Diagnostic Discrepancies in Retinopathy of Prematurity Classification

Purpose To identify the most common areas for discrepancy in retinopathy of prematurity (ROP) classification between experts. Design Prospective cohort study. Participants A total of 281 infants were identified as part of a multicenter, prospective, ROP cohort study from 7 participating centers. Each site had participating ophthalmologists who provided the clinical classification after routine examination using binocular indirect ophthalmoscopy (BIO) and obtained wide-angle retinal images, which were independently classified by 2 study experts. Methods Wide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA) were obtained from study subjects, and 2 experts evaluated each image using a secure web-based module. Image-based classifications for zone, stage, plus disease, and overall disease category (no ROP, mild ROP, type II or pre-plus, and type I) were compared between the 2 experts and with the clinical classification obtained by BIO. Main Outcome Measures Inter-expert image-based agreement and image-based versus ophthalmoscopic diagnostic agreement using absolute agreement and weighted kappa statistic. Results A total of 1553 study eye examinations from 281 infants were included in the study. Experts disagreed on the stage classification in 620 of 1553 comparisons (40%), plus disease classification (including pre-plus) in 287 of 1553 comparisons (18%), zone in 117 of 1553 comparisons (8%), and overall ROP category in 618 of 1553 comparisons (40%). However, agreement for presence versus absence of type 1 disease was >95%. There were no differences between image-based and clinical classification except for zone III disease. Conclusions The most common area of discrepancy in ROP classification is stage, although inter-expert agreement for clinically significant disease, such as presence versus absence of type 1 and type 2 disease, is high. There were no differences between image-based grading and clinical examination in the ability to detect clinically significant disease. This study provides additional evidence that image-based classification of ROP reliably detects clinically significant levels of ROP with high accuracy compared with the clinical examination.