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Retinopathy of prematurity: A comprehensive risk analysis for prevention and prediction of disease
Leah A Owen1,2, Margaux A Morrison1,2, Robert O Hoffman1,2
1Department of Ophthalmology and Visual Sciences, John Moran Eye Center, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.
Insights
New models using gestational age, birth weight, surgery, and maternal magnesium better predict retinopathy of prematurity (ROP) risk in preterm infants. This improves infant risk stratification and ROP prevention strategies.
Area of Science:
- Neonatal ophthalmology
- Perinatal medicine
- Biostatistics
Background:
- Retinopathy of prematurity (ROP) is a leading cause of blindness in preterm infants.
- Current ROP screening criteria are outdated and do not effectively identify high-risk infants.
Purpose of the Study:
- To develop a more accurate predictive model for ROP development and severity.
- To identify key maternal, infant, and environmental risk factors for ROP.
- To explore statistical interactions between ROP risk variables.
Main Methods:
- Retrospective cohort analysis of 457 preterm infants undergoing ROP screening (2010-2015).
- Logistic regression and stepwise analysis of 57 proposed ROP risk variables.
- Evaluation of models for overall ROP, severe ROP, and type 1 ROP.
Main Results:
- A model including gestational age, birth weight, surgery, and maternal magnesium prophylaxis demonstrated superior ROP prediction (AUC 0.8641) compared to traditional methods.
- Gestational age, surgery, and early mortality/BPD predicted severe ROP.
- Gestational age and severe chronic lung disease predicted type 1 ROP.
- No significant statistical interactions were found between risk variables.
Conclusions:
- Novel risk models offer improved prediction of ROP development and severity.
- This comprehensive analysis identifies key independent risk factors for ROP.
- Findings support enhanced individual infant risk stratification and ROP prevention.
Background:
Retinopathy of prematurity (ROP) is a blinding morbidity of preterm infants. Our current screening criteria have remained unchanged since their inception and lack the ability to identify those at greatest risk.
Objectives:
We sought to comprehensively analyze numerous proposed maternal, infant, and environmental ROP risk variables in a robustly phenotyped population using logistic regression to determine the most predictive model for ROP development and severity. We further sought to determine the statistical interaction between significant ROP risk variables, which has not previously been done in the field of ROP. We hypothesize that our comprehensive analysis will allow for better identification of risk variables that independently correlate with ROP disease. Going forward, this may allow for improved infant risk stratification along a time continuum from prenatal to postnatal development, making prevention more feasible.
Methods:
We performed a retrospective cohort analysis of preterm infants referred for ROP screening in one neonatal intensive care unit from 2010-2015. The primary outcome measure was presence of ROP. Secondary outcome measures were ROP requiring treatment and severe ROP not clearly meeting current treatment criteria. Univariate, stepwise regression and statistical interaction analyses of 57 proposed ROP risk variables was performed to identify variables which were significantly associated with each outcome measure.
Results:
We identified 457 infants meeting our inclusion criteria. Within this cohort, numerous factors showed a significant individual association with our ROP outcome measures; however, stepwise regression analysis found the most predictive model for overall ROP risk included estimated gestational age, birth weight, the need for any surgery, and maternal magnesium prophylaxis. The corresponding Area Under the Curve (AUC) for this model was 0.8641, while the traditional model of gestational age and birth weight predicted ROP disease less well with an AUC of 0.8489. Development of severe ROP was best predicted by estimated gestational age (week), the need for any surgery and increased probability of death or moderate-severe BPD at 7 days. Finally, the model most predictive for type 1 ROP included estimated gestational age (week) and the presence of severe chronic lung disease. No significant statistical interaction was found between variables.
Conclusions:
Our work is unique as we report comprehensive analysis of the greatest number of proposed ROP risk variables to date in a robustly phenotyped population. We describe novel risk models for our ROP outcome measures and demonstrate independence of these variables using statistical modeling not previously applied to ROP. This may better allow for individual infant risk stratification and importantly mitigation of future risk.
