Plus Disease in Retinopathy of Prematurity

Rao, Rohini; Jonsson, Nina J.; Ventura, Camila V.; Gelman, Rony; Lindquist, Martin A.; Casper, Daniel S.; Chiang, Michael F.

doi:10.1097/iae.0b013e31823ac3c3

Cited by 29 publications

(36 citation statements)

References 23 publications

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“…field of view, attention to non-ICROP features such as venous tortuosity, etc.) 10–12,15 may relate to the apparently different cut points between experts. Computer-based image analysis systems may be able to help us better understand the relationships of all of these factors to expert behavior.…”

Section: Discussionmentioning

confidence: 99%

“…6–9 Several possible explanations exist for this inter-expert variability. Available evidence suggests that some clinicians may focus on wider fields of view than shown in the standard photograph, 10–12 that clinicians may focus on different vascular features (such as venous tortuosity) 13,14 which were not included in the standard definition of plus disease, that experts may be unable to readily identify which vascular features are most important to them in diagnosis, 15 and that experts may have different “cut points” for vascular abnormality required for diagnosis of plus disease. Overall, the underlying basis for variability in plus disease diagnosis is not well understood.…”

Section: Introductionmentioning

confidence: 99%

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Plus Disease in Retinopathy of Prematurity

et al. 2016

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Objective To identify patterns of inter-expert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). Design We developed two datasets of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP study, and determined a consensus reference standard diagnosis (RSD) for each image, based on 3 independent image graders and the clinical exam. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. Subjects, Participants, and/or Controls Images obtained during routine ROP screening in neonatal intensive care units. 8 participating experts with >10 years of clinical ROP experience and >5 peer-reviewed ROP publications. Methods, Intervention, or Testing Expert classification of images of plus disease in ROP. Main Outcome Measures Inter-expert agreement (weighted kappa statistic), and agreement and bias on ordinal classification between experts (ANOVA) and the RSD (percent agreement). Results There was variable inter-expert agreement on diagnostic classifications between the 8 experts and the RSD (weighted kappa 0 – 0.75, mean 0.30). RSD agreement ranged from 80 – 94% agreement for the dataset of 100 images, and 29 – 79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and pre-plus disease. The two-way ANOVA model suggested a highly significant effect of both image and user on the average score (P<0.05, adjusted R2=0.82 for dataset A, and P< 0.05 and adjusted R2 =0.6615 for dataset B). Conclusions and Relevance There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different “cut-points” for the amounts of vascular abnormality required for presence of plus and pre-plus disease. This has important implications for research, teaching and patient care for ROP, and suggests that a continuous ROP plus disease severity score may more accurately reflect the behavior of expert ROP clinicians, and may better standardize classification in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Plus Disease in Retinopathy of Prematurity

et al. 2016

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“…Computer-assisted systems have previously been developed for quantitative analysis of the retinal vessel course in other retinal disorders, primarily in retinopathy of prematurity [9,10,11]. In a previous study, we used dedicated software to assess retinal vessel tortuosity in Fabry disease [12].…”

Section: Introductionmentioning

confidence: 99%

Computer-Assisted Evaluation of Retinal Vessel Diameter in Retinitis Pigmentosa

et al. 2016

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Purpose: The evaluation of retinal vessel attenuation is very subjective and not sufficiently reliable in patients with retinitis pigmentosa (RP). We tested semiautomatic software capable of obtaining real-time measurements of vessel diameter. Methods: Retinal vessel diameter was calculated in 25 RP subjects and in 20 healthy controls. The Mann-Whitney test was used to compare the average values of RP patients with those of controls and subgroups of RP patients with different clinical features. Results: The retinal vessel diameter was significantly smaller in RP patients than in controls (p < 0.001). In particular, vessel diameters were smaller in older subjects, in patients with worse ERG responses, and in patients with more severe visual field loss. Conclusions: Computer-assisted analysis of retinal fundus pictures may be helpful in the diagnosis of RP and in monitoring disease progression.

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“…It is well established that inter-expert agreement in ROP classification is imperfect. However, most previous work has focused on plus disease 8,9,11,12,16 and zone classification. 7,19,20 In this study, the majority of discrepancies in stage classification were at the very mild ends of the disease spectrum (stage 0 vs. 1).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Discrepancies in Retinopathy of Prematurity Classification

et al. 2016

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Objective To identify the most common areas for discrepancy in retinopathy of prematurity (ROP) classification between experts. Design Prospective cohort study. Subjects, Participants, and/or Controls 281 infants were identified as part of a multi-center, 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 two study experts. Methods Wide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA) were obtained from study subjects, and two experts evaluated each image using a secure web-based module. Image-based classifications for zone, stage, plus disease, overall disease category (no ROP, mild ROP, Type II or pre-plus, and Type I) were compared between the two experts, and to the clinical classification obtained by BIO. Main Outcome Measures Inter-expert image-based agreement and image-based vs. ophthalmoscopic diagnostic agreement using absolute agreement and weighted kappa statistic. Results 1553 study eye examinations from 281 infants were included in the study. Experts disagreed on the stage classification in 620/1553 (40%) of comparisons, plus disease classification (including pre-plus) in 287/1553 (18%), zone in 117/1553 (8%), and overall ROP category in 618/1553 (40%). However, agreement for presence vs. 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 vs. absence of type 1 and type 2 disease is high. There were no differences between image-based grading and the clinical exam 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 to the clinical exam.

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Plus Disease in Retinopathy of Prematurity

Cited by 29 publications

References 23 publications

Plus Disease in Retinopathy of Prematurity

Plus Disease in Retinopathy of Prematurity

Computer-Assisted Evaluation of Retinal Vessel Diameter in Retinitis Pigmentosa

Diagnostic Discrepancies in Retinopathy of Prematurity Classification

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