PURPOSE: The purpose of this study was to determine classification criteria for spondyloarthritis/HLA-B27-associated anterior uveitis DESIGN: Machine learning of cases with spondyloarthritis/HLA-B27-associated 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 1,083 cases of anterior uveitides, including 184 cases of spondyloarthritis/HLA-B27-associated 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% CI: 92.4-98.6). Key criteria for spondyloarthritis/HLA-B27-associated anterior uveitis included 1) acute or recurrent acute unilateral or unilateral alternating anterior uveitis with either spondyloarthritis or a positive test result for HLA-B27; or 2) chronic anterior uveitis with a history of the classic course and either spondyloarthritis or HLA-B27; or 3) anterior uveitis with both spondyloarthritis and HLA-B27. The misclassification rates for spondyloarthritis/HLA-B27-associated anterior uveitis were 0% in the training set and 3.6% in the validation set. CONCLUSIONS: The criteria for spondyloarthritis/HLA-B27-associated anterior uveitis had a low misclassification rate and appeared to perform well enough for use in clinical and translational research.
PURPOSE: The purpose of this study was to determine classification criteria for spondyloarthritis/HLA-B27-associated anterior uveitis DESIGN: Machine learning of cases with spondyloarthritis/HLA-B27-associated 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 in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 1,083 cases of anterior uveitides, including 184 cases of spondyloarthritis/HLA-B27-associated 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% CI: 92.4-98.6). Key criteria for spondyloarthritis/HLA-B27-associated anterior uveitis included 1) acute or recurrent acute unilateral or unilateral alternating anterior uveitis with either spondyloarthritis or a positive test result for HLA-B27; or 2) chronic anterior uveitis with a history of the classic course and either spondyloarthritis or HLA-B27; or 3) anterior uveitis with both spondyloarthritis and HLA-B27. The misclassification rates for spondyloarthritis/HLA-B27-associated anterior uveitis were 0% in the training set and 3.6% in the validation set. CONCLUSIONS: The criteria for spondyloarthritis/HLA-B27-associated anterior uveitis had a low misclassification rate and appeared to perform well enough 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: M Rudwaleit; D van der Heijde; R Landewé; N Akkoc; J Brandt; C T Chou; M Dougados; F Huang; J Gu; Y Kirazli; F Van den Bosch; I Olivieri; E Roussou; S Scarpato; I J Sørensen; R Valle-Oñate; U Weber; J Wei; J Sieper Journal: Ann Rheum Dis Date: 2010-11-24 Impact factor: 19.103
Authors: Ali A Zaidi; Gui-Shuang Ying; Ebenezer Daniel; Sapna Gangaputra; James T Rosenbaum; Eric B Suhler; Jennifer E Thorne; C Stephen Foster; Douglas A Jabs; Grace A Levy-Clarke; Robert B Nussenblatt; John H Kempen Journal: Ophthalmology Date: 2009-12-14 Impact factor: 12.079
Authors: M Rudwaleit; R Landewé; D van der Heijde; J Listing; J Brandt; J Braun; R Burgos-Vargas; E Collantes-Estevez; J Davis; B Dijkmans; M Dougados; P Emery; I E van der Horst-Bruinsma; R Inman; M A Khan; M Leirisalo-Repo; S van der Linden; W P Maksymowych; H Mielants; I Olivieri; R Sturrock; K de Vlam; J Sieper Journal: Ann Rheum Dis Date: 2009-03-17 Impact factor: 19.103
Authors: M Rudwaleit; D van der Heijde; R Landewé; J Listing; N Akkoc; J Brandt; J Braun; C T Chou; E Collantes-Estevez; M Dougados; F Huang; J Gu; M A Khan; Y Kirazli; W P Maksymowych; H Mielants; I J Sørensen; S Ozgocmen; E Roussou; R Valle-Oñate; U Weber; J Wei; J Sieper Journal: Ann Rheum Dis Date: 2009-03-17 Impact factor: 19.103