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Prediction of retinopathy of prematurity using the screening algorithm WINROP in a Mexican population of preterm
Insights
The WINROP algorithm effectively identified type 1 retinopathy of prematurity in very preterm infants but showed limited accuracy in moderately preterm infants in Mexico. Further research is needed to understand these performance variations.
Area of Science:
- Neonatology
- Ophthalmology
- Pediatrics
Background:
- Retinopathy of prematurity (ROP) is a significant cause of visual impairment in preterm infants.
- Early identification of type 1 ROP is crucial for timely intervention and prevention of vision loss.
- The WINROP (weight, insulin-like growth factor I, neonatal, retinopathy of prematurity) algorithm is a tool designed for early ROP detection.
Purpose of the Study:
- To evaluate the effectiveness of the WINROP algorithm in identifying type 1 ROP among preterm infants in a Mexican population.
- To compare the WINROP algorithm's performance in very preterm (gestational age <32 weeks) versus moderately preterm (gestational age ≥32 weeks) infants.
Main Methods:
- A retrospective validation study was conducted on 192 very preterm and 160 moderately preterm infants.
- Weekly weight measurements and recorded maximal ROP stages from eye examinations were analyzed.
- Data were sourced from a case-control database for severe ROP risk factors.
Main Results:
- Type 1 ROP prevalence was 51.0% in very preterm and 35.6% in moderately preterm infants.
- The WINROP algorithm achieved 84.7% accuracy in identifying type 1 ROP in very preterm infants.
- Accuracy was significantly lower (5.3%) in moderately preterm infants, with specificities of 26.6% and 88.3% for very preterm and moderately preterm groups, respectively.
Conclusions:
- The WINROP algorithm shows high efficacy in early detection of type 1 ROP among very preterm infants in this Mexican cohort.
- Performance variations in moderately preterm infants may be attributed to uncertainties in gestational dating and differing postnatal care.
- Further validation and potential algorithm refinement are warranted for diverse populations.
Objective:
To retrospectively validate the WINROP (weight, insulin-like growth factor I, neonatal, retinopathy of prematurity [ROP]) algorithm in identification of type 1 ROP in a Mexican population of preterm infants.
Methods:
In infants admitted to the neonatal intensive care unit at Hospital Civil de Guadalajara from 2005 to 2010, weight measurements had been recorded once weekly for 192 very preterm infants (gestational age [GA] <32 weeks) and for 160 moderately preterm infants (GA ≥32 weeks). Repeated eye examinations had been performed and maximal ROP stage had been recorded. Data are part of a case-control database for severe ROP risk factors.
Results:
Type 1 ROP was found in 51.0% of very preterm and 35.6% of moderately preterm infants. The WINROP algorithm correctly identified type 1 ROP in 84.7% of very preterm infants but in only 5.3% of moderately preterm infants. For infants with GA less than 32 weeks, the specificity was 26.6%, and for those with GA 32 weeks or more, it was 88.3%.
Conclusions:
In this Mexican population of preterm infants, WINROP detected type 1 ROP early in 84.7% of very preterm infants and correctly identified 26.6% of infants who did not develop type 1 ROP. Uncertainties in dating of pregnancies and differences in postnatal conditions may be factors explaining the different outcomes of WINROP in this population.
