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Mengfei Wu, Mengling Liu, Joel S Schuman, Hiroshi Ishikawa, Gadi Wollstein; Can Clock Hour OCT Retinal Nerve Fiber Layer (RNFL) Thickness Measurements Outperform Global Mean RNFL for Glaucoma Diagnosis?. Invest. Ophthalmol. Vis. Sci. 2020;61(7):5195.
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© ARVO (1962-2015); The Authors (2016-present)
To compare the discrimination accuracy for glaucoma diagnosis using the OCT RNFL clock hours compared with average RNFL.
In a large, ongoing, longitudinal cohort of healthy subjects and subjects with glaucoma, all subjects underwent visual field (VF) and OCT testing. Principal component (PC) analysis was used to reduce the dimensionality of clock hour measurements while maintaining maximum information variability for diagnostic performance. The first four PCs with linear regression were used as predictors of VF mean deviation (MD) and to classify glaucoma diagnosis. The prediction accuracy and discrimination power using cross validation were compared to the models using only average RNFL as a predictor. All models were adjusted for age, signal strength, and intra-subject correlation.
1317 healthy and glaucomatous eyes (717 subjects) were included in the study. A PC analysis was built on the 9 clock hours while excluding non-informative sectors (clock hours 3, 4, and 9). The first PC explained 51% of the total variance, and the first four PCs explained 82% of the total variance and thus were used for subsequent regression models. A PC regression for glaucoma discrimination showed that clock hours 1, 5, 6, 7, 10, 11, 12 were significantly association with diagnosis. The PC showed better glaucoma diagnosis performance compared to average RNFL, with 10-fold cross-validation AUCs of 0.898 and 0.877, respectively (p<0.001). The PC regression for MD improved the model fit measured by R2 by 9% compared to a regression using average RNFL. PC showed that clock hours 2, 5, 6, 7, 10, 11, 12 were significantly associated with MD.
Using PCs with RNFL clock hours improved classification performance for glaucoma diagnosis and model fit for MD, compared to using average RNFL. This method improves discrimination performance by both considering all sectoral RNFL information and removing locations with low diagnostic yield.
This is a 2020 ARVO Annual Meeting abstract.
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