September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Diagnostic discrepancies in retinopathy of prematurity classification
Author Affiliations & Notes
  • J. Peter Campbell
    Oregon Health & Science University, Portland, Oregon, United States
  • Michael Ryan
    Oregon Health & Science University, Portland, Oregon, United States
  • Emily Lore
    Oregon Health & Science University, Portland, Oregon, United States
  • Susan Ostmo
    Oregon Health & Science University, Portland, Oregon, United States
  • Karyn Jonas
    University of Illinois Chicago, Chicago, Illinois, United States
  • Robison Vernon Paul Chan
    University of Illinois Chicago, Chicago, Illinois, United States
  • Michael F Chiang
    Oregon Health & Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   J. Peter Campbell, None; Michael Ryan, None; Emily Lore, None; Susan Ostmo, None; Karyn Jonas, None; Robison Chan, None; Michael Chiang, Clarity Medical Systems (S)
  • Footnotes
    Support  Supported by grants R01 EY19474 and P30 EY010572 from the National Institutes of Health, Bethesda, MD (MFC), unrestricted departmental funding from Research to Prevent Blindness, New York, NY (JPC, MCR, SO, KJ, RVPC, MFC), the St. Giles Foundation (RVPC), and the iNsight Foundation (RVPC, KEJ).
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 6291. doi:
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    • Get Citation

      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)

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Abstract

Purpose : To identify the most common areas for discrepancy in retinopathy of prematurity (ROP) classification between experts and between image-based and ophthalmoscopic classification

Methods : 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.

Results : 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.

Conclusions : 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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