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The Postnatal Growth and Retinopathy of Prematurity Model: A Multi-institutional Validation Study
Islam S H Ahmed1, Wagih Aclimandos2, Nadia Azad2
1Ophthalmology Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
Insights
The G-ROP model effectively predicts retinopathy of prematurity (ROP) in preterm infants. This validated model improves ROP screening efficiency by identifying infants who need early treatment.
Area of Science:
- Neonatology
- Ophthalmology
- Predictive Modeling
Background:
- Retinopathy of prematurity (ROP) is a significant cause of visual impairment in premature infants.
- Current ROP screening methods can be resource-intensive.
- The G-ROP model was developed to enhance screening efficiency.
Purpose of the Study:
- To validate the G-ROP model's predictive accuracy for ROP.
- To assess the G-ROP model's performance in diverse infant cohorts (Egypt and UK).
- To evaluate the potential reduction in infants requiring ROP screening.
Main Methods:
- Retrospective review of preterm infants' records (Jan-June 2018).
- Application of the G-ROP model based on gestational age, birth weight, and postnatal weight gain.
- Calculation of model sensitivity for type 1 ROP and any ROP.
Main Results:
- The G-ROP model achieved 100% sensitivity for predicting type 1 ROP in both Egyptian and UK cohorts.
- The model reduced the number of infants needing screening by 14.1% (Egypt) and 21.8% (UK).
- Validation confirmed the model's efficacy across different populations.
Conclusions:
- The G-ROP model is a validated tool for predicting type 1 ROP.
- The model demonstrates successful application in both Egyptian and UK infant cohorts.
- G-ROP model implementation can optimize ROP screening protocols.
Purpose:
The G-ROP model was proposed to improve the retinopathy of prematurity (ROP) screening efficiency. It is based on gestational age, birth weight and postnatal weight gain. The current study aimed to validate the G-ROP model's ability to predict ROP in cohorts of premature infants from Egypt and the United Kingdom (UK).
Methods:
We retrospectively reviewed the records of preterm infants born between 1st of January and 30th of June 2018 with a known outcome for ROP screening and regular weight measurements until day 39 after birth. We applied the G-ROP model to the study group and calculated the sensitivity of the model for detecting Early Treatment of ROP (ETROP) study type 1 ROP and for any ROP and calculated the reduction of the number of infants requiring ROP screening by the model application.
Results:
We applied the G-ROP model on 605 infants (504 from Egypt and 101 from the UK). The model successfully predicted all type 1 ROP cases (100% sensitivity) in both cohorts (95% confidence interval [CI], 91.1-100% in the Egyptian cohort and 65.5-100% in the UK cohort). The model reduced the number of infants requiring screening by 14.1% in the Egyptian cohort and 21.8% in the UK cohort.
Conclusions:
The G-ROP model was successfully validated for detecting type 1 ROP and in both cohorts from Egypt and the UK.
