ROPScore screening tool validation in a 3rd level hospital in Argentina

Abstract

Purpose : Retinopathy of prematurity (ROP) is a developmental alteration that affects normal retinal vascularization and may lead to complete bilateral blindness. ROP screening takes resources and time, and should be wide enough to avoid missed cases. Therefore, it is important to define a strategy to safely diminish the number of screened babies. ROPScore is a mathematical tool that predicts severe ROP using clinical parameters at the 6^th week of life. Our aim is to evaluate if ROPScore can be useful to predict severe ROP in a population of premature babies in Argentina.

Methods : Data from the clinical records of all premature babies examined between 2012 and 2018 in the ophthalmology department of a public third level hospital in Cordoba, Argentina, were obtained. ROPScore screening was applied using a Microsoft Excel spreadsheet. The sensitivity, specificity, positive and negative predictive values of the algorithm was analyzed. Agreement between ROPScore calculations and ROPScore calculator app was assesed.

Results : Between 2012 and 2018, a total of 2894 preterm infants were examined. 411 children met the inclusion criteria, of whom 34% (n 139) presented some form of ROP and 6% (n 25) developed severe forms that required treatment. The sensitivity of the algorithm for any ROP and severe ROP was 100%. The PPV and NPV were 35.64% and 100%, respectively, for any ROP and 9.88% and 100% for severe ROP.

Conclusions : The ROPScore algorithm could identify severe cases after validation for each population, reducing the number of screened babies by 38% in babies ≤1500 gr or ≤32 weeks of gestational age.

This is a 2020 ARVO Annual Meeting abstract.