Outcome Scores in Pediatric Rheumatology.

PURPOSE OF REVIEW: Providing a summary of the latest research on outcome measures in juvenile idiopathic arthritis, childhood -onset systemic lupus erythematosus, and juvenile dermatomyositis. RECENT FINDINGS: A rational management of patients with pediatric rheumatic diseases requires the regular assessment of the level of disease activity and damage, as well as the evaluation of therapeutic response through validated and standardized outcome measures. Ideally, such tools should be simple, feasible, and easily applicable in routine care. Recently, there has been a great deal of effort to refine existing tools and devise novel outcome measures, aiming to address the various aspects of disease impact and to improve the reliability of research studies and clinical trials. The newest outcome tools in pediatric rheumatology have markedly enlarged the spectrum of health domains assessable in a standardized way, thus increasing the reliability of evaluation of clinical response and fostering future clinical trials.

Definition of improvement in juvenile idiopathic arthritis using the juvenile arthritis disease activity score.

OBJECTIVE: The aim of this study was to define improvement thresholds for the Juvenile Arthritis Disease Activity Score (JADAS). METHODS: Physicians' and parents' judgements on treatment efficacy, the ACR paediatric response measure (PedACR) and JADAS were extracted from BIKER. Patients were categorized by baseline classes in the 10-joint JADAS (JADAS10) as low (5 to <15), moderate (15 to <25) and high (25 to ≤40). Cut-offs for defining improvement following treatment with biologics or MTX were chosen by calculating the interquartile ranges (IQRs) of the judgement groups and considering the accuracy, sensitivity and specificity of the resulting model. Differences in the change of JADAS10 by JIA category were also analysed by analysis of variance (ANOVA). Sensitivity, specificity and accuracy were calculated. RESULTS: A total of 1315 treatment courses were analysed. The ANOVA of the JIA categories showed no significant differences of the mean JADAS10 in all baseline classes and IQRs also showed good overall limits. Therefore all JIA categories were combined for a collective cut-off. Analysis by baseline class revealed clear cut-off points. Improvement could be defined by the minimal decrease in the JADAS10 in baseline class low by 4 (41%), moderate by 10 (53%) and high by 17 (57%). The model shows values for accuracy from 75.6 to 85.5% and comparable values for sensitivity and specificity. CONCLUSION: Improvement after 3 months can be defined efficiently by the decrease of the JADAS10, depending on the baseline JADAS10 score, which specifies low, moderate or high disease activity. Our model demonstrates clear cut-off values. The JADAS10 may be used in addition to ACR criteria in clinical trials. Also, since the JADAS10 can easily be calculated at each patient visit, it also can be used for clinical decisions.