Classification criteria for Behçet Disease Uveitis

Standardization of Uveitis Nomenclature (SUN) Working Group

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)


PURPOSE: To determine classification criteria for Behçet disease uveitis.

DESIGN: Machine learning of cases with Behçet 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 intermediate uveitides. The resulting criteria were evaluated on the validation set.

RESULTS: One thousand twelve of cases panuveitides, including 194 cases of Behçet disease with uveitis, were evaluated by machine learning. The 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 Behçet disease uveitis were a diagnosis of Behçet disease using the International Study Group for Behçet Disease criteria and a compatible uveitis, including: 1) anterior uveitis; 2) anterior chamber and vitreous inflammation; 3) posterior uveitis with retinal vasculitis and/or focal infiltrates; or 4) panuveitis with retinal vasculitis and/or focal infiltrates. The misclassification rates for Behçet disease uveitis were 0.6 % in the training set and 0% in the validation set, respectively.

CONCLUSIONS: The criteria for Behçet disease uveitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Original languageEnglish
Pages (from-to)80-88
Number of pages9
JournalAmerican Journal of Ophthalmology
Early online date11 May 2021
Publication statusPublished - Aug 2021

Bibliographical note

Funding Information:
Funding/Support: Supported by grant R01 EY026593 from the National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA; the David Brown Fund, New York, New York, USA; the Jillian M. and Lawrence A. Neubauer Foundation, New York, New York, USA; and the New York Eye and Ear Foundation, New York, New York, USA.

Publisher Copyright:
© 2021 Elsevier Inc.


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