Predicting gestational age using neonatal metabolic markers.
Predicting gestational age using neonatal metabolic markers.
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DOI：
10.1016/j.ajog.2015.11.028
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发表时间：
2016-04
影响因子：
9.8
通讯作者：
Dagle JM
中科院分区：
文献类型：
--
作者：
Ryckman KK;Berberich SL;Dagle JM
准确的胎龄估计对于新生儿的临床护理决策以及围产期健康研究至关重要。虽然产前超声测年是估计胎龄最准确的方法之一，但并非在所有情况下都可行。确定新的准确的出生胎龄估计方法是很重要的，特别是在没有常规超声测年的地区监测早产率。我们假设，在没有产前超声技术的情况下，通过常规新生儿筛查捕获的代谢和内分泌标志物可以改善胎龄估计。这是对2004年至2009年爱荷华州新生儿筛查项目收集的230,013例新生儿代谢筛查记录的回顾性分析。数据随机分为模型构建数据集（n = 153,342）和模型测试数据集（n = 76,671）。我们以胎龄（周）作为结果测量，进行多元线性回归建模。我们检测了44种代谢物，包括氨基酸和脂肪酸代谢的生物标志物、促甲状腺激素和17-羟孕酮。决定系数（R2）和均方根误差用于评估模型构建数据集中的模型，然后在模型测试数据集中对模型进行测试。新生儿代谢回归模型由88个参数组成，包括截距、37个代谢物指标、29个代谢物指标的平方和21个代谢物指标的立方。该模型解释了模型测试数据集中52.8%的胎龄变化。78%的个体在妊娠1周内预测胎龄，95%的个体在妊娠2周内预测胎龄。该模型在区分早产（<37周）和足月（≥37周）的曲线下面积为0.899（95%可信区间0.895 - 0.903）。在小胎龄出生的婴儿中，新生儿代谢模型预测的胎龄与记录的胎龄之间的平均差异为1.5周。相比之下，仅包括新生儿体重的模型预测的胎龄与记录的胎龄之间的平均差异为1.9周。当仅使用包括新生儿体重的模型时，小于37周妊娠的婴儿亚群中早产的估计患病率为18.79%，是实际患病率9.20%的两倍多。新生儿代谢模型低估了6.94%的早产发生率，但与仅考虑新生儿体重的模型相比，更接近基于记录胎龄的早产发生率。新生儿代谢谱，作为衍生的常规新生儿筛查标志物，是一个准确的方法来估计胎龄。在小胎龄新生儿中，新生儿代谢模型预测胎龄比单独预测新生儿体重更准确。在没有产前超声测量或新生儿体重的情况下，新生儿代谢筛查是一种潜在的有效的早产儿人口监测方法。
Accurate gestational age estimation is extremely important for clinical care decisions of the newborn as well as for perinatal health research. Although prenatal ultrasound dating is one of the most accurate methods for estimating gestational age, it is not feasible in all settings. Identifying novel and accurate methods for gestational age estimation at birth is important, particularly for surveillance of preterm birth rates in areas without routine ultrasound dating. We hypothesized that metabolic and endocrine markers captured by routine newborn screening could improve gestational age estimation in the absence of prenatal ultrasound technology. This is a retrospective analysis of 230,013 newborn metabolic screening records collected by the Iowa Newborn Screening Program between 2004 and 2009. The data were randomly split into a model-building dataset (n = 153,342) and a model-testing dataset (n = 76,671). We performed multiple linear regression modeling with gestational age, in weeks, as the outcome measure. We examined 44 metabolites, including biomarkers of amino acid and fatty acid metabolism, thyroid-stimulating hormone, and 17-hydroxyprogesterone. The coefficient of determination (R2) and the root-mean-square error were used to evaluate models in the model-building dataset that were then tested in the model-testing dataset. The newborn metabolic regression model consisted of 88 parameters, including the intercept, 37 metabolite measures, 29 squared metabolite measures, and 21 cubed metabolite measures. This model explained 52.8% of the variation in gestational age in the model-testing dataset. Gestational age was predicted within 1 week for 78% of the individuals and within 2 weeks of gestation for 95% of the individuals. This model yielded an area under the curve of 0.899 (95% confidence interval 0.895−0.903) in differentiating those born preterm (<37 weeks) from those born term (≥37 weeks). In the subset of infants born small-for-gestational age, the average difference between gestational ages predicted by the newborn metabolic model and the recorded gestational age was 1.5 weeks. In contrast, the average difference between gestational ages predicted by the model including only newborn weight and the recorded gestational age was 1.9 weeks. The estimated prevalence of preterm birth <37 weeks’ gestation in the subset of infants that were small for gestational age was 18.79% when the model including only newborn weight was used, over twice that of the actual prevalence of 9.20%. The newborn metabolic model underestimated the preterm birth prevalence at 6.94% but was closer to the prevalence based on the recorded gestational age than the model including only newborn weight. The newborn metabolic profile, as derived from routine newborn screening markers, is an accurate method for estimating gestational age. In small-for-gestational age neonates, the newborn metabolic model predicts gestational age to a better degree than newborn weight alone. Newborn metabolic screening is a potentially effective method for population surveillance of preterm birth in the absence of prenatal ultrasound measurements or newborn weight.