Gradient Boosted Decision Tree Classification of Endophthalmitis Versus Uveitis and Lymphoma from Aqueous and Vitreous IL-6 and IL-10 Levels.
To investigate the effectiveness of gradient boosting to classify endophthalmitis versus uveitis and lymphoma by intraocular cytokine levels.



Patient diagnoses and aqueous and vitreous levels of interleukin (IL)-6 and IL-10 were retrospectively extracted from a National Eye Institute Histopathology Core database and compared by Kruskal-Wallis and post hoc Dunn tests. A gradient-boosted decision tree classifier was trained to differentiate endophthalmitis versus uveitis and lymphoma from vitreous IL-6 and IL-10, vitreous IL-6 only, and aqueous IL-6 only data sets; and was tested with 80-20 train-test split and 3-fold cross-validation of the training set.



Seven endophthalmitis, 29 lymphoma, and 49 uveitis patients were included. IL-6 was higher in endophthalmitis than uveitis (P = 0.0713 aqueous, 0.0014 vitreous) and lymphoma (P = 0.0032 aqueous, 0.0001 vitreous). IL-10 was significantly higher in lymphoma than uveitis (P = 0.0017 aqueous, 0.0014 vitreous). Three-fold cross validation demonstrated 95% ± 5%, 95% ± 4%, and 97% ± 5% predictive accuracy for vitreous IL-6 and IL-10, vitreous IL-6 only, and aqueous IL-6 only data sets. Upon validation with the testing set, vitreous IL-6 and IL-10 and aqueous IL-6 only data sets achieved 100% predictive accuracy and vitreous IL-6 only data achieved 93% predictive accuracy with 100% sensitivity, 92% specificity, and an area under the receiver operating characteristic curve (ROC/AUC) of 96%.



With limited sample size, gradient boosting can differentiate endophthalmitis from uveitis and lymphoma by IL-6 and IL-10 with high sensitivity and specificity; however, a larger cohort is needed for further validation.
1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, Maryland.|2 University of California , San Diego, School of Medicine, San Diego, California.
David E
DE
Kuo
1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, Maryland.|3 Georgetown University , School of Medicine, Washington, District of Columbia.
Maggie M
MM
Wei
1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, Maryland.
Karen R
KR
Armbrust
1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, Maryland.
Jared E
JE
Knickelbein
1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, Maryland.|4 Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom .
Ian Y L
IYL
Yeung
1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, Maryland.
Robert B
RB
Nussenblatt
1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, Maryland.
Chi-Chao
CC
Chan
1 Laboratory of Immunology, National Eye Institute, National Institutes of Health , Bethesda, Maryland.
Hatice Nida
HN
Sen
D003627:Data Interpretation, Statistical; D003663:Decision Trees; D009877:Endophthalmitis / Q000175:diagnosis*; D006801:Humans; D016753:Interleukin-10 / Q000032:analysis*; D015850:Interleukin-6 / Q000032:analysis*; D008223:Lymphoma / Q000175:diagnosis*; D000069550:Machine Learning; D012372:ROC Curve; D014605:Uveitis / Q000175:diagnosis*; D014867:Water / Q000737:chemistry
C508609:IL10 protein, human; C508600:IL6 protein, human; D015850:Interleukin-6; D014867:Water; D016753:Interleukin-10
cytokines; immunology; inflammation; interleukins; uveitis