PURPOSE: To determine classification criteria for intermediate uveitis, non-pars planitis type (IU-NPP, also known as undifferentiated intermediate uveitis). DESIGN: Machine learning of cases with IU-NPP 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 of cases of intermediate uveitides, including 114 cases of IU-NPP, 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 IU-NPP included unilateral or bilateral intermediate uveitis with neither snowballs in the vitreous humor nor snowbanks on the pars plana. Other key exclusions included multiple sclerosis, sarcoidosis, and syphilis. The misclassification rates for IU-NPP were 0% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for IU-NPP had a low misclassification rate and seemed to perform well enough for use in clinical and translational research.
PURPOSE: To determine classification criteria for intermediate uveitis, non-pars planitis type (IU-NPP, also known as undifferentiated intermediate uveitis). DESIGN: Machine learning of cases with IU-NPP 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 of cases of intermediate uveitides, including 114 cases of IU-NPP, 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 IU-NPP included unilateral or bilateral intermediate uveitis with neither snowballs in the vitreous humor nor snowbanks on the pars plana. Other key exclusions included multiple sclerosis, sarcoidosis, and syphilis. The misclassification rates for IU-NPP were 0% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for IU-NPP had a low misclassification rate and seemed to perform well enough for use in clinical and translational research.
Authors: Mandeep S Sagoo; Hemal Mehta; Andrew J Swampillai; Victoria M L Cohen; Sepideh Z Amin; P Nicholas Plowman; Sue Lightman Journal: Surv Ophthalmol Date: 2013-12-31 Impact factor: 6.048
Authors: Hawkar Kazi; Jolanda D F de Groot-Mijnes; Ninette H Ten Dam-van Loon; Jeannette Ossewaarde-van Norel; Jan Jelrik Oosterheert; Joke H de Boer Journal: Am J Ophthalmol Date: 2016-04-11 Impact factor: 5.258