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Colorado retinopathy of prematurity model: a multi-institutional validation study
Jennifer H Cao1, Brandie D Wagner2, Ashlee Cerda3
1Department of Ophthalmology, University of Colorado, Denver; Department of Ophthalmology, University of Texas Southwestern Medical Center, Dallas.
Insights
The Colorado retinopathy of prematurity (ROP) prediction model (CO-ROP) shows high sensitivity in identifying infants who need ROP screening. This model could significantly decrease the number of infants undergoing unnecessary examinations.
Area of Science:
- Neonatal Medicine
- Ophthalmology
- Predictive Analytics
Background:
- Retinopathy of prematurity (ROP) is a significant cause of visual impairment in premature infants.
- Current screening guidelines for ROP involve a large number of infants, leading to resource strain.
- The Colorado retinopathy of prematurity (CO-ROP) prediction model was developed to identify infants at high risk for ROP.
Purpose of the Study:
- To externally validate the sensitivity and specificity of the CO-ROP prediction model.
- To assess the CO-ROP model's performance in a larger, multi-institutional cohort of neonates.
- To determine the potential reduction in ROP screenings using the CO-ROP model.
Main Methods:
- Retrospective review of medical records from neonates screened for ROP at four US academic centers.
- Calculation of sensitivity, specificity, and 95% confidence intervals for the CO-ROP model in detecting different grades of ROP.
- Comparison of CO-ROP screening criteria against current national guidelines.
Main Results:
- The CO-ROP algorithm demonstrated high sensitivity: 98.1% for type 1 ROP, 95.6% for type 2 ROP, and 95.0% for all grades of ROP.
- External validation confirmed the model's effectiveness in a diverse cohort.
- Application of the CO-ROP model could reduce the total number of infants screened by 23.9%.
Conclusions:
- The CO-ROP prediction model exhibits high sensitivity for identifying infants requiring ROP examination.
- Implementing the CO-ROP model has the potential to significantly decrease the number of infants undergoing ROP screenings.
- This validation supports the CO-ROP model as a tool to optimize ROP screening protocols.
Purpose:
The Colorado retinopathy of prematurity (ROP) prediction model (CO-ROP), developed using a cohort of infants from Colorado, calls for ROP examination of infants meeting all of the following criteria: gestational age of ≤30 weeks, birth weight of ≤1500 g, and a net weight gain of ≤650 g between birth and 4 weeks of age. The purpose of this study was to perform an external validation to assess the sensitivity and specificity of the CO-ROP model in a larger cohort of babies screened for ROP from four academic institutions in the United States.
Methods:
The medical records of neonates screened for ROP according current national guidelines was conducted at 4 US academic centers were retrospectively reviewed. Sensitivity, specificity, and respective 95% confidence intervals in detecting ROP using CO-ROP were calculated for type 1, type 2, and any grade of ROP.
Results:
A total of 858 cases were included. The CO-ROP algorithm had a sensitivity of 98.1% (95% CI, 93.3%-99.8%) for type 1 ROP, 95.6% (95% CI 78.0-99.9%) for type 2 ROP, and 95.0% (95% CI, 93.1-97.4%) for all grades of ROP. The CO-ROP model would have reduced the total number of infants screened by 23.9% compared to current 2013 screening guidelines.
Conclusions:
CO-ROP demonstrated high sensitivity in predicting ROP and would have greatly reduced the number of infants needing examination.
