Jennifer L Jung1, Brandie D Wagner2, Emily A McCourt3, Alan G Palestine4, Ashlee Cerda5, Jennifer H Cao6, Robert W Enzenauer3, Jasleen K Singh3, Rebecca S Braverman3, Erica Wymore7, Anne M Lynch5. 1. Department of Ophthalmology, Children's Hospital Colorado, University of Colorado School of Medicine, Aurora, Colorado. Electronic address: Jennifer.Jung@ucdenver.edu. 2. Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado. 3. Department of Ophthalmology, Children's Hospital Colorado, University of Colorado School of Medicine, Aurora, Colorado. 4. Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado. 5. Division of Ophthalmic Epidemiology, Department of Ophthalmology University of Colorado School of Medicine, Aurora, Colorado. 6. Department of Ophthalmology, University of Texas Southwestern Medical Center, Dallas, Texas. 7. Department of Pediatrics, Section of Neonatology, University of Colorado School of Medicine, Aurora, Colorado.
Abstract
BACKGROUND: WINROP (weight, insulin-like growth factor 1, neonatal, retinopathy of prematurity) is a web-based retinopathy of prematurity (ROP) risk algorithm that uses postnatal weight gain as a surrogate of insulin-like growth factor-1 (IGF-1) to predict the risk of severe ROP in premature infants. The purpose of this study was to validate the web-based algorithm WINROP in detecting severe (type 1 or type 2) ROP in a North American cohort of infants. METHODS: The records of consecutive infants who underwent ROP examinations between 2008 and 2011 were reviewed retrospectively. Infants were classified into categories of "alarm" (at risk for developing severe ROP) and "no alarm" (minimal risk for severe ROP). RESULTS: A total of 483 were included. Alarm occurred in 241 neonates (50%), with the median time from birth to alarm of 2 weeks. WINROP had a sensitivity of 81.8% (95% CI, 67.3%-91.8%) and specificity of 53.3% (95% CI, 48.5%-58.0%) for identifying infants with severe ROP. Eight of the 44 infants with severe ROP were not detected (5 with type 1 and 3 with type 2). Of these 8 infants, 7 (88%) had birth weight in excess of the 70th pecentile. With additional weight data entry, sensitivity of WINROP rose to 88.6%. CONCLUSIONS: Very preterm infants (gestational age of ≤27 weeks) with relatively high birth weight for gestational age may not be detected by WINROP as high risk for developing severe ROP.
BACKGROUND: WINROP (weight, insulin-like growth factor 1, neonatal, retinopathy of prematurity) is a web-based retinopathy of prematurity (ROP) risk algorithm that uses postnatal weight gain as a surrogate of insulin-like growth factor-1 (IGF-1) to predict the risk of severe ROP in premature infants. The purpose of this study was to validate the web-based algorithm WINROP in detecting severe (type 1 or type 2) ROP in a North American cohort of infants. METHODS: The records of consecutive infants who underwent ROP examinations between 2008 and 2011 were reviewed retrospectively. Infants were classified into categories of "alarm" (at risk for developing severe ROP) and "no alarm" (minimal risk for severe ROP). RESULTS: A total of 483 were included. Alarm occurred in 241 neonates (50%), with the median time from birth to alarm of 2 weeks. WINROP had a sensitivity of 81.8% (95% CI, 67.3%-91.8%) and specificity of 53.3% (95% CI, 48.5%-58.0%) for identifying infants with severe ROP. Eight of the 44 infants with severe ROP were not detected (5 with type 1 and 3 with type 2). Of these 8 infants, 7 (88%) had birth weight in excess of the 70th pecentile. With additional weight data entry, sensitivity of WINROP rose to 88.6%. CONCLUSIONS: Very preterm infants (gestational age of ≤27 weeks) with relatively high birth weight for gestational age may not be detected by WINROP as high risk for developing severe ROP.
Authors: Christoph Bührer; Ömer Erdeve; Anton van Kaam; Angelika Berger; Evelyn Lechner; Benjamin Bar-Oz; Karel Allegaert; Tom Stiris; İstemi Han Çelik; Janet Berrington Journal: Pediatr Res Date: 2017-08-16 Impact factor: 3.756
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: Stanford Taylor; James M Brown; Kishan Gupta; J Peter Campbell; Susan Ostmo; R V Paul Chan; Jennifer Dy; Deniz Erdogmus; Stratis Ioannidis; Sang J Kim; Jayashree Kalpathy-Cramer; Michael F Chiang Journal: JAMA Ophthalmol Date: 2019-07-03 Impact factor: 7.389