Elsevier

Ophthalmology

Volume 127, Issue 8, August 2020, Pages 1105-1112
Ophthalmology

Original Article
Aggressive Posterior Retinopathy of Prematurity: Clinical and Quantitative Imaging Features in a Large North American Cohort

Presented at: Association for Research in Vision and Ophthalmology Annual Meeting, May 2019, Vancouver, British Columbia, Canada.
https://doi.org/10.1016/j.ophtha.2020.01.052Get rights and content

Purpose

Aggressive posterior retinopathy of prematurity (AP-ROP) is a vision-threatening disease with a significant rate of progression to retinal detachment. The purpose of this study was to characterize AP-ROP quantitatively by demographics, rate of disease progression, and a deep learning-based vascular severity score.

Design

Retrospective analysis.

Participants

The Imaging and Informatics in ROP cohort from 8 North American centers, consisting of 947 patients and 5945 clinical eye examinations with fundus images, was used. Pretreatment eyes were categorized by disease severity: none, mild, type 2 or pre-plus, treatment-requiring (TR) without AP-ROP, TR with AP-ROP. Analyses compared TR with AP-ROP and TR without AP-ROP to investigate differences between AP-ROP and other TR disease.

Methods

A reference standard diagnosis was generated for each eye examination using previously published methods combining 3 independent image-based gradings and 1 ophthalmoscopic grading. All fundus images were analyzed using a previously published deep learning system and were assigned a score from 1 through 9.

Main Outcome Measures

Birth weight, gestational age, postmenstrual age, and vascular severity score.

Results

Infants who demonstrated AP-ROP were more premature by birth weight (617 g vs. 679 g; P = 0.01) and gestational age (24.3 weeks vs. 25.0 weeks; P < 0.01) and reached peak severity at an earlier postmenstrual age (34.7 weeks vs. 36.9 weeks; P < 0.001) compared with infants with TR without AP-ROP. The mean vascular severity score was greatest in TR with AP-ROP infants compared with TR without AP-ROP infants (8.79 vs. 7.19; P < 0.001). Analyzing the severity score over time, the rate of progression was fastest in infants with AP-ROP (P < 0.002 at 30–32 weeks).

Conclusions

Premature infants in North America with AP-ROP are born younger and demonstrate disease earlier than infants with less severe ROP. Disease severity is quantifiable with a deep learning-based score, which correlates with clinically identified categories of disease, including AP-ROP. The rate of progression to peak disease is greatest in eyes that demonstrate AP-ROP compared with other treatment-requiring eyes. Analysis of quantitative characteristics of AP-ROP may help improve diagnosis and treatment of an aggressive, vision-threatening form of ROP.

Section snippets

Methods

This project was conducted as part of the multicenter Imaging and Informatics in ROP consortium. This study was approved by the institutional review board at the coordinating center (Oregon Health & Science University), and by each of the 8 participating institutions (Columbia University, Cornell University, University of Illinois at Chicago, William Beaumont Hospital, Children’s Hospital Los Angeles, Cedars-Sinai Medical Center, University of Miami, and Asociación para Evitar la Ceguera en

Clinical and Demographic Features of Study Cohort

Table 1 displays demographic features of the study cohort based on category of peak disease severity and associated comorbidities. A total of 5945 eye examinations from 947 infants (1889 eyes) were analyzed. Sixty-two of 1889 eyes (3%) demonstrated a peak disease severity of TR with AP ROP. Infants in the TR with AP ROP category had a lower mean ± standard deviation gestational age (24.3±0.9 weeks; P < 0.01) and birth weight (617±119 g; P < 0.01) than all other disease categories and were

Discussion

In this study, we analyzed a cohort of premature infants in North America to determine the demographics, clinical comorbidities, quantitative vascular severity, and interexpert diagnostic agreement for patients with AP ROP. Several key findings were determined: (1) premature infants in North America with AP ROP are born younger, demonstrate disease earlier, and have more chronic lung disease than infants with other categories of ROP; (2) quantitative evaluation of vascular severity using a deep

