Classification Criteria for Juvenile Idiopathic Arthritis-Associated Chronic Anterior Uveitis

PURPOSE: To determine classification criteria for juvenile idiopathic arthritis (JIA)-associated chronic anterior uveitis (CAU).

DESIGN: Machine learning of cases with JIA CAU and 8 other anterior uveitides.

METHODS: Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set.

RESULTS: One thousand eighty-three cases of anterior uveitides, including 202 cases of JIA CAU, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for JIA CAU included (1) chronic anterior uveitis (or, if newly diagnosed, insidious onset) and (2) JIA, except for the systemic, rheumatoid factor-positive polyarthritis, and enthesitis-related arthritis variants. The misclassification rates for JIA CAU were 2.4% in the training set and 0% in the validation set.

CONCLUSIONS: The criteria for JIA CAU had a low misclassification rate and seemed to perform well enough for use in clinical and translational research.