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Postnatal Growth and Retinopathy of Prematurity Study: Rationale, Design, and Subject Characteristics
Gil Binenbaum1,2, Lauren A Tomlinson1
1a The Children's Hospital of Philadelphia , Philadelphia , PA , USA.
Insights
This study developed a predictive model using postnatal growth data to improve retinopathy of prematurity (ROP) screening. The large G-ROP dataset enables precise sensitivity estimates for severe ROP, enhancing screening efficiency.
Area of Science:
- Ophthalmology
- Neonatology
- Biostatistics
Background:
- Retinopathy of prematurity (ROP) screening has low specificity, leading to unnecessary interventions.
- Prior predictive models for ROP are limited by small sample sizes.
- Postnatal growth data offers potential for more accurate ROP screening.
Purpose of the Study:
- To develop a predictive model for severe ROP using a large cohort of at-risk infants.
- To achieve highly precise sensitivity estimates for severe ROP detection.
- To improve the efficiency of ROP screening through growth-based algorithms.
Main Methods:
- A multicenter retrospective cohort study (G-ROP Study) involving 30 North American hospitals.
- Data collection from 8334 enrolled infants, including ROP findings, growth parameters, and clinical data.
- Rigorous data quality monitoring through validation rules, audits, and discrepancy algorithms.
Main Results:
- The study included 7483 infants with known ROP outcomes.
- Median birth weight was 1070g, mean gestational age 28 weeks.
- Severe ROP developed in 12.5% (931) of infants.
Conclusions:
- The large G-ROP dataset allows for precise estimation of severe ROP sensitivity (<0.5% half-confidence interval width).
- This study provides a robust dataset for evaluating growth-based algorithms to enhance ROP screening.
- The findings support the integration of predictive models into clinical ROP screening protocols.
Purpose:
Postnatal-growth-based predictive models demonstrate strong potential for improving the low specificity of retinopathy of prematurity (ROP) screening. Prior studies are limited by inadequate sample size. We sought to study a sufficiently large cohort of at-risk infants to enable development of a model with highly precise estimates of sensitivity for severe ROP.
Methods:
The Postnatal Growth and ROP (G-ROP) Study was a multicenter retrospective cohort study of infants at 30 North American hospitals during 2006-2012. A total of 65 G-ROP-certified abstractors submitted data to a secure, web-based database. Data included ROP examination findings, treatments, complications, daily weight measurements, daily oxygen supplementation, maternal/infant demographics, medical comorbidities, surgical events, and weekly nutrition. Data quality was monitored with system validation rules, data audits, and discrepancy algorithms.
Results:
Of 11,261 screened infants, 8334 were enrolled, and 2927 had insufficient data due to transfer, discharge, or death. Of the enrolled infants, 90% (7483) had a known ROP outcome and were included in the study. Median birth weight was 1070 g (range 310-3000g) and mean gestational age 28 weeks (range 22-35 weeks). Severe ROP (Early Treatment of Retinopathy type 1 or 2) developed in 931 infants (12.5%).
Conclusion:
Successful incorporation of a predictive model into ROP screening requires confidence that it will capture cases of severe ROP. This dataset provides power to estimate sensitivity with half-confidence interval width of less than 0.5%, determined by the high number of severe ROP cases. The G-ROP Study represents a large, diverse cohort of at-risk infants undergoing ROP screening. It will facilitate evaluation of growth-based algorithms to improve efficiency of ROP screening.
