Validation of three weight gain-based algorithms as a screening tool to detect retinopathy of prematurity: A multicenter study

• Published in: Indian journal of ophthalmology Vol. 71; no. 6; pp. 2555 - 2560
• Main Authors: Raffa, Lina, Alamri, Aliaa, Alosaimi, Amal, Alessa, Sarah, Alharbi, Suzan, Ahmedhussain, Huda, Almarzouki, Hashem, AlQurashi, Mansour
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer - Medknow 01.06.2023; Medknow Publications and Media Pvt. Ltd; Medknow Publications & Media Pvt. Ltd; Wolters Kluwer Medknow Publications
• Edition: 2
• Subjects: Algorithms; Analysis; Birth Weight; Care and treatment; co-rop; Developing countries; Diagnosis; Gestational Age; Health aspects; Humans; Infant; Infant, Newborn; Infant, Premature; Infants; Infants (Premature); Medical screening; Medical tests; Methods; Neonatal Screening; Neonates; Neonatology; Newborn babies; Original; Original Article; Premature babies; preterm; Retinopathy; retinopathy of prematurity; Retinopathy of Prematurity - diagnosis; Retinopathy of Prematurity - epidemiology; Retrolental fibroplasia; Retrospective Studies; Risk Factors; ropscore; Weight Gain; winrop; India; Saudi Arabia; Colorado; Georgia; preterm; WINROP; CO-ROP; retinopathy of prematurity; ROPScore
• ISSN: 0301-4738; 1998-3689; 1998-3689
• DOI: 10.4103/ijo.IJO_2013_22

Purpose: Screening guidelines for retinopathy of prematurity (ROP) are updated frequently to help clinicians identify infants at risk of type 1 ROP. This study aims to evaluate the accuracy of three different predictive algorithms-WINROP, ROPScore, and CO-ROP-in detecting ROP in preterm infants in a developing country. Methods: This retrospective study was conducted on 386 preterm infants from two centers between 2015 and 2021. Neonates with gestational age ≤30 weeks and/or birth weight ≤1500 g who underwent ROP screening were included. Results: One hundred twenty-three neonates (31.9%) developed ROP. The sensitivity to identify type 1 ROP was as follows: WINROP, 100%; ROPScore, 100%; and CO-ROP, 92.3%. The specificity was 28% for WINROP, 1.4% for ROPScore, and 19.3% for CO-ROP. CO-ROP missed two neonates with type 1 ROP. WINROP provided the best performance for type 1 ROP with an area under the curve score at 0.61. Conclusion: The sensitivity was at 100% for WINROP and ROPScore for type 1 ROP; however, specificity was quite low for both algorithms. Highly specific algorithms tailored to our population may serve as a useful adjunctive tool to detect preterm infants at risk of sight-threatening ROP.