4.6 Article

Classification Criteria for Vogt-Koyanagi-Harada Disease

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AMERICAN JOURNAL OF OPHTHALMOLOGY
卷 228, 期 -, 页码 205-211

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ELSEVIER SCIENCE INC
DOI: 10.1016/j.ajo.2021.03.036

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  1. National Eye Institute, the National Institutes of Health, Bethesda, Maryland, USA [R01 EY026593]
  2. David Brown Fund, New York, New York, USA
  3. Jillian M. and Lawrence A. Neubauer Foundation, New York, New York, USA
  4. New York Eye and Ear Foundation, New York, New York, USA

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The study established classification criteria for VKH disease through machine learning, achieving high accuracy rates in the training and validation sets, with reasonably low misclassification rates for both early and late-stage VKH.
PURPOSE: To determine classification criteria for Vogt-Koyanagi-Harada (VKH) disease. DESIGN: Machine learning of cases with VKH disease and 5 other panuveitides. METHODS: Cases of 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 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 panuveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand twelve cases of panuveitides, including 156 cases of early-stage VKH and 103 cases of late-stage VKH, were evaluated. Overall accuracy for panuveitides was 96.3% in the training set and 94.0% in the validation set (95% confidence interval 89.0, 96.8). Key criteria for early-stage VKH included the following: (1) exudative retinal detachment with characteristic appearance on fluorescein angiogram or optical coherence tomography or (2) panuveitis with >= 2 of 5 neurologic symptoms/signs. Key criteria for late-stage VKH included history of early-stage VKH and either (1) sunset glow fundus or (2) uveitis and >= 1 of 3 cutaneous signs. The misclassification rates in the learning and validation sets for early-stage VKH were 8.0% and 7.7%, respectively, and for late-stage VKH 1.0% and 12%, respectively. CONCLUSIONS: The criteria for VKH had a reasonably low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research. ((C) 2021 Elsevier Inc. All rights reserved.)

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