Classification Criteria for Sympathetic Ophthalmia.

Academic Article

Abstract

  • PURPOSE: The purpose of this study was to determine classification criteria for sympathetic ophthalmia. DESIGN: Machine learning of cases with sympathetic ophthalmia 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the panuveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 1,012 cases of panuveitides, including 110 cases of sympathetic ophthalmia, 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 sympathetic ophthalmia included bilateral uveitis with 1) a history of unilateral ocular trauma or surgery and 2) an anterior chamber and vitreous inflammation or a panuveitis with choroidal involvement. The misclassification rates for sympathetic ophthalmia were 4.2% in the training set and 6.7% in the validation set. CONCLUSIONS: The criteria for sympathetic ophthalmia had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
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    Published In

    Keywords

  • Adult, Anterior Chamber, Female, Humans, Male, Middle Aged, Ophthalmia, Sympathetic, Tomography, Optical Coherence
  • Digital Object Identifier (doi)

    Pubmed Id

  • 1875136
  • Author List

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

  • 212
  • End Page

  • 219
  • Volume

  • 228