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  • Title: Classification Criteria for Multiple Sclerosis-Associated Intermediate Uveitis.
    Author: Standardization of Uveitis Nomenclature (SUN) Working Group.
    Journal: Am J Ophthalmol; 2021 Aug; 228():72-79. PubMed ID: 33845022.
    Abstract:
    PURPOSE: The purpose of this study was to determine classification criteria for multiple sclerosis-associated intermediate uveitis. DESIGN: Machine learning of cases with multiple sclerosis-associated intermediate uveitis 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 589 cases of intermediate uveitides, including 112 cases of multiple sclerosis-associated intermediate uveitis, 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 multiple sclerosis-associated intermediate uveitis included unilateral or bilateral intermediate uveitis and multiple sclerosis diagnosed by the McDonald criteria. Key exclusions included syphilis and sarcoidosis. The misclassification rates for multiple sclerosis-associated intermediate uveitis were 0 % in the training set and 0% in the validation set. CONCLUSIONS: The criteria for multiple sclerosis-associated intermediate uveitis had a low misclassification rate and appeared to perform sufficiently well enough for use in clinical and translational research.
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