ArticleJournal of ophthalmology2023
Comparison of Weight-Gain-Based Prediction Models for Retinopathy of Prematurity in an Australian Population.
Article in Journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Prediction Models for Recurrence of Retinopathy of Prematurity after Intravitreal Ranibizumab or Laser Photocoagulation Treatment: A Multicenter Cohort Study.Ophthalmology and therapy · 2026Article
- Postnatal growth and metabolic blood biomarkers in preterm infants developing reversible retinopathy of prematurity.BMJ open ophthalmology · 2025Article
- Laser Photocoagulation Subsequent to Intravitreal Ranibizumab for Retinopathy of Prematurity Long-Term Efficacy in Extremely Low Birth Weight Twins.Journal of ophthalmology · 2025Article
- Retinopathy of Prematurity in Eight Portuguese Neonatal Intensive Care Units: Incidence, Risk Factors, and Progression-A Prospective Multicenter Study.Children (Basel, Switzerland) · 2024Article
- Mechanical Ventilation, Retinal Avascularity and Rate of Vascularisation: A Triad of Predictors for Retinopathy of Prematurity Treatment.Journal of personalized medicine · 2024Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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