Predicting Retinopathy of Prematurity in Thailand: A Practical Model for Risk-Based Screening.

IF 3.8 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Suppachai Lawanaskol, Jayanton Patumanond, Phichayut Phinyo, Songklot Noppakaonamchokchai, Chompoonoot Kunaviktikul, Anavat Bupphachareonsuk, Pakawadi Vuttipittayamongkol, Apichat Tantraworasin, Janejit Choovuthayakorn
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引用次数: 0

Abstract

Background/Objectives: Retinopathy of prematurity (ROP) screening must balance timely detection with feasibility, particularly in resource-limited settings. We aimed to develop a pragmatic, locally derived prediction model to support prioritization of ophthalmologic assessment among Thai neonates already eligible for ROP screening. Methods: We conducted a retrospective cohort study of neonates undergoing their first planned ROP screening examination at Chiang Rai Prachanukroh Hospital (2011-2015) and Lampang Hospital (2021-2022). Prespecified predictors included gestational age, birth weight, multiple pregnancy, respiratory distress syndrome, neonatal anemia, neonatal sepsis, necrotizing enterocolitis, bronchopulmonary dysplasia, duration of oxygen exposure, and duration of mechanical ventilation. A multivariable logistic regression model was developed. Performance was evaluated using discrimination, calibration, decision curve analysis, and bootstrap internal validation with 1000 resamples. Results: Among 681 infants, 50 (7.3%) had any ROP detected at the index examination. The apparent area under the receiver operating characteristic curve was 0.901 (95% CI, 0.856-0.946), and the optimism-corrected estimate was 0.883 (bootstrap 95% CI, 0.841-0.928). Optimism-corrected calibration-in-the-large was 0.008, the expected-to-observed ratio was 1.005, and the calibration slope was 0.866. At the sensitivity-constrained exploratory cut-off of 1.5%, sensitivity was 96.0% and specificity was 53.4%. The observed ROP risk was 14.0% in the higher-risk group and 0.6% in the lower-risk group. Decision curve analysis suggested net benefit across threshold probabilities up to 0.20. Conclusions: The model showed good discrimination and may support risk stratification and prioritization among infants already eligible for ROP screening. The 1.5% cut-off remains exploratory, and independent external validation, preferably in prospective contemporary cohorts, is required before clinical implementation.

预测泰国早产儿视网膜病变:一个基于风险筛选的实用模型。
背景/目的:早产儿视网膜病变(ROP)筛查必须平衡及时发现和可行性,特别是在资源有限的情况下。我们的目的是开发一个实用的,本地衍生的预测模型,以支持泰国已经符合ROP筛查条件的新生儿眼科评估的优先级。方法:对清莱Prachanukroh医院(2011-2015年)和南邦医院(2021-2022年)首次计划进行ROP筛查的新生儿进行回顾性队列研究。预先指定的预测因素包括胎龄、出生体重、多胎妊娠、呼吸窘迫综合征、新生儿贫血、新生儿败血症、坏死性小肠结肠炎、支气管肺发育不良、氧气暴露时间和机械通气时间。建立了多变量logistic回归模型。使用鉴别、校准、决策曲线分析和1000个样本的bootstrap内部验证来评估性能。结果:681例患儿中有50例(7.3%)在指标检查中发现ROP。受试者工作特征曲线下的表观面积为0.901 (95% CI, 0.856 ~ 0.946),乐观校正估计为0.883 (bootstrap 95% CI, 0.841 ~ 0.928)。Optimism-corrected calibration-in-the-large为0.008,expected-to-observed比率为1.005,校准斜率为0.866。在1.5%的敏感性约束下,敏感性为96.0%,特异性为53.4%。观察到高风险组的ROP风险为14.0%,低风险组为0.6%。决策曲线分析表明,跨阈值概率的净收益高达0.20。结论:该模型具有良好的辨别能力,可以支持对已符合ROP筛查条件的婴儿进行风险分层和优先排序。1.5%的临界值仍然是探索性的,在临床实施之前需要独立的外部验证,最好是在前瞻性的当代队列中进行验证。
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来源期刊
Diagnostics
Diagnostics Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍: Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.
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