Classification Criteria for Behçet Disease Uveitis.

Academic Article


  • 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 cases of 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 seemed to perform sufficiently well for use in clinical and translational research.
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  • Adult, Behcet Syndrome, Consensus, Female, Humans, Machine Learning, Male, Retrospective Studies, Uveitis, Young Adult
  • Digital Object Identifier (doi)

    Author List

  • Standardization of Uveitis Nomenclature (SUN) Working Group
  • Start Page

  • 80
  • End Page

  • 88
  • Volume

  • 228