Prediction of retinopathy of prematurity using the screening algorithm WINROP in a Saudi cohort of preterm infants

• Published in: Saudi medical journal Vol. 41; no. 6; pp. 622 - 627
• Main Authors: Raffa, Lina H., Alessa, Sarah K., Alamri, Aliaa S., Malaikah, Rawan H.
• Format: Journal Article
• Language: English
• Published: Saudi Arabia Saudi Medical Journal 01.06.2020; Prince Sultan Military Medical City (PSMMC)
• Subjects: Algorithms; Analysis; Birth Weight; Cohort Studies; Diagnostic Techniques, Ophthalmological; Eye diseases; Gestational age; Humans; Infant; Infant, Newborn; Infant, Premature; Insulin-Like Growth Factor I; Mass Screening - methods; Medical screening; Neonatal care; Newborn babies; Ophthalmology; Original; Predictive Value of Tests; Premature babies; Premature birth; Premature infants; Retinopathy of prematurity; Retinopathy of Prematurity - diagnosis; Retrospective Studies; Sensitivity and Specificity; Weight Gain; Saudi Arabia
• ISSN: 0379-5284; 1658-3175; 1658-3175
• DOI: 10.15537/smj.2020.6.25127

To validate the web weight gain-based WINROP (weight, insulin-like growth factor I, neonatal, retinopathy of prematurity [ROP]) algorithm retrospectively to identify type 1 ROP in a Saudi cohort of premature infants.  Methods: The records of preterm infants (greater than 23 and less than 32 weeks gestation) born between August 2013 and October 2018, were reviewed. Birth weight, gestational age, and weekly weight measurements of the premature infants were entered online. Based on weekly weight gain, the WINROP algorithm alerted clinicians whether infants were at high-risk for vision‑threatening type 1 ROP. Sensitivity, specificity, positive and negative predictive values were calculated. Results: The median gestational age of the infants at birth was 28 weeks, with median birth weight at 1085 g. Of the 175 infants included in the study, 13 (7.4%) developed type 1 ROP. WINROP positive alarm was triggered in 70.9% (124/175) of all infants and 100% (13/13) of those treated for type 1 ROP. The specificity of the algorithm was 31.5%. Positive predictive values was 10.5% and negative was 100%. Conclusion: The general WINROP sensitivity in identifying type 1 ROP was 100% similar to that reported in developed countries; however, its specificity was low at 31.5%. Tweaking of the algorithm based on the population may increase the specificity and promote the practical utility of this non-invasive screening tool for ophthalmologists and neonatologists in this population.