Retinopathy of prematurity: A comprehensive risk analysis for prevention and prediction of disease
Retinopathy of prematurity: A comprehensive risk analysis for prevention and prediction of disease
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DOI：
10.1371/journal.pone.0171467
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发表时间：
2017-02-14
期刊：
影响因子：
3.7
通讯作者：
DeAngelis, Margaret M.
中科院分区：
文献类型：
--
作者：
Owen, Leah A.;Morrison, Margaux A.;DeAngelis, Margaret M.
研究背景早产儿视网膜病变(ROP)是早产儿的致盲性疾病。我们目前的筛查标准自成立以来一直保持不变，缺乏识别最高风险人群的能力。目的我们试图使用Logistic回归综合分析大量建议的母婴和环境ROP风险变量，以确定对ROP的发展和严重程度最具预测性的模型。我们进一步试图确定重大ROP风险变量之间的统计交互作用，这是以前在ROP领域从未做过的。我们假设，我们的综合分析将允许更好地识别与ROP疾病独立相关的风险变量。展望未来，这可能会改善从产前到出生后发育的婴儿风险分层，使预防变得更加可行。方法我们对2010-2015年间在一个新生儿重症监护病房进行ROP筛查的早产儿进行了回顾性队列分析。主要结果衡量标准是ROP的存在。次要结果是ROP需要治疗，严重ROP不明显符合当前的治疗标准。对57个建议的ROP风险变量进行了单变量、逐步回归和统计交互作用分析，以确定与每项结果测量显著相关的变量。在这个队列中，许多因素与我们的ROP结果指标有显著的个体相关性；然而，逐步回归分析发现，对总体ROP风险最具预测性的模型包括估计的胎龄、出生体重、是否需要任何手术以及母亲是否服用镁预防。该模型相应的曲线下面积(AUC)为0.8641，而传统的胎龄和出生体重模型预测ROP疾病的AUC值为0.8489。估计胎龄(周)、是否需要手术以及死亡概率增加或7天内中重度BPD是预测重度ROP发生的最佳指标。最后，最能预测1型ROP的模型包括估计的胎龄(周)和是否存在严重的慢性肺部疾病。在变量之间没有发现显著的统计学交互作用。结论我们的工作是独一无二的，因为我们报告了迄今为止在强健表型人群中提出的最大数量的ROP风险变量的综合分析。我们为ROP结果测量描述了新的风险模型，并使用以前未应用于ROP的统计建模来证明这些变量的独立性。这可能更好地考虑到个别婴儿的风险分层，更重要的是缓解未来的风险。
BackgroundRetinopathy of prematurity (ROP) is a blinding morbidity of preterm infants. Our current screening criteria have remained unchanged since their inception and lack the ability to identify those at greatest risk.ObjectivesWe sought to comprehensively analyze numerous proposed maternal, infant, and environmental ROP risk variables in a robustly phenotyped population using logistic regression to determine the most predictive model for ROP development and severity. We further sought to determine the statistical interaction between significant ROP risk variables, which has not previously been done in the field of ROP. We hypothesize that our comprehensive analysis will allow for better identification of risk variables that independently correlate with ROP disease. Going forward, this may allow for improved infant risk stratification along a time continuum from prenatal to postnatal development, making prevention more feasible.MethodsWe performed a retrospective cohort analysis of preterm infants referred for ROP screening in one neonatal intensive care unit from 2010-2015. The primary outcome measure was presence of ROP. Secondary outcome measures were ROP requiring treatment and severe ROP not clearly meeting current treatment criteria. Univariate, stepwise regression and statistical interaction analyses of 57 proposed ROP risk variables was performed to identify variables which were significantly associated with each outcome measure.ResultsWe identified 457 infants meeting our inclusion criteria. Within this cohort, numerous factors showed a significant individual association with our ROP outcome measures; however, stepwise regression analysis found the most predictive model for overall ROP risk included estimated gestational age, birth weight, the need for any surgery, and maternal magnesium prophylaxis. The corresponding Area Under the Curve (AUC) for this model was 0.8641, while the traditional model of gestational age and birth weight predicted ROP disease less well with an AUC of 0.8489. Development of severe ROP was best predicted by estimated gestational age (week), the need for any surgery and increased probability of death or moderate-severe BPD at 7 days. Finally, the model most predictive for type 1 ROP included estimated gestational age (week) and the presence of severe chronic lung disease. No significant statistical interaction was found between variables.ConclusionsOur work is unique as we report comprehensive analysis of the greatest number of proposed ROP risk variables to date in a robustly phenotyped population. We describe novel risk models for our ROP outcome measures and demonstrate independence of these variables using statistical modeling not previously applied to ROP. This may better allow for individual infant risk stratification and importantly mitigation of future risk.