Classification Criteria for Cytomegalovirus Anterior Uveitis

PURPOSE: To determine classification criteria for cytomegalovirus (CMV) anterior uveitis. DESIGN: Machine learning of cases with CMV anterior uveitis 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 89 cases of CMV anterior uveitis, 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 CMV anterior uveitis included unilateral anterior uveitis with a positive aqueous humor polymerase chain reaction assay for CMV. No clinical features reliably diagnosed CMV anterior uveitis. The misclassification rates for CMV anterior uveitis were 1.3% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for CMV anterior uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

Machine learning was used to determine classification criteria for anterior uveitides, including 89 cases of CMV anterior uveitis with key criteria of unilateral anterior uveitis and positive CMV polymerase chain reaction assay in aqueous humor. The misclassification rates were low, making the criteria suitable for clinical and translational research purposes.