The long-term goal of this project is to identify clinical and genetic features of retinopathy of prematurity (ROP) development, and to analyze their relationships. Although biomedical research data are being generated at an enormous pace, much less work has been done to integrate disparate scientific findings across the spectrum from genomics to imaging to clinical medicine. Our overall hypotheses are that genetic factors are involved in the initiation and modulation of ROP, and that analysis of relationships among clinical, imaging, and genetic findings in ROP using bioinformatics approaches will improve understanding of disease pathogenesis and diagnosis. These hypotheses will be tested using three Specific Aims: (1) Recruit, phenotype, and collect genetic material from ~600 additional premature infants at-risk for ROP from 8 centers. This renewal will continue work from the original project, to obtain a total cohort size of >1600 infants for genetic analysis. Demographic and clinical features from serial ophthalmic examinations will be ascertained fully, retinal images will be captured, rigorous reference standards will be established, and blood samples will be collected. (2) Perform imaging and informatics analysis of this cohort. This will: (a) Create quantitative indices for computer- based diagnosis of severe ROP. Features from automated image analysis that best correlate with severe ROP and plus disease will be identified, and quantitative indices will be defined and validated for clinical use. (b) Combine this into disease prediction models incorporating the effects of quantitative image traits, clinical features, and environmental risk factors on ROP susceptibility. (3) Perform genetic and bioinformatics analysis of this cohort. This will: (a) analyze exome sequence data (previously obtained) from 100 phenotypically extreme subjects that were also preferentially enriched as being dizygotic twin pairs, to identify rare variants; (b) perform and analyze genome-wide genotyping to test against clinical and imaging findings, testing specific candidate genes related to vascular pathology in the eye and the vasculature at large, followed by performing a pathway-based analysis; and (c) perform bioinformatics analysis to combine findings from (3a) and (3b) in relation to the clinical findings. Ultimately, these studies should improve understanding of neovascularization in ROP and related ocular diseases, and of normal vascular development in infants. In addition, this work should demonstrate a prototype for health information management which combines genotypic and phenotypic data. This project will be performed by a multi-disciplinary team of collaborative investigators with expertise in ophthalmology, biomedical informatics, computer science, computational biology, ophthalmic genetics, genetic analysis, and statistical genetics.
ROP is a leading cause of childhood blindness in the United States and throughout the world, and the number of infants at risk for disease is increasing as the rate of premature birth rises. Rapidly-progressive changes associated with retinal vascular development and angiogenesis may be visualized by clinical examination, captured by wide-angle imaging, and analyzed genetically. This project will improve our understanding of the pathogenesis of ROP and other neovascular diseases, and will provide tools to help clinicians identify infants at risk for severe ROP using image analysis, genetic analysis, and integrative informatics.
| Redd, Travis K; Campbell, J Peter; Chiang, Michael F (2018) Is This the Right Reference Standard Diagnosis for Retinopathy of Prematurity?-Reply. JAMA Ophthalmol 136:1429-1430 |
| Swan, Ryan; Kim, Sang Jin; Campbell, J Peter et al. (2018) The genetics of retinopathy of prematurity: a model for neovascular retinal disease. Ophthalmol Retina 2:949-962 |
| Chee, Ru-Ik; Darwish, Dana; Fernandez-Vega, Alvaro et al. (2018) Retinal Telemedicine. Curr Ophthalmol Rep 6:36-45 |
| Biten, Hilal; Redd, Travis K; Moleta, Chace et al. (2018) Diagnostic Accuracy of Ophthalmoscopy vs Telemedicine in Examinations for Retinopathy of Prematurity. JAMA Ophthalmol 136:498-504 |
| Redd, Travis K; Campbell, John Peter; Brown, James M et al. (2018) Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. Br J Ophthalmol : |
| Kim, Sang Jin; Port, Alexander D; Swan, Ryan et al. (2018) Retinopathy of prematurity: a review of risk factors and their clinical significance. Surv Ophthalmol 63:618-637 |
| Gupta, Mrinali P; Dow, Eliot; Jeng-Miller, Karen W et al. (2018) SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY FINDINGS IN COATS DISEASE. Retina : |
| Kim, Sang Jin; Campbell, J Peter; Ostmo, Susan et al. (2017) Changes in Relative Position of Choroidal Versus Retinal Vessels in Preterm Infants. Invest Ophthalmol Vis Sci 58:6334-6341 |
| Moleta, Chace; Campbell, J Peter; Kalpathy-Cramer, Jayashree et al. (2017) Plus Disease in Retinopathy of Prematurity: Diagnostic Trends in 2016 Versus 2007. Am J Ophthalmol 176:70-76 |
| Campbell, J Peter; Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica et al. (2016) Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis. JAMA Ophthalmol 134:651-7 |
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