Classification Criteria for Syphilitic Uveitis

Douglas A. Jabs, Rubens Belfort Jr, Bahram Bodaghi, Elizabeth Graham, Gary N. Holland, Susan L. Lightman, Neal Oden, Alan G. Palestine, Justine R. Smith, Jennifer E. Thorne, Brett E. Trusko, for the Standardization of Uveitis Nomenclature (SUN) Working Group

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Purpose: To determine classification criteria for syphilitic uveitis. Design: Machine learning of cases with syphilitic uveitis and 24 other uveitides. Methods: Cases of anterior, intermediate, posterior, and 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 analyzed by anatomic class, and each class was 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 different uveitic classes. The resulting criteria were evaluated on the validation set. Results: Two hundred twenty-two cases of syphilitic uveitis were evaluated by machine learning, with cases evaluated against other uveitides in the relevant uveitic class. Key criteria for syphilitic uveitis included a compatible uveitic presentation (anterior uveitis; intermediate uveitis; or posterior or panuveitis with retinal, retinal pigment epithelial, or retinal vascular inflammation) and evidence of syphilis infection with a positive treponemal test. The Centers for Disease Control and Prevention reverse screening algorithm for syphilis testing is recommended. The misclassification rates for syphilitic uveitis in the training sets were as follows: anterior uveitides 0%, intermediate uveitides 6.0%, posterior uveitides 0%, panuveitides 0%, and infectious posterior/panuveitides 8.6%. The overall accuracy of the diagnosis of syphilitic uveitis in the validation set was 100% (99% confidence interval 99.5, 100)—that is, the validation set's misclassification rates were 0% for each uveitic class. Conclusions: The criteria for syphilitic uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.

Original languageEnglish
Pages (from-to)182-191
Number of pages10
JournalAmerican Journal of Ophthalmology
Volume228
DOIs
Publication statusPublished - Aug 2021

Keywords

  • syphilitic uveitis
  • classification criteria
  • panuveitides
  • multinomial logistic regression
  • spirochete Treponema pallidum
  • rapid plasmin regain test

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