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Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population
Alexander Bremner1, Li Yen Chan2, Courtney Jones2
1University of Sydney, Ophthalmology, Camperdown 2006, NSW, Australia.
Insights
Four weight-gain algorithms for predicting type 1 retinopathy of prematurity (ROP) were evaluated in Australian infants. The CHOPROP, CO-ROP, and G-ROP algorithms demonstrated strong performance, potentially reducing the need for ROP screenings.
Area of Science:
- Ophthalmology
- Neonatal Medicine
- Pediatrics
Background:
- Retinopathy of prematurity (ROP) is a significant cause of visual impairment in premature infants.
- Accurate prediction of type 1 ROP is crucial for timely intervention and preventing vision loss.
- Current screening guidelines may lead to over-examination of infants not at risk for severe ROP.
Purpose of the Study:
- To compare the performance of four weight-gain-based algorithms for predicting type 1 ROP in an Australian cohort.
- To evaluate the sensitivity, specificity, and predictive values of the WINROP, CHOPROP, CO-ROP, and G-ROP algorithms.
- To assess the potential of these algorithms to optimize ROP screening protocols.
Main Methods:
- A retrospective cohort analysis of 531 infants screened for ROP in a tertiary neonatal intensive care unit in Brisbane, Australia.
- Inclusion of infants with a mean gestational age of 28 weeks and 3 days.
- Calculation of sensitivities, specificities, and predictive values for each algorithm in identifying type 1 ROP.
Main Results:
- 24 infants (4.5%) developed type 1 ROP.
- CHOPROP, CO-ROP, and G-ROP achieved 100% sensitivity for type 1 ROP prediction.
- These algorithms demonstrated varying specificities, with CHOPROP at 46.0%, CO-ROP at 32.0%, and G-ROP at 28.2%.
- CHOPROP, CO-ROP, and G-ROP could reduce the number of infants requiring examinations by 43.9%, 30.5%, and 26.9%, respectively.
Conclusions:
- CHOPROP, CO-ROP, and G-ROP algorithms performed effectively in this Australian ROP cohort.
- These weight-gain-based algorithms show promise in reducing unnecessary ROP examinations.
- Implementing these algorithms could serve as a valuable adjunct to current ROP screening practices.
Purpose:
Four weight-gain-based algorithms are compared for the prediction of type 1 ROP in an Australian cohort: the weight, insulin-like growth factor, neonatal retinopathy of prematurity (WINROP) algorithm, the Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOPROP), the Colorado Retinopathy of Prematurity (CO-ROP) algorithm, and the postnatal growth, retinopathy of prematurity (G-ROP) algorithm.
Methods:
A four-year retrospective cohort analysis of infants screened for ROP in a tertiary neonatal intensive care unit in Brisbane, Australia. The main outcome measures were sensitivities, specificities, and positive and negative predictive values.
Results:
531 infants were included (mean gestational age 28 + 3). 24 infants (4.5%) developed type 1 ROP. The sensitivities, specificities, and negative predictive values, respectively, for type 1 ROP (95% confidence intervals) were for WINROP 83.3% (61.1-93.3%), 52.3% (47.8-56.7%), and 98.4% (96.1-99.4%); for CHOPROP 100% (86.2-100%), 46.0% (41.7-50,3%), and 100% (98.4-100%); for CO-ROP 100% (86.2-100%), 32.0% (28.0%-36.1%), and 100% (98.3-100%); and for G-ROP 100% (86.2-100%), 28.2% (24.5-32.3%), and 100% (97.4-100%). Of the five infants with persistent nontype 1 ROP that underwent treatment, only CO-ROP was able to successfully identify all.
Conclusions:
CHOPROP, CO-ROP, and G-ROP performed well in this Australian population. CHOPROP, CO-ROP, and G-ROP would reduce the number of infants requiring examinations by 43.9%, 30.5%, and 26.9%, respectively, compared to current ROP screening guidelines. Weight-gain-based algorithms would be a useful adjunct to the current ROP screening.
