Objectives:
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 (>23 and <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.
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.
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