Abstract
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 beta-2 microglobulin. The mis-classification rates for TINU were 1.2% in the training set and 0% in the validation set.
CONCLUSIONS: The criteria for TINU had a low mis-classification rate and seemed to perform well enough for use in clinical and translational research. (C) 2021 Elsevier Inc. All rights reserved.
