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Early Prediction Model for Retinopathy of Prematurity Using Placental and Neonatal Risk Factors
Salma El Emrani1,2, Frank Doornkamp3, Jelle J Goeman3
1Department of Ophthalmology, Leiden University Medical Center, Leiden, The Netherlands.
Insights
A new model predicts retinopathy of prematurity (ROP) risk in high-risk neonates using placental and early factors. This allows earlier intervention before standard ROP screenings begin.
Area of Science:
- Neonatal Medicine
- Ophthalmology
- Pathology
Background:
- Existing retinopathy of prematurity (ROP) prediction models focus on screening starting 5-7 weeks post-birth.
- Early identification of high-risk neonates is crucial for timely intervention.
- Placental and early postnatal factors may offer predictive value before standard ROP screening.
Purpose of the Study:
- To develop and validate a model incorporating placental and early postnatal risk factors for earlier ROP prediction.
- To identify high-risk neonates for retinopathy of prematurity (ROP) before conventional screening.
- To enable earlier consideration of neonatal treatment for ROP prevention.
Main Methods:
- Retrospective analysis of 591 neonates born ≤32 weeks gestational age and/or ≤1500 grams birthweight.
- Placental examination for histological abnormalities.
- Development of the Placenta as an Additional Predictor for ROP (PAPROP) model including gestational age, birthweight, ventilation, postnatal corticosteroids, and placental pathologies (chorioamnionitis, villous hypoplasia).
- Internal validation using five-fold cross-validation and comparison with a reference model (GA & BW only).
Main Results:
- The PAPROP model demonstrated a higher discriminatory ability (0.81) compared to the reference model (0.78).
- The model achieved a sensitivity of 0.97 and specificity of 0.44 at a 10% threshold.
- Implementing PAPROP in the second postnatal week could reduce ROP screenings by 25% without missing severe ROP stages.
Conclusions:
- The PAPROP model accurately predicts ROP development by the second postnatal week.
- This model shows potential for high clinical utility and cost-effectiveness in ROP management.
- Further external validation may support personalized neonatal treatment for ROP prevention in high-risk infants.
Purpose:
Existing prediction models for retinopathy of prematurity (ROP) primarily focus on screening reduction which occur 5 to 7 weeks after birth. We hypothesized that high-risk neonates could be identified much earlier if placental and early postnatal risk factors are incorporated, so that this high risk can be considered during neonatal treatment well before ROP screening begins.
Methods:
We included 591 neonates born ≤32 weeks of gestational age (GA) and/or birthweight (BW) ≤1500 grams. Data were retrospectively collected, and placentas were examined for histological abnormalities. The "Placenta as an Additional Predictor for ROP" (PAPROP) model included: GA, BW, mechanical ventilation, postnatal corticosteroids, severe histological chorioamnionitis, and distal villous hypoplasia. This model was internally validated with five-fold cross-validation and compared to a reference model using only GA and BW.
Results:
The PAPROP model had a discriminatory ability between ROP presence and absence of 0.81 (95% confidence interval [CI] = 0.76-0.86) compared to 0.78 in the reference GA&BW model. This model had a sensitivity of 0.97 and specificity of 0.44 in the test set (threshold 10%). Using clinically relevant thresholds of 10% to 15%, implementing this model in the second postnatal week could lead to a 25% reduction in ROP screenings without missing stage 2 and severe ROP.
Conclusions:
The PAPROP model has a high ability to predict ROP development at the end of the second postnatal week, has a potential high clinical utility, and is likely cost-effective. After further external validation, it may aid in creating a personalized neonatal treatment approach for ROP prevention in high-risk neonates.
