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.
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.
Authors: Ross E Petty; Taunton R Southwood; Prudence Manners; John Baum; David N Glass; Jose Goldenberg; Xiaohu He; Jose Maldonado-Cocco; Javier Orozco-Alcala; Anne-Marie Prieur; Maria E Suarez-Almazor; Patricia Woo Journal: J Rheumatol Date: 2004-02 Impact factor: 4.666
Authors: Fasika Woreta; Jennifer E Thorne; Douglas A Jabs; Sanjay R Kedhar; James P Dunn Journal: Am J Ophthalmol Date: 2006-12-20 Impact factor: 5.258
Authors: Paul A Latkany; Douglas A Jabs; Justine R Smith; James T Rosenbaum; Howard Tessler; Ivan R Schwab; R Christopher Walton; Jennifer E Thorne; Albert M Maguire Journal: Am J Ophthalmol Date: 2002-12 Impact factor: 5.258
Authors: Timothy Beukelman; Sarah Ringold; Trevor E Davis; Esi Morgan DeWitt; Christina F Pelajo; Pamela F Weiss; Yukiko Kimura Journal: J Rheumatol Date: 2012-08-01 Impact factor: 4.666