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Tyler Brown, Brittany Chung, Yue Shi, Jyotsna Maram, Srinivas Sadda, Olivia L Lee; Assessment of conjunctival hyperemia severity based on color photographs. Invest. Ophthalmol. Vis. Sci. 2020;61(7):4687.
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© ARVO (1962-2015); The Authors (2016-present)
To describe and validate a new scale for conjunctival hyperemia based on standard color photography.
This new scale classifies conjunctival hyperemia into 5 severity levels: None (0), Trace (1), Mild (2), Moderate (3), and Severe (4). These levels are defined photographically, as well as with descriptions of conjunctival vessel density, dilation, color, and tortuosity.Two certified and masked graders retrospectively applied the scale to color images taken from 41 eyes in primary gaze with a single lens reflex camera system (Canfield scientific Inc. Fairfield, NJ), using fixed parameters under consistent lighting conditions. Each image was assigned a hyperemia severity, allowing for either whole or half step (“0.5”) severity scores. Hyperemia was graded for the visible bulbar conjunctiva of the entire eye (global), as well the nasal and temporal quadrants. Unweighted Kappa statistics were computed to evaluate the intra- and inter- grader reproducibility.
All 41 images were analyzable using the described scale. In comparing gradings based on whole step versus half step severity scoring, the reproducibility was higher for whole step severity scoring. Kappa analysis demonstrated a high level of reproducibility for both intra- and inter- grader comparisons. Specifically intra-grader unweighted kappa values were 0.934, 0.867 and 0.933 for global, nasal and temporal quadrants, respectively. Inter-grader unweighted kappa values were 0.837, 0.836 and 0.934 for global, nasal and temporal quadrants, respectively.
A methodology for conjunctival hyperemia is described and validated using color photographs of the eye taken in primary gaze with a simple set focus camera. Given the high level of reproducibility of this method, it may be suitable for use in future clinical research investigations.
This is a 2020 ARVO Annual Meeting abstract.
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