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The CHOP postnatal weight gain, birth weight, and gestational age retinopathy of prematurity risk model
Insights
A new model using birth weight, gestational age, and postnatal weight gain accurately predicts retinopathy of prematurity (ROP) risk. This tool could significantly reduce ROP screening examinations for premature infants.
Area of Science:
- Neonatalogy
- Ophthalmology
- Biostatistics
Background:
- Retinopathy of prematurity (ROP) is a leading cause of visual impairment in premature infants.
- Current screening guidelines for ROP involve frequent ophthalmological examinations.
- Predictive models can potentially optimize ROP screening protocols.
Purpose of the Study:
- To develop and validate a prediction model for ROP risk.
- The model incorporates birth weight (BW), gestational age (GA), and postnatal weight gain.
- The aim is to identify infants requiring treatment for ROP more efficiently.
Main Methods:
- Retrospective analysis of data from premature infants (BW < 1501g or GA ≤ 30 weeks).
- Multivariate logistic regression used BW, GA, and daily weight gain rate.
- Risk assessment was performed weekly to guide examination indications.
Main Results:
- The Children's Hospital of Philadelphia (CHOP) ROP model accurately predicted all cases of type 1 ROP.
- The model could have reduced ROP examinations by 49% compared to standard screening.
- Adjusting the risk threshold could further decrease examinations, with a potential 79% reduction by missing one type 1 ROP case.
Conclusions:
- The BW-GA-weight gain CHOP ROP model offers accurate risk assessment for ROP.
- This model significantly reduces the need for ROP examinations compared to current guidelines.
- Further research with larger cohorts is necessary to refine sensitivity estimates before widespread clinical adoption.
Objective:
To develop a birth weight (BW), gestational age (GA), and postnatal-weight gain retinopathy of prematurity (ROP) prediction model in a cohort of infants meeting current screening guidelines.
Methods:
Multivariate logistic regression was applied retrospectively to data from infants born with BW less than 1501 g or GA of 30 weeks or less at a single Philadelphia hospital between January 1, 2004, and December 31, 2009. In the model, BW, GA, and daily weight gain rate were used repeatedly each week to predict risk of Early Treatment of Retinopathy of Prematurity type 1 or 2 ROP. If risk was above a cut-point level, examinations would be indicated.
Results:
Of 524 infants, 20 (4%) had type 1 ROP and received laser treatment; 28 (5%) had type 2 ROP. The model (Children's Hospital of Philadelphia [CHOP]) accurately predicted all infants with type 1 ROP; missed 1 infant with type 2 ROP, who did not require laser treatment; and would have reduced the number of infants requiring examinations by 49%. Raising the cut point to miss one type 1 ROP case would have reduced the need for examinations by 79%. Using daily weight measurements to calculate weight gain rate resulted in slightly higher examination reduction than weekly measurements.
Conclusions:
The BW-GA-weight gain CHOP ROP model demonstrated accurate ROP risk assessment and a large reduction in the number of ROP examinations compared with current screening guidelines. As a simple logistic equation, it can be calculated by hand or represented as a nomogram for easy clinical use. However, larger studies are needed to achieve a highly precise estimate of sensitivity prior to clinical application.
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