PURPOSE: To determine classification criteria for varicella zoster virus (VZV) anterior uveitis. DESIGN: Machine learning of cases with VZV anterior uveitis and 8 other anterior uveitides. METHODS: Cases of anterior uveitides 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 anterior uveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand eighty-three cases of anterior uveitides, including 123 cases of VZV anterior uveitis, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for VZV anterior uveitis included unilateral anterior uveitis with either (1) positive aqueous humor polymerase chain reaction assay for VZV; (2) sectoral iris atrophy in a patient ≥60 years of age; or (3) concurrent or recent dermatomal herpes zoster. The misclassification rates for VZV anterior uveitis were 0.9% in the training set and 0% in the validation set, respectively. CONCLUSIONS: The criteria for VZV anterior uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.
PURPOSE: To determine classification criteria for varicella zoster virus (VZV) anterior uveitis. DESIGN: Machine learning of cases with VZV anterior uveitis and 8 other anterior uveitides. METHODS: Cases of anterior uveitides 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 anterior uveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand eighty-three cases of anterior uveitides, including 123 cases of VZV anterior uveitis, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for VZV anterior uveitis included unilateral anterior uveitis with either (1) positive aqueous humor polymerase chain reaction assay for VZV; (2) sectoral iris atrophy in a patient ≥60 years of age; or (3) concurrent or recent dermatomal herpes zoster. The misclassification rates for VZV anterior uveitis were 0.9% in the training set and 0% in the validation set, respectively. CONCLUSIONS: The criteria for VZV anterior uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.
Authors: Douglas A Jabs; Andrew Dick; John T Doucette; Amod Gupta; Susan Lightman; Peter McCluskey; Annabelle A Okada; Alan G Palestine; James T Rosenbaum; Sophia M Saleem; Jennifer Thorne; Brett Trusko Journal: Am J Ophthalmol Date: 2017-11-06 Impact factor: 5.258
Authors: Zane Anwar; Anat Galor; Thomas A Albini; Darlene Miller; Victor Perez; Janet L Davis Journal: Am J Ophthalmol Date: 2013-02-12 Impact factor: 5.258
Authors: Neelofar Ghaznawi; Ajoy Virdi; Amir Dayan; Kristin M Hammersmith; Christopher J Rapuano; Peter R Laibson; Elisabeth J Cohen Journal: Ophthalmology Date: 2011-07-23 Impact factor: 12.079
Authors: Rohit Aggarwal; Sarah Ringold; Dinesh Khanna; Tuhina Neogi; Sindhu R Johnson; Amy Miller; Hermine I Brunner; Rikke Ogawa; David Felson; Alexis Ogdie; Daniel Aletaha; Brian M Feldman Journal: Arthritis Care Res (Hoboken) Date: 2015-07 Impact factor: 4.794