ESTIMATING THE RISK OF SEVERE RETINOPATHY OF PREMATURITY

早产儿视网膜病变(ROP)是一种未成熟视网膜的纤维血管增生性疾病，是早产儿视力受损和致盲的重要原因。治疗重度(阈值)ROP的结果并不总是成功的。人们对预防严重ROP的兴趣与日俱增，这从对被认为存在ROP阈值风险的婴儿进行早期干预的建议中可见一斑。早期准确地识别严重ROP的最大风险婴儿对于允许积极的早期干预试验以及避免使低风险婴儿不必要地接受实验或临床干预是至关重要的。一系列ROP风险模型是基于冷冻治疗早产儿视网膜病变多中心试验(CRYO ROP)的数据开发的，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风险模型的开发将有助于早期识别有严重ROP风险的婴儿。我们建议测试这样一种假设，即与低温ROP风险模型相比，当代ROP风险模型结合了疾病严重性的测量、眼科发现的顺序评分、胎龄和预测性人口统计学变量，将改善对个人严重ROP风险的估计。这将通过对来自两个现有研究数据集的新合并的数据库(SNAP ROP数据库)进行二次分析来实现，这两个研究数据集是NEI(ROP数据库)和AHCPR(疾病严重性SNAP数据库)支持的结果。将这两个数据集整合到一个异常丰富的研究质量数据库(SNAP ROP)中，为识别新的基于生理的、疾病严重程度的ROP决定因素，并开发用于预测严重(阈值)ROP的当代ROP风险模型提供了一个非同寻常的机会。我们将在与派生队列相似的独立新生儿人群上验证疾病严重性ROP风险模型(SNAP ROP风险模型)，并比较SNAP ROP风险模型和低温ROP风险模型在预测当代新生儿人群阈值ROP(使用低温ROP阈值定义)方面的性能。

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.