Division of Neonatology, Department of Pediatrics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Department of Ophthalmology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Indian J Ophthalmol. 2024 May 1;72(Suppl 3):S514-S520. doi: 10.4103/IJO.IJO_1640_23. Epub 2024 Apr 20.
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.
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.
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.
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.
基于泰国早产儿的危险因素开发严重早产儿视网膜病变（ROP）预测模型，以减少低危婴儿的不必要眼部检查。
本回顾性队列研究纳入了 2009 年 9 月至 2020 年 12 月在泰国曼谷一家三级医院接受 ROP 筛查的早产儿。通过多变量逻辑回归分析确定危险因素后，建立预测评分模型和基于危险因素的算法。报告了有效性评分及其相应的 95%置信区间（CI）。
845 名入组婴儿的平均胎龄和出生体重（标准差）分别为 30.3（2.6）周和 1264.9（398.1）g，ROP 患病率为 26.2%。模型中独立的危险因素包括胎龄、出生体重、无产前类固醇使用、产后类固醇使用、氧疗持续时间和出生后第 4 周的体重增加。预测评分的敏感性（95%CI）为 92.2%（83.0，96.6），阴性预测值（NPV）为 99.2%（98.1，99.6），阴性似然比（NLR）为 0.1。基于危险因素的算法显示敏感性为 100%（94，100），NPV 为 100%（99，100），NLR 为 0.1。当“任何氧疗”取代“氧疗持续时间”时，也观察到类似的有效性。未修改、修改后的预测评分和算法分别减少了 71%、43%和 16%的眼部检查。
我们的基于危险因素的算法为减少不必要的眼部检查提供了一种有效的方法，同时保持了高危严重 ROP 婴儿的安全性。需要前瞻性验证该模型。
