Classification Criteria for Syphilitic Uveitis

PURPOSE: To determine classification criteria for syphilitic uveitis DESIGN: Machine learning of cases with syphilitic uveitis and 24 other uveitides. METHODS: Cases of anterior, intermediate, posterior, and panuveitides 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 analyzed by anatomic class, and each class was 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 different uveitic classes. The resulting criteria were evaluated on the validation set. RESULTS: Two hundred twenty-two cases of syphilitic uveitis were evaluated by machine learning with cases evaluated against other uveitides in the relevant uveitic class. Key criteria for syphilitic uveitis included a compatible uveitic presentation, (1) anterior uveitis, 2) intermediate uveitis, or 3) posterior or panuveitis with retinal, retinal pigment epithelial, or retinal vascular inflammation) and evidence of syphilis infection with a positive treponemal test. The Centers for Disease Control and Prevention reverse screening algorithm for syphilis testing is recommended. The misclassification rates for syphilitic uveitis in the training sets were: anterior uveitides 0%, intermediate uveitides 6.0%, posterior uveitides 0%, panuveitides 0%, and infectious posterior/panuveitides 8.6%. The overall accuracy of the diagnosis of syphilitic uveitis in the validation set was 100% (99% CI 99.5, 100) – i.e. the validation sets misclassification rates were 0% for each uveitic class. CONCLUSIONS: The criteria for syphilitic uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.