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A clinical prediction model to stratify retinopathy of prematurity risk using postnatal weight gain
Gil Binenbaum1, Gui-shuang Ying, Graham E Quinn
1Division of Ophthalmology, Children's Hospital of Philadelphia, 34th Street and Civic Center Boulevard, 9-MAIN, Philadelphia, PA 19104, USA. binenbaum@email.chop.edu
Insights
A new model using birth weight, gestational age, and postnatal weight gain can identify infants at risk for severe retinopathy of prematurity (ROP). This approach could reduce unnecessary eye exams by 30% while detecting all infants needing treatment.
Area of Science:
- Neonatal Medicine
- Ophthalmology
- Clinical Prediction Modeling
Background:
- Retinopathy of prematurity (ROP) is a leading cause of blindness in premature infants.
- Current screening criteria based on birth weight (BW) and gestational age (GA) have limited efficiency, with <5% of screened infants requiring treatment.
- Identifying infants at high risk for severe ROP is crucial for timely intervention and resource optimization.
Purpose of the Study:
- To develop and validate an efficient clinical prediction model for severe ROP.
- Incorporate postnatal weight gain into the model to improve risk identification.
- Reduce the number of infants undergoing unnecessary eye examinations.
Main Methods:
- Secondary analysis of prospective data from 451 infants with BW < 1000 g.
- Multivariate logistic regression to predict severe ROP (stage 3 or treatment).
- Model included GA, BW, and daily weight gain rate; weekly risk assessment triggered alarms for eye examinations.
Main Results:
- The final cohort included 367 infants; 67 (18.3%) developed severe ROP.
- The prediction model (GA, BW, weight gain rate) achieved 99% sensitivity for identifying severe ROP and detected all infants requiring treatment.
- Implementing the model could have reduced eye examinations by 30% in the high-risk cohort.
Conclusions:
- A prediction model incorporating BW, GA, and postnatal weight gain is effective in identifying high-risk infants for severe ROP.
- This model significantly reduces the need for eye examinations while maintaining high sensitivity for detecting treatable ROP.
- Further studies are needed to validate the model and nomograms in broader infant populations before clinical implementation.
Objective:
To develop an efficient clinical prediction model that includes postnatal weight gain to identify infants at risk of developing severe retinopathy of prematurity (ROP). Under current birth weight (BW) and gestational age (GA) screening criteria, <5% of infants examined in countries with advanced neonatal care require treatment.
Patients And Methods:
This study was a secondary analysis of prospective data from the Premature Infants in Need of Transfusion Study, which enrolled 451 infants with a BW < 1000 g at 10 centers. There were 367 infants who remained after excluding deaths (82) and missing weights (2). Multivariate logistic regression was used to predict severe ROP (stage 3 or treatment).
Results:
Median BW was 800 g (445-995). There were 67 (18.3%) infants who had severe ROP. The model included GA, BW, and daily weight gain rate. Run weekly, an alarm that indicated need for eye examinations occurred when the predicted probability of severe ROP was >0.085. This identified 66 of 67 severe ROP infants (sensitivity of 99% [95% confidence interval: 94%-100%]), and all 33 infants requiring treatment. Median alarm-to-outcome time was 10.8 weeks (range: 1.9-17.6). There were 110 (30%) infants who had no alarm. Nomograms were developed to determine risk of severe ROP by BW, GA, and postnatal weight gain.
Conclusion:
In a high-risk cohort, a BW-GA-weight-gain model could have reduced the need for examinations by 30%, while still identifying all infants requiring laser surgery. Additional studies are required to determine whether including larger-BW, lower-risk infants would reduce examinations further and to validate the prediction model and nomograms before clinical use.
