Classification Criteria for Juvenile Idiopathic Arthritis–Associated Chronic Anterior Uveitis

Autor: Philip I. Murray, Peter McCluskey, Nisha R. Acharya, Debra A. Goldstein, Jennifer E. Thorne, Soon-Phaik Chee, Alan G. Palestine, Douglas A. Jabs, James T. Rosenbaum, Brett Trusko, Neal Oden
Rok vydání: 2021
Předmět:
Zdroj: Am J Ophthalmol
ISSN: 0002-9394
Popis: 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, respectively. Conclusions The criteria for JIA CAU had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.
Databáze: OpenAIRE