TY - JOUR
T1 - Classification Criteria for Syphilitic Uveitis
AU - for the Standardization of Uveitis Nomenclature (SUN) Working Group
AU - Jabs, Douglas A.
AU - Belfort, Rubens
AU - Bodaghi, Bahram
AU - Graham, Elizabeth
AU - Holland, Gary N.
AU - Lightman, Susan L.
AU - Oden, Neal
AU - Palestine, Alan G.
AU - Smith, Justine R.
AU - Thorne, Jennifer E.
AU - Trusko, Brett E.
N1 - Funding Information:
Funding/Support: Supported by grant R01 EY026593 from the National Eye Institute, the 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. Financial Disclosures: Douglas A. Jabs: none; Rubens Belfort: none; Bahram Bodaghi: none; Elizabeth Graham: none; Gary N. Holland: none; Susan L. Lightman: none; Neal Oden: none; Alan G. Palestine: none; Justine R. Smith: none; Jennifer E. Thorne: Dr Thorne engaged in part of this research as a consultant and was compensated for the consulting service; Brett E. Trusko: none. All authors attest that they meet the current ICMJE criteria for authorship.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
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U2 - 10.1016/j.ajo.2021.03.039
DO - 10.1016/j.ajo.2021.03.039
M3 - Article
C2 - 33845020
AN - SCOPUS:85109106801
SN - 0002-9394
VL - 228
SP - 182
EP - 191
JO - American journal of ophthalmology
JF - American journal of ophthalmology
ER -