A clinical prediction model for estimating the risk of developing uveitis in patients with juvenile idiopathic arthritis

Abstract

Objective To build a prediction model for uveitis in children with JIA for use in current clinical practice. Methods Data from the international observational Pharmachild registry were used. Adjusted risk factors as well as predictors for JIA-associated uveitis (JIA-U) were determined using multivariable logistic regression models. The prediction model was selected based on the Akaike information criterion. Bootstrap resampling was used to adjust the final prediction model for optimism. Results JIA-U occurred in 1102 of 5529 JIA patients (19.9%). The majority of patients that developed JIA-U were female (74.1%), ANA positive (66.0%) and had oligoarthritis (59.9%). JIA-U was rarely seen in patients with systemic arthritis (0.5%) and RF positive polyarthritis (0.2%). Independent risk factors for JIA-U were ANA positivity [odds ratio (OR): 1.88 (95% CI: 1.54, 2.30)] and HLA-B27 positivity [OR: 1.48 (95% CI: 1.12, 1.95)] while older age at JIA onset was an independent protective factor [OR: 0.84 (9%% CI: 0.81, 0.87)]. On multivariable analysis, the combination of age at JIA onset [OR: 0.84 (95% CI: 0.82, 0.86)], JIA category and ANA positivity [OR: 2.02 (95% CI: 1.73, 2.36)] had the highest discriminative power among the prediction models considered (optimism-adjusted area under the receiver operating characteristic curve = 0.75). Conclusion We developed an easy to read model for individual patients with JIA to inform patients/parents on the probability of developing uveitis.

Triage by PAX1 and ZNF582 Methylation in Women With Cervical Intraepithelial Neoplasia Grade 3: A Multicenter Case–Control Study

“…Subsequently, ΔCp of PAX1 and ZNF582 were considered for inclusion into a multivariable logistic regression model to predict the primary outcome. The model was developed by assessing the area under the receiver operating characteristic curve (AUC) in the training data and 10-fold cross-validation, validated, and adjusted by bootstrap resampling [ 28 , 29 ]. The final formula was as follows: ΔCp Model = 0.1*ΔCp PAX1 + 0.9*ΔCp ZNF582 + 2.032.…”

Uveitis Is a Risk Factor for Juvenile Idiopathic Arthritis' Significant Flare in Patients Treated With Biologics

“…Uveitis might be a risk factor of more severe course of JIA. In previous studies, it was shown that the JIA category, e.g., oligoarthritis, is an independent risk factor of uveitis when combined with ANA-positivity and early age of the JIA onset ( 13 – 15 ). It is well-established that persistent oligoarthritis and extended oligoarthritis have the highest probability of developing uveitis, while, in systemic and RF-positive categories, uveitis occurs extremely rarely ( 5 , 14 , 15 ).…”

Clinical Features and Risk Factors of Uveitis in Korean Children with Juvenile Idiopathic Arthritis

“…By contrast, the anti-CCP antibody was negative in JIA-U patients of this study, as previous studies. 2,4,9 Anti-CCP antibody positivity is more common in RF-positive polyarthritis, which has an aggressive and erosive disease course, but a lower uveitis occurrence rate. 25,26 Consistent with previous studies which reported RF-positive polyarthritis was protective against JIA-U, 4,21 uveitis did not occur in RF-positive polyarthritis in our study.…”

“…3,4,8−10 On the other hand, late onset of JIA, rheumatoid factor (RF)-positive polyarthritis, systemic arthritis, and positivity of anti-cyclic citrullinated peptide (CCP) antibodies are suggested as protective factors against JIA-U. 2,4,9 Nevertheless, there are still few studies on the clinical features and risk factors for JIA-U in Asian or Korean children. Identifying patients at higher risk of JIA-U is important for the appropriate screening, early diagnosis, and timely treatment of uveitis, as well as for understanding the pathophysiology of JIA and JIA-U.…”