LAUSR

PURPOSE To determine classification criteria for acute retinal necrosis (ARN). DESIGN Machine learning of cases with ARN and 4 other infectious posterior/ panuveitides. METHODS Cases of infectious posterior/panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on 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 infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set. RESULTS Eight hundred three cases of infectious posterior/panuveitides, including 186 cases of ARN, were evaluated by machine learning. Key criteria for ARN included: 1) peripheral necrotizing retinitis; and either 2) polymerase chain reaction assay of an intraocular fluid specimen positive for either herpes simplex virus or varicella zoster virus; or 3) a characteristic clinical appearance with circumferential or confluent retinitis, retinal vascular sheathing and/or occlusion, and more than minimal vitritis. Overall accuracy for infectious posterior/panuveitides was 92.1% in the training set and 93.3% (95% confidence interval 88.2, 96.3) in the validation set. The misclassification rates for ARN were 15% in the training set and 11.5% in the validation set. CONCLUSIONS The criteria for ARN had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.