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Risk factor-based models to predict severe retinopathy of prematurity in preterm Thai infants
Natthapicha Najmuangchan1, Sopapan Ngerncham1, Saranporn Piampradad2
1Division of Neonatology, Department of Pediatrics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Insights
A new risk factor algorithm for retinopathy of prematurity (ROP) in preterm infants can significantly reduce eye exams. This model helps identify infants needing ROP screening, improving efficiency in neonatal care.
Area of Science:
- Neonatal Ophthalmology
- Public Health
- Medical Informatics
Background:
- Retinopathy of prematurity (ROP) is a leading cause of visual impairment in preterm infants.
- Current screening protocols may lead to unnecessary eye examinations in low-risk infants.
- Developing accurate predictive models can optimize ROP screening strategies.
Purpose of the Study:
- To create predictive models for severe ROP in preterm Thai infants.
- To reduce unnecessary eye examinations for low-risk infants.
- To identify key risk factors for severe ROP.
Main Methods:
- Retrospective cohort study of preterm infants screened for ROP (September 2009 - December 2020).
- Development of a predictive score model and a risk factor-based algorithm using multivariate logistic regression.
- Analysis of independent risk factors including gestational age, birth weight, steroid use, and oxygen supplementation.
Main Results:
- The study included 845 preterm infants with a 26.2% ROP prevalence.
- Key risk factors identified: gestational age, birth weight, antenatal/postnatal steroid use, oxygen duration, and weight gain.
- The risk factor-based algorithm demonstrated 100% sensitivity and 100% NPV, reducing eye exams by 43% (modified) to 71% (unmodified).
Conclusions:
- A risk factor-based algorithm is effective in reducing unnecessary ROP eye examinations.
- The developed model maintains safety for infants at risk of severe ROP.
- Prospective validation of the predictive model is recommended for clinical implementation.
Purpose:
To develop prediction models for severe retinopathy of prematurity (ROP) based on risk factors in preterm Thai infants to reduce unnecessary eye examinations in low-risk infants.
Methods:
This retrospective cohort study included preterm infants screened for ROP in a tertiary hospital in Bangkok, Thailand, between September 2009 and December 2020. A predictive score model and a risk factor-based algorithm were developed based on the risk factors identified by a multivariate logistic regression analysis. Validity scores, and corresponding 95% confidence intervals (CIs), were reported.
Results:
The mean gestational age and birth weight (standard deviation) of 845 enrolled infants were 30.3 (2.6) weeks and 1264.9 (398.1) g, respectively. The prevalence of ROP was 26.2%. Independent risk factors across models included gestational age, birth weight, no antenatal steroid use, postnatal steroid use, duration of oxygen supplementation, and weight gain during the first 4 weeks of life. The predictive score had a sensitivity (95% CI) of 92.2% (83.0, 96.6), negative predictive value (NPV) of 99.2% (98.1, 99.6), and negative likelihood ratio (NLR) of 0.1. The risk factor-based algorithm revealed a sensitivity of 100% (94, 100), NPV of 100% (99, 100), and NLR of 0. Similar validity was observed when "any oxygen supplementation" replaced "duration of oxygen supplementation." Predictive score, unmodified, and modified algorithms reduced eye examinations by 71%, 43%, and 16%, respectively.
Conclusions:
Our risk factor-based algorithm offered an efficient approach to reducing unnecessary eye examinations while maintaining the safety of infants at risk of severe ROP. Prospective validation of the model is required.
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