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Introduction: WINROP (Weight, Insulin-like growth factor 1, Neonatal Retinopathy of Prematurity) is a computer-based ROP risk which correlate postnatal weight gain with the developed of treatment-requiring ROP. The purpose of this study was to evaluate the ability of the WINROP algorithm to detect severe (Type 1 or Type 2) ROP in a Spanish cohort of infants.
Methods: Birth weight, gestational age, and weekly weight measurements of preterm infants (>23 and <32 weeks gestation) born between 2015 and 2017 were retrospectively collected and entered in WINROP algorithm. Infants were classified according alarm activation and compared with ROP screening outcomes. Sensitivity, specificity, and predictive values were calculated.
Results: A total of 109 infants were included. The mean gestational age was 29.37 ± 2.26 weeks and mean birth weight was 1178 ± 320 g. Alarm occurred in 47.7 % (52/109) of neonates, with a mean time from birth to alarm of 1.9 ± 1.4 weeks. WINROP had a sensitivity of 100% (CI 95%, 59-100), a specificity of 55.9% (CI 95%, 45.7-65.7), a positive predictive value of 13.5% (CI 95%, 11.1-16.2) and a negative predictive value of 100% (CI 95%, 93.7-100) for predicting severe ROP.
Conclusion: The WINROP algorithm has proven to be a useful tool in the detection of severe ROP in our cohort. Nevertheless, in extremely preterm infants (GA <28 weeks) the results should be taken with caution and an optimization of WINROP can be necessary to improve its utility in other populations.
retinopathy, prematurity, WINROP, weight gain, screening tool, retinopathy, WINROP
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