LAUSR

PURPOSE To determine classification criteria for toxoplasmic retinitis. DESIGN Machine learning of cases with toxoplasmic retinitis 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 174 cases of toxoplasmic retinitis, were evaluated by machine learning. Key criteria for toxoplasmic retinitis included: 1) focal or paucifocal necrotizing retinitis and either; 2) positive polymerase chain reaction assay for Toxoplasma gondii from an intraocular specimen or 3) the characteristic clinical picture of a round or oval retinitis lesion proximal to a hyperpigmented and/or atrophic chorioretinal scar. 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 toxoplasmic retinitis were 8.2% in the training set and 10% in the validation set. CONCLUSIONS The criteria for toxoplasmic retinitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.