Purpose: To determine classification criteria for acute retinal necrosis (ARN).
Design: Machine learning of cases with ARN and 4 other infectious posterior uveitides / panuveitides.
Methods: Cases of infectious posterior uveitides / 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 uveitides / panuveitides. The resulting criteria were evaluated on the validation set.
Results: Eight hundred three cases of infectious posterior uveitides / 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 uveitides / 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 seemed to perform sufficiently well for use in clinical and translational research.
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