Jennifer H Cao1, Brandie D Wagner2, Ashlee Cerda3, Emily A McCourt3, Alan Palestine3, Robert W Enzenauer3, Rebecca S Braverman3, Ryan K Wong4, Irena Tsui4, Charlotte Gore5, Shira L Robbins5, Michael A Puente6, Levi Kauffman6, Lingkun Kong6, David G Morrison7, Anne M Lynch3. 1. Department of Ophthalmology, University of Colorado, Denver; Department of Ophthalmology, University of Texas Southwestern Medical Center, Dallas. Electronic address: Jennifer.Cao@utsw.edu. 2. Biostatistics and Informatics, University of Colorado, Denver. 3. Department of Ophthalmology, University of Colorado, Denver. 4. Department of Ophthalmology, University of California-Los Angeles, Los Angeles. 5. Ratner Children's Eye Center in the Shiley Eye Institute, University of California-San Diego, San Diego. 6. Department of Ophthalmology, Baylor College of Medicine, Houston, Texas. 7. Department of Ophthalmology, Vanderbilt University, Nashville, Tennessee.
Abstract
PURPOSE: The Colorado retinopathy of prematurity (ROP) prediction model (CO-ROP), developed using a cohort of infants from Colorado, calls for ROP examination of infants meeting all of the following criteria: gestational age of ≤30 weeks, birth weight of ≤1500 g, and a net weight gain of ≤650 g between birth and 4 weeks of age. The purpose of this study was to perform an external validation to assess the sensitivity and specificity of the CO-ROP model in a larger cohort of babies screened for ROP from four academic institutions in the United States. METHODS: The medical records of neonates screened for ROP according current national guidelines was conducted at 4 US academic centers were retrospectively reviewed. Sensitivity, specificity, and respective 95% confidence intervals in detecting ROP using CO-ROP were calculated for type 1, type 2, and any grade of ROP. RESULTS: A total of 858 cases were included. The CO-ROP algorithm had a sensitivity of 98.1% (95% CI, 93.3%-99.8%) for type 1 ROP, 95.6% (95% CI 78.0-99.9%) for type 2 ROP, and 95.0% (95% CI, 93.1-97.4%) for all grades of ROP. The CO-ROP model would have reduced the total number of infants screened by 23.9% compared to current 2013 screening guidelines. CONCLUSIONS: CO-ROP demonstrated high sensitivity in predicting ROP and would have greatly reduced the number of infants needing examination.
PURPOSE: The Colorado retinopathy of prematurity (ROP) prediction model (CO-ROP), developed using a cohort of infants from Colorado, calls for ROP examination of infants meeting all of the following criteria: gestational age of ≤30 weeks, birth weight of ≤1500 g, and a net weight gain of ≤650 g between birth and 4 weeks of age. The purpose of this study was to perform an external validation to assess the sensitivity and specificity of the CO-ROP model in a larger cohort of babies screened for ROP from four academic institutions in the United States. METHODS: The medical records of neonates screened for ROP according current national guidelines was conducted at 4 US academic centers were retrospectively reviewed. Sensitivity, specificity, and respective 95% confidence intervals in detecting ROP using CO-ROP were calculated for type 1, type 2, and any grade of ROP. RESULTS: A total of 858 cases were included. The CO-ROP algorithm had a sensitivity of 98.1% (95% CI, 93.3%-99.8%) for type 1 ROP, 95.6% (95% CI 78.0-99.9%) for type 2 ROP, and 95.0% (95% CI, 93.1-97.4%) for all grades of ROP. The CO-ROP model would have reduced the total number of infants screened by 23.9% compared to current 2013 screening guidelines. CONCLUSIONS: CO-ROP demonstrated high sensitivity in predicting ROP and would have greatly reduced the number of infants needing examination.
Authors: Sang Jin Kim; Alexander D Port; Ryan Swan; J Peter Campbell; R V Paul Chan; Michael F Chiang Journal: Surv Ophthalmol Date: 2018-04-19 Impact factor: 6.048
Authors: Emily A McCourt; Gui-Shuang Ying; Anne M Lynch; Alan G Palestine; Brandie D Wagner; Erica Wymore; Lauren A Tomlinson; Gil Binenbaum Journal: JAMA Ophthalmol Date: 2018-04-01 Impact factor: 7.389
Authors: Gil Binenbaum; Edward F Bell; Pamela Donohue; Graham Quinn; James Shaffer; Lauren A Tomlinson; Gui-Shuang Ying Journal: JAMA Ophthalmol Date: 2018-09-01 Impact factor: 7.389
Authors: Aldina Pivodic; Helena Johansson; Lois E H Smith; Anna-Lena Hård; Chatarina Löfqvist; Bradley A Yoder; M Elizabeth Hartnett; Carolyn Wu; Marie-Christine Bründer; Wolf A Lagrèze; Andreas Stahl; Abbas Al-Hawasi; Eva Larsson; Pia Lundgren; Lotta Gränse; Birgitta Sunnqvist; Kristina Tornqvist; Agneta Wallin; Gerd Holmström; Kerstin Albertsson-Wikland; Staffan Nilsson; Ann Hellström Journal: Br J Ophthalmol Date: 2021-05-12 Impact factor: 5.908