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Predicting proliferative retinopathy in a Brazilian population of preterm infants with the screening algorithm WINROP
Anna-Lena Hård1, Chatarina Löfqvist, Joao Borges Fortes Filho
1Department of Ophthalmology, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden.
Insights
The WINROP algorithm accurately predicted retinopathy of prematurity (ROP) in Brazilian infants, identifying 90.5% of those who developed severe ROP. This predictive tool aids in managing ROP risk in neonatal intensive care units.
Area of Science:
- Neonatal Medicine
- Ophthalmology
- Predictive Analytics
Background:
- Retinopathy of prematurity (ROP) is a significant cause of visual impairment in preterm infants.
- Early detection and management of ROP are crucial for preventing severe visual outcomes.
- The WINROP (Weight, Insulin-like Growth factor I, Neonatal, Retinopathy of Prematurity) algorithm uses weight trends to predict ROP risk.
Purpose of the Study:
- To retrospectively validate the predictive accuracy of the WINROP algorithm in a Brazilian neonatal population.
- To assess the WINROP algorithm's ability to identify infants at risk for developing severe ROP.
Main Methods:
- Retrospective analysis of weekly weight measurements from preterm infants (gestational age ≤ 32 weeks) admitted to a Brazilian NICU.
- Application of the WINROP algorithm using longitudinal weight data until 36 weeks postmenstrual age.
- Comparison of WINROP alarm status with the development of proliferative ROP.
Main Results:
- The WINROP algorithm identified 90.5% of infants who developed stage 3 ROP.
- In 53% of infants, no or low-risk alarms occurred after 32 weeks, and 190 of these did not develop proliferative disease.
- Of infants with high- or low-risk alarms before or at 32 weeks (47%), 12% developed proliferative ROP.
Conclusions:
- The WINROP algorithm demonstrates significant accuracy in predicting severe ROP in a Brazilian preterm infant population.
- The algorithm's effectiveness suggests its utility for early ROP risk stratification in neonatal intensive care units.
- Potential improvements in WINROP accuracy may be achieved through population-specific algorithm adjustments.
Objective:
To retrospectively validate the WINROP (weight, insulinlike growth factor I, neonatal, retinopathy of prematurity [ROP]) algorithm in a Brazilian population. WINROP aims to predict ROP and is based on longitudinal weight measurements from birth until postmenstrual age 36 weeks. WINROP has predicted 100% of severe ROP in 3 neonatal intensive care unit settings in the United States and Sweden.
Methods:
In children admitted to the neonatal intensive care unit at Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil, from April 2002 to October 2008, weight measurements had been recorded once a week for children screened for ROP, 366 of whom had a gestational age of 32 weeks or less. The participating children had a median gestational age of 30 weeks (range, 24-32 weeks) at birth and their median birth weight was 1215 g (range, 505-2000 g).
Results:
For 192 of 366 children (53%), no alarm or low-risk alarm after postmenstrual age 32 weeks occurred. Of these, 190 of 192 did not develop proliferative disease. Two boys with severe sepsis who were treated for ROP received low-risk alarms at postmenstrual age 33 and 34 weeks, respectively. The remaining 174 children (47%) received high- or low-risk alarms before or at 32 weeks. Of these infants, 21 (12%) developed proliferative ROP.
Conclusions:
In this Brazilian population, WINROP, with limited information on specific gestational age and date of weight measurement, detected early 90.5% of infants who developed stage 3 ROP and correctly predicted the majority who did not. Adjustments to the algorithm for specific neonatal intensive care unit populations may improve the results for specific preterm populations.
