Classification Criteria for Fuchs Uveitis Syndrome.

ABSTRACT

PURPOSE: To determine classification criteria for Fuchs' uveitis syndrome. DESIGN: Machine learning of cases with Fuchs' uveitis syndrome 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 146 cases of Fuchs' uveitis syndrome, 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 Fuchs' uveitis syndrome included unilateral anterior uveitis with or without vitritis and either 1) heterochromia or 2) unilateral diffuse iris atrophy and stellate keratic precipitates. The misclassification rates for Fuchs' uveitis syndrome were 4.7% in the training set and 5.5% in the validation set, respectively. CONCLUSIONS: The criteria for Fuchs' uveitis syndrome had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.