Validation of WINROP (online prediction model) to identify severe retinopathy of prematurity (ROP) in an Australian preterm population: a retrospective study

Abstract

Introduction: Retinopathy of prematurity (ROP) is the most common disease leading to blindness in extreme preterm infants. Current screening guidelines recommend frequent eye examinations. There is a dearth of trained ophthalmologists for these frequent screening procedures. The ANZNN neonatal network report (2013) found that only 6.4% of all screened infants had severe ROP and less than half received treatment. WINROP (online prediction model, Sweden) uses the postnatal weight gain (surrogate marker for low insulin-like growth factor IGF-1 and poor retinal vascular growth) to identify ROP requiring treatment and aims to reduce the number of examinations. Our objective was to validate the WINROP model in an Australian cohort of preterm infants. Methods: Birth weight, gestational age, and weekly weight measurements were retrieved retrospectively along with the final ROP outcomes and plotted on the online WINROP software. Results: The sensitivity, specificity, positive predictive value, and negative predictive value of WINROP were 85.7%, 59.0%, 6.98%, and 99.1% respectively for a cohort of 221 preterm infants (Median birth weight, 1040 g; Gestational age, 27.9 weeks). WINROP alarm was signaled in 42.6% of all infants. WINROP did not signal an alarm in one infant who needed treatment. This infant had intra ventricular hemorrhage grade 3–4 and temporary ventricular dilatation. Conclusions: This is the first Australian study validating WINROP model. Our findings suggest that it lacked sensitivity to be used alone. However, adjusting the algorithm for the Australian population may improve the efficacy and reduce the number of examinations when used along with the current screening guidelines.