Conclusions

Visual outcomes of untreated AP ROP are poor, which make accurate and timely diagnosis critical. The use of quantitative disease metrics, such as the Imaging in Informatics in ROP deep learning vascular severity scale, may represent a way to improve diagnostic agreement and enable earlier recognition of AP ROP in the future.32 In this North American population, we found that the disease tends to occur only in the most premature babies, who have multiple comorbidities, and tends to present

Acknowledgments

Members of the i-ROP research consortium: Oregon Health & Science University (Portland, OR): Michael F. Chiang, MD, Susan Ostmo, MS, Sang Jin Kim, MD, PhD, Kemal Sonmez, PhD, J. Peter Campbell, MD, MPH. University of Illinois at Chicago (Chicago, IL): RV Paul Chan, MD, Karyn Jonas, RN. Columbia University (New York, NY): Jason Horowitz, MD, Osode Coki, RN, Cheryl-Ann Eccles, RN, Leora Sarna, RN. Weill Cornell Medical College (New York, NY): Anton Orlin, MD. Bascom Palmer Eye Institute (Miami,

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  • Cited by (0)

    Financial Disclosure(s): The author(s) have made the following disclosure(s): J.B.: Patent – preliminary application submitted on the Imaging and Informatics in retinopathy of prematurity deep learning system.

    S.J.K.: Consultant – Novartis, Curacle, Hanmi Pharmaceutical, Reyon Pharmaceutical Co., Ltd.

    A.C.: Patent – preliminary application submitted on the Imaging and Informatics in retinopathy of prematurity deep learning system.

    S.O.: Patent – preliminary application submitted on the Imaging and Informatics in retinopathy of prematurity deep learning system.

    R.V.P.C.: Consultant – Novartis, Alcon; Scientific advisory board – Phoenix Technology Group; Financial support – Regeneron, Genentech; Patent – preliminary application submitted on the Imaging and Informatics in retinopathy of prematurity deep learning system.

    J.K.-C.: Financial support – Genentech; Patent – preliminary application submitted on the Imaging and Informatics in retinopathy of prematurity deep learning system.

    M.F.C.: Scientific advisory board – Clarity Medical Systems; Consultant – Novartis; Equity Owner – Inteleretina; Financial support – Genentech; Patent – preliminary application submitted on the Imaging and Informatics in retinopathy of prematurity deep learning system.

    J.P.C.: Financial support – Genentech; Patent – preliminary application submitted on the Imaging and Informatics in retinopathy of prematurity deep learning system.

    Supported by the National Institutes of Health, Bethesda, Maryland (grant nos.: R01EY19474, K12EY027720, T15LM007088, and P30EY10572); the National Science Foundation, Arlington, Virginia (grant nos.: 1622542 and SCH-1622679); and Research to Prevent Blindness, Inc., New York, New York (unrestricted departmental funding and Career Development Award [J.P.C.]). None of the funding agencies had any role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

    HUMAN SUBJECTS: Human subjects were included in this study. The human ethics committees at Oregon Health & Science University, Columbia University, Cornell University, University of Illinois at Chicago, William Beaumont Hospital, Children’s Hospital Los Angeles, Cedars-Sinai Medical Center, University of Miami, and Asociación para Evitar la Ceguera en México approved the study. All research adhered to the tenets of the Declaration of Helsinki. All parents of all infants enrolled provided informed consent.

    No animal subjects were included in this study.

    Author Contributions:

    Conception and design: Brown, Ostmo, Chan

    Analysis and interpretation: Bellsmith, Brown, Kim, Goldstein, Coyner, Ostmo, Gupta, Chan, Kalpathy-Cramer, Chiang, Campbell

    Data collection: Brown, Ostmo, Chan

    Obtained funding: Brown, Kim, Coyner, Ostmo, Chan, Kalpathy-Cramer, Chiang, Campbell

    Overall responsibility: Bellsmith, Brown, Kim, Goldstein, Coyner, Ostmo, Gupta, Chan, Kalpathy-Cramer, Chiang, Campbell, on behalf of the Imaging and Informatics in ROP (i-ROP) research Consortium

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