PURPOSE: The purpose of this study was to determine classification criteria for sarcoidosis-associated uveitis. DESIGN: Machine learning of cases with sarcoid uveitis and 15 other uveitides. METHODS: Cases of anterior, intermediate, and panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed including 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 in the training sets to determine a parsimonious set of criteria that minimized the misclassification rate among the uveitides. The resulting criteria were evaluated in the validation sets. RESULTS: A total of 1,083 cases of anterior uveitides, 589 cases of intermediate uveitides, and 1,012 cases of panuveitides, including 278 cases of sarcoidosis-associated uveitis, were evaluated by machine learning. Key criteria for sarcoidosis-associated uveitis included a compatible uveitic syndrome of any anatomic class and evidence of sarcoidosis, either 1) tissue biopsy results demonstrating non-caseating granulomata or 2) bilateral hilar adenopathy on chest imaging. The overall accuracy of the diagnosis of sarcoidosis-associated uveitis in the validation set was 99.7% (95% confidence interval: 98.8-99.9). The misclassification rates for sarcoidosis-associated uveitis in the training sets were 3.2% in anterior uveitis, 2.6% in intermediate uveitis, and 1.2% in panuveitis; in the validation sets, the misclassification rates were 0% in anterior uveitis, 0% in intermediate uveitis, and 0% in panuveitis. CONCLUSIONS: The criteria for sarcoidosis-associated uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
PURPOSE: The purpose of this study was to determine classification criteria for sarcoidosis-associated uveitis. DESIGN: Machine learning of cases with sarcoid uveitis and 15 other uveitides. METHODS: Cases of anterior, intermediate, and panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed including 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 in the training sets to determine a parsimonious set of criteria that minimized the misclassification rate among the uveitides. The resulting criteria were evaluated in the validation sets. RESULTS: A total of 1,083 cases of anterior uveitides, 589 cases of intermediate uveitides, and 1,012 cases of panuveitides, including 278 cases of sarcoidosis-associated uveitis, were evaluated by machine learning. Key criteria for sarcoidosis-associated uveitis included a compatible uveitic syndrome of any anatomic class and evidence of sarcoidosis, either 1) tissue biopsy results demonstrating non-caseating granulomata or 2) bilateral hilar adenopathy on chest imaging. The overall accuracy of the diagnosis of sarcoidosis-associated uveitis in the validation set was 99.7% (95% confidence interval: 98.8-99.9). The misclassification rates for sarcoidosis-associated uveitis in the training sets were 3.2% in anterior uveitis, 2.6% in intermediate uveitis, and 1.2% in panuveitis; in the validation sets, the misclassification rates were 0% in anterior uveitis, 0% in intermediate uveitis, and 0% in panuveitis. CONCLUSIONS: The criteria for sarcoidosis-associated uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
Authors: R P Baughman; A S Teirstein; M A Judson; M D Rossman; H Yeager; E A Bresnitz; L DePalo; G Hunninghake; M C Iannuzzi; C J Johns; G McLennan; D R Moller; L S Newman; D L Rabin; C Rose; B Rybicki; S E Weinberger; M L Terrin; G L Knatterud; R Cherniak Journal: Am J Respir Crit Care Med Date: 2001-11-15 Impact factor: 21.405
Authors: Rina La Distia Nora; Mirjam E J van Velthoven; Ninette H Ten Dam-van Loon; Tom Misotten; Marleen Bakker; Martin P van Hagen; Aniki Rothova Journal: Am J Ophthalmol Date: 2013-11-18 Impact factor: 5.258
Authors: Patompong Ungprasert; Andrea A Tooley; Cynthia S Crowson; Eric L Matteson; Wendy M Smith Journal: Ocul Immunol Inflamm Date: 2017-10-12 Impact factor: 3.070