Validation of WINROP for detecting retinopathy of prematurity in a North American cohort of preterm infants

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 70^th 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.