Predicting Which Children with Juvenile Idiopathic Arthritis Will Have a Severe Disease Course: Results from the ReACCh-Out Cohort

Abstract

Objective.We studied an inception cohort of children with juvenile idiopathic arthritis (JIA) to (1) identify distinct disease courses based on changes over 5 years in 5 variables prioritized by patients, parents, and clinicians; and (2) estimate the probability of a severe disease course for each child at diagnosis.Methods.Assessments of quality of life, pain, medication requirements, patient-reported side effects, and active joint counts were scheduled at 0, 6, 12, 18, 24, 36, 48, and 60 months. Patients who attended at least 6 assessments were included. Multivariable cluster analysis, r2, and silhouette statistics were used to identify distinct disease courses. One hundred candidate prediction models were developed in random samples of 75% of the cohort; their reliability and accuracy were tested in the 25% not used in their development.Results.Four distinct courses were identified in 609 subjects. They differed in prioritized variables, disability scores, and probabilities of attaining inactive disease and remission. We named them Mild (43.8% of children), Moderate (35.6%), Severe Controlled (9%), and Severe Persisting (11.5%). A logistic regression model using JIA category, active joint count, and pattern of joint involvement at enrollment best predicted a severe disease course (Controlled + Persisting, c-index = 0.87); 91% of children in the highest decile of risk actually experienced a severe disease course, compared to 5% of those in the lowest decile.Conclusion.Children in this JIA cohort followed 1 of 4 disease courses and the probability of a severe disease course could be estimated with information available at diagnosis.