PURPOSE: To determine classification criteria for tubulointerstitial nephritis with uveitis (TINU). DESIGN: Machine learning of cases with TINU 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 94 cases of TINU, 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 TINU included anterior chamber inflammation and evidence of tubulointerstitial nephritis with either (1) a positive renal biopsy or (2) evidence of nephritis (elevated serum creatinine and/or abnormal urine analysis) and an elevated urine β-2 microglobulin. The misclassification rates for TINU were 1.2% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for TINU had a low misclassification rate and seemed to perform well enough for use in clinical and translational research.
PURPOSE: To determine classification criteria for tubulointerstitial nephritis with uveitis (TINU). DESIGN: Machine learning of cases with TINU 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 94 cases of TINU, 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 TINU included anterior chamber inflammation and evidence of tubulointerstitial nephritis with either (1) a positive renal biopsy or (2) evidence of nephritis (elevated serum creatinine and/or abnormal urine analysis) and an elevated urine β-2 microglobulin. The misclassification rates for TINU were 1.2% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for TINU had a low misclassification rate and seemed to perform well enough for use in clinical and translational research.
Authors: F Mackensen; F David; V Schwenger; L K Smith; R Rajalingam; R D Levinson; C R Austin; D Houghton; T M Martin; J T Rosenbaum Journal: Br J Ophthalmol Date: 2010-11-07 Impact factor: 4.638
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