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Related Experiment Video

Updated: Jun 13, 2025

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

Published on: August 6, 2021

Can We Optimize Retinopathy of Prematurity Screening by Combined Risk Score Analysis?

Emma Banwell¹, Daniel York², Sravanthi Vegunta³

¹Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT.

The Journal of Pediatrics

|March 20, 2025

Summary

A new composite metric combining DIGIROP-Birth (DRB) and Neonatal Research Network Bronchopulmonary Dysplasia Outcome Estimator (NRN-BPD) accurately predicts severe retinopathy of prematurity requiring treatment (TR-ROP). This approach achieves 100% sensitivity while reducing the number of infants needing screening.

Keywords:

bronchopulmonary dysplasia prematurity retinopathy of prematurity screening

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Last Updated: Jun 13, 2025

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Published on: March 24, 2020

Area of Science:

Neonatal Ophthalmology
Perinatal Medicine
Predictive Analytics in Healthcare

Background:

Retinopathy of prematurity (ROP) is a leading cause of visual impairment in premature infants.
Early detection and treatment of severe ROP are crucial to prevent vision loss.
Current screening methods may lead to over-examination of infants at low risk.

Purpose of the Study:

To evaluate a composite metric using DIGIROP-Birth (DRB) and Neonatal Research Network Bronchopulmonary Dysplasia Outcome Estimator (NRN-BPD) scores.
To assess the metric's ability to predict severe retinopathy of prematurity requiring treatment (TR-ROP).
To determine if this composite metric can optimize ROP screening protocols.

Main Methods:

Retrospective cohort study of 990 premature infants.
Chart review to identify TR-ROP and stage 2 or greater ROP.
Quantification of DRB and 14-day NRN-BPD scores.
Receiver operator characteristic (ROC) curve analysis to determine optimal thresholds and assess sensitivity/specificity.

Main Results:

The composite metric demonstrated high predictive accuracy for TR-ROP (AUC for DRB: 0.867; NRN-BPD: 0.845).
Optimal cutoffs identified: DRB score of 1.7 (97% sensitivity) and NRN-BPD of 40% (96% sensitivity).
A composite screening approach achieved 100% sensitivity for TR-ROP and reduced the number of infants requiring screening by 43% (562 infants).

Conclusions:

The composite metric of DRB and NRN-BPD offers a sensitive and potentially more efficient method for predicting TR-ROP.
This approach may facilitate early identification of infants needing treatment while reducing the burden of screening examinations.
Further large-scale validation studies are recommended to confirm these findings.