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G-ROP versus WINROP for retinopathy of prematurity screening: a Calgary perspective
Rahul Moorjani1, Emi Sanders2, Kyla Lavery2
1Department Ophthalmology and Visual Sciences, University of Alberta, Edmonton, AB, Canada.
Insights
The G-ROP model demonstrated 100% sensitivity for detecting retinopathy of prematurity requiring treatment, outperforming the WINROP model in a Canadian cohort. This offers a more accurate and clinically applicable screening tool for at-risk infants.
Area of Science:
- Neonatal ophthalmology
- Pediatric critical care
Background:
- Retinopathy of prematurity (ROP) is a leading cause of childhood blindness.
- Current Canadian screening has high sensitivity but low specificity, leading to unnecessary infant examinations.
- Increased neonatal survival necessitates improved ROP screening to manage physician workload.
Purpose of the Study:
- To validate and compare the accuracy of the G-ROP and WINROP models for identifying neonates at risk of treatment-requiring ROP in a Canadian cohort.
- To assess the clinical utility of G-ROP and WINROP algorithms for ROP screening.
Main Methods:
- Retrospective cohort study of 1001 preterm infants (23-31 weeks GA or ≤1250g birth weight) in Calgary, Canada.
- Assessed sensitivity, specificity, PPV, and NPV of WINROP and G-ROP algorithms.
- Compared algorithm performance against the need for ROP treatment.
Main Results:
- G-ROP model achieved 100% sensitivity for identifying infants needing ROP treatment.
- WINROP algorithm showed 95.7% sensitivity for treatment-requiring ROP.
- G-ROP specificity was 30.4%, while WINROP specificity was 41.7%.
Conclusions:
- The G-ROP model is more appropriate for clinical application in this Canadian cohort.
- G-ROP offers superior sensitivity (100%) and improved specificity compared to current screening guidelines.
- This model can reduce unnecessary examinations and optimize ROP screening for preterm infants.
Objective:
Retinopathy of prematurity (ROP) remains one of the leading causes of childhood blindness. The current screening criteria in Canada have extremely high sensitivity but low specificity, leading to unnecessary examinations of neonates. Moreover, a screening algorithm that reduces the burden of screening is urgently needed owing to the increase in neonatal survival after extreme premature delivery, combined with the limited number of physicians qualified to screen for ROP. This retrospective study aimed to validate and compare the accuracy of the postnatal growth and ROP (G-ROP) and Weight, Insulin-like growth factor-1, Neonatal Retinopathy of Prematurity (WINROP) models for identifying neonates at risk for developing treatment-requiring ROP in a Canadian cohort.
Design:
Single-center retrospective cohort study conducted in Calgary, Alberta, Canada. Data from preterm infants born between 23- and 31-week gestational age or birth weight less than or equal to 1 250 grams were analyzed. A total of 1 001 infants were included in the study. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value for WINROP, and G-ROP algorithms were assessed in identifying neonates at risk of treatment-requiring ROP.
Results:
The WINROP algorithm yielded 95.7% sensitivity in identifying infants requiring ROP treatment compared to 100% sensitivity with the G-ROP model. Specificity for treatment-requiring ROP for WINROP was 41.7% and G-ROP was 30.4%.
Conclusions:
The G-ROP model was found to be more appropriate in our cohort, lending itself seamlessly to clinical care, while providing 100% sensitivity and greater specificity compared to current screening guidelines in our cohort.
