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Updated: Aug 30, 2025
Experimental Autoimmune Uveitis: An Intraocular Inflammatory Mouse Model
Published on: January 12, 2022
Development and External Validation of a Model Predicting New-Onset Chronic Uveitis at Different Disease Durations in
Joeri W van Straalen1, Lianne Kearsley-Fleet2, Jens Klotsche3
1Department of Pediatric Immunology and Rheumatology, Wilhelmina Children's Hospital, University Medical Center Utrecht, and Faculty of Medicine, Utrecht University, Utrecht, The Netherlands.
Insights
This study developed a validated tool to predict chronic uveitis in children with juvenile idiopathic arthritis (JIA). The model uses clinical factors to help guide screening and treatment decisions for JIA patients.
Area of Science:
- Rheumatology
- Ophthalmology
- Clinical Prediction Modeling
Background:
- Juvenile idiopathic arthritis (JIA) is associated with an increased risk of chronic uveitis.
- Early detection and management of uveitis in JIA are crucial to prevent vision loss.
- Predicting individual risk of uveitis in JIA patients is challenging.
Purpose of the Study:
- To develop and externally validate a prediction model for new-onset chronic uveitis in children with JIA.
- To create a clinical tool for risk assessment and management guidance.
Main Methods:
- A multivariable Cox proportional hazards model was developed using data from the international Pharmachild registry.
- Predictors were selected via backward selection, with missing values handled by multiple imputation.
- The model was validated and recalibrated in two independent inception cohorts (CAPS and ICON).
Main Results:
- The final model included age at JIA onset, ANA positivity, and JIA category.
- The prediction model demonstrated acceptable performance with good calibration in validation cohorts.
- C statistics for 7-year uveitis risk were 0.75 (ICON) and 0.70 (CAPS).
Conclusions:
- A validated prognostic tool for predicting chronic uveitis risk in individual JIA patients was developed.
- The tool utilizes common clinical parameters, facilitating clinical application.
- This model can inform patient/parent counseling and guide uveitis screening frequency and treatment choices.
Objective:
To develop and externally validate a prediction model for new-onset chronic uveitis in children with juvenile idiopathic arthritis (JIA) for clinical application.
Methods:
Data from the international Pharmachild registry were used to develop a multivariable Cox proportional hazards model. Predictors were selected by backward selection, and missing values were handled by multiple imputation. The model was subsequently validated and recalibrated in 2 inception cohorts: the UK Childhood Arthritis Prospective Study (CAPS) study and the German Inception Cohort of Newly diagnosed patients with juvenile idiopathic arthritis (ICON) study. Model performance was evaluated by calibration plots and C statistics for the 2-, 4-, and 7-year risk of uveitis. A diagram and digital risk calculator were created for use in clinical practice.
Results:
A total of 5,393 patients were included for model development, and predictor variables were age at JIA onset (hazard ratio [HR] 0.83 [95% confidence interval (95% CI) 0.77-0.89]), ANA positivity (HR 1.59 [95% CI 1.06-2.38]), and International League of Associations for Rheumatology category of JIA (HR for oligoarthritis, psoriatic arthritis, and undifferentiated arthritis versus rheumatoid factor-negative polyarthritis 1.40 [95% CI 0.91-2.16]). Performance of the recalibrated prediction model in the validation cohorts was acceptable; calibration plots indicated good calibration and C statistics for the 7-year risk of uveitis (0.75 [95% CI 0.72-0.79] for the ICON cohort and 0.70 [95% CI 0.64-0.76] for the CAPS cohort).
Conclusion:
We present for the first time a validated prognostic tool for easily predicting chronic uveitis risk for individual JIA patients using common clinical parameters. This model could be used by clinicians to inform patients/parents and provide guidance in choice of uveitis screening frequency and arthritis drug therapy.
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