Risk factor-based models to predict severe retinopathy of prematurity in preterm Thai infants
6
Issued Date
2024-05-01
Resource Type
ISSN
03014738
eISSN
19983689
Scopus ID
2-s2.0-85190835911
Journal Title
Indian Journal of Ophthalmology
Volume
72
Issue
Suppl 3
Start Page
S514
End Page
S520
Rights Holder(s)
SCOPUS
Bibliographic Citation
Indian Journal of Ophthalmology Vol.72 No.Suppl 3 (2024) , S514-S520
Suggested Citation
Najmuangchan N., Ngerncham S., Piampradad S., Nunthanid P., Tatritorn D., Amnartpanich T., Limkongngam N., Praikanarat T., Arjkongharn N., Udompunthurak S., Atchaneeyasakul L.O., Trinavarat A. Risk factor-based models to predict severe retinopathy of prematurity in preterm Thai infants. Indian Journal of Ophthalmology Vol.72 No.Suppl 3 (2024) , S514-S520. S520. doi:10.4103/IJO.IJO_1640_23 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/98144
Title
Risk factor-based models to predict severe retinopathy of prematurity in preterm Thai infants
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Corresponding Author(s)
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Abstract
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.