PURPOSE: To determine classification criteria for pars planitis DESIGN: Machine learning of cases with pars planitis and 4 other intermediate uveitides.
METHODS: Cases of intermediate 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 intermediate uveitides. The resulting criteria were evaluated on the validation set.
RESULTS: Five hundred eighty-nine cases of intermediate uveitides, including 226 cases of pars planitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval 96.1, 99.9). Key criteria for pars planitis included unilateral or bilateral intermediate uveitis with either 1) snowballs in the vitreous or 2) snowbanks on the pars plana. Key exclusions included: 1) multiple sclerosis, 2) sarcoidosis, and 3) syphilis. The misclassification rates for pars planitis were 0 % in the training set and 1.7% in the validation set, respectively.
CONCLUSIONS: The criteria for pars planitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
Bibliographical noteFunding Information:
Funding/Support: The Standardization of Uveitis Nomenclature (SUN) Working Group was supported by National Eye Institute/National Institutes of Health grant R01 EY026593; the David Brown Fund; the Jillian M. and Lawrence A. Neubauer Foundation; and the New York Eye and Ear Foundation.
© 2021 Elsevier Inc.