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