ESTIMATING THE RISK OF SEVERE RETINOPATHY OF PREMATURITY

早产儿视网膜病变（Retinopathy of prematurity， ROP）是一种发生在未成熟视网膜的纤维血管增生性疾病，是导致早产儿视力损害和失明的重要原因。治疗严重（阈值）ROP的结果并不总是成功的。人们对预防严重的ROP越来越感兴趣，这一点从研究早期干预被认为有阈值ROP风险的婴儿的建议中可以明显看出。准确早期识别严重ROP风险最大的婴儿对于进行积极的早期干预试验以及避免将低风险婴儿不必要地暴露于实验或临床干预至关重要。基于早产儿视网膜病变冷冻治疗多中心试验（CRYO ROP）的数据，建立了一系列ROP风险模型，但这些模型反映了1986-1987年的婴儿和护理实践。包括新的预测因素的当代早产儿ROP风险模型的发展将有助于早期识别有严重ROP风险的婴儿。与CRYO ROP风险模型相比，我们建议验证一个包含疾病严重程度、眼科检查结果顺序评分、胎龄和预测人口变量的当代ROP风险模型，该模型将改善对个体严重ROP风险的估计。这将通过对来自NEI （ROP数据库）和AHCPR（疾病严重程度SNAP数据库）支持的两个现有研究数据集的新合并数据库（SNAP ROP数据库）的二次分析来完成。将这两个数据集整合到一个非常丰富的研究质量数据库（SNAP ROP）中，为确定新的基于生理的ROP疾病严重程度决定因素以及开发用于预测严重（阈值）ROP的当代ROP风险模型提供了一个非凡的机会。我们将在类似衍生队列的独立新生儿人群中验证疾病严重程度ROP风险模型（SNAP ROP风险模型），并比较SNAP ROP风险模型与CRYO ROP风险模型在当代新生儿人群中预测阈值ROP（使用CRYO ROP阈值定义）的性能。

Retinopathy of prematurity (ROP) is a fibrovascular proliferative disease of the immature retina and an important cause of visual impairment and blindness in premature infants. The results of treating severe (threshold) ROP are not always successful. There is growing interest in preventing severe ROP, as is apparent from proposals to study earlier interventions in infants deemed to be at risk for threshold ROP. Accurate earlier identification of infants at greatest risk for severe ROP is essential to permit aggressive early intervention trials, as well as to avoid exposing lower-risk infants unnecessarily to experimental or clinical intervention. A series of ROP risk models were developed based on data from the Multicenter Trial of Cryotherapy for Retinopathy of Prematurity (CRYO ROP), but these reflect infants and care practices of 1986-1987. Development of ROP risk models on contemporary premature infants which include new predictive factors should improve earlier identification of infants at risk for severe ROP. We propose to test the hypothesis that a contemporary ROP risk model which incorporates measures of illness severity, sequential scoring of ophthalmic findings, gestational age, and predictive demographic variables will improve estimation of an individual's risk of severe ROP compared to the CRYO ROP risk model. This will be accomplished through secondary analyses of a newly merged database (SNAP ROP database) from two existing research data sets which are the result of support by the NEI (ROP database) and AHCPR (illness severity SNAP database). The integration of these two data sets into a singularly rich, research quality database (SNAP ROP) presents an extraordinary opportunity to identify new physiologic-based, illness severity determinants of ROP and to develop contemporary ROP risk models for predicting severe (threshold) ROP. We will validate the illness severity ROP risk models (SNAP ROP risk models) on an independent neonatal population similar to the derivation cohort, and compare the performance of the SNAP ROP risk model with the CRYO ROP risk model for predicting threshold ROP (using CRYO ROP threshold definition) on a contemporary, neonatal population.