMC
http://dx.doi.org/10.5114/reum.2015.53132DOI Listing

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