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J. Peter Campbell, Michael Ryan, Emily Lore, Susan Ostmo, Karyn Jonas, Robison Vernon Paul Chan, Michael F Chiang; Diagnostic discrepancies in retinopathy of prematurity classification. Invest. Ophthalmol. Vis. Sci. 2016;57(12):6291.
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© ARVO (1962-2015); The Authors (2016-present)
To identify the most common areas for discrepancy in retinopathy of prematurity (ROP) classification between experts and between image-based and ophthalmoscopic classification
325 infants were recruited as part of a multi-center ROP cohort study from 8 participating centers. Each site had participating ophthalmologists who provided the clinical classification after routine ophthalmoscopic examination, and obtained wide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA). Images were independently classified by two study experts using a secure web-based module. Image-based classifications (zone, stage, plus disease, overall disease category) were compared between the two experts, and to the clinical classification by the examining ophthalmologist. Inter-expert image-based agreement and image-based vs. clinical diagnostic agreement were determined using absolute agreement and weighted kappa statistic.
1774 study eye examinations from 325 infants were included in the study. Experts disagreed with each other on the stage classification in 721/1774 (41%) of comparisons, plus disease classification (including pre-plus) in 334/1774 (19%), and zone in 147/1774 (8%). Disagreement in classifying presence of type 1 disease was between 3 and 5% for all comparisons. There were systematic differences between image-based classification clinical (ophthalmoscopic) classification for zone, but not for stage, plus, type 1 or type 2 disease. Among discrepancies in the diagnosis of type 1 disease, the majority of disagreements involved classification discrepancies in both stage and plus disease.
The most common area of disagreement in ROP classification between experts in this study is in diagnosis of stage. However, agreement about presence of type 1 and type 2 disease is high. There were no systematic differences between image-based classification and the clinical exam in detecting type 1 or type 2 disease. These findings are important in identifying potential areas for error during ROP diagnosis and education, and support efforts to incorporate telemedicine approaches using image-based classification either using human graders or automated computer based image analysis.
This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.
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