Wilson Kumanan, Hawken Steven, Potter Beth K, Chakraborty Pranesh, Walker Mark, Ducharme Robin, Little Julian
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Institute for Clinical Evaluative Sciences, University of Ottawa, Ottawa, Ontario, Canada; School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada; Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada; Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada.
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Institute for Clinical Evaluative Sciences, University of Ottawa, Ottawa, Ontario, Canada; School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada; Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada.
Am J Obstet Gynecol. 2016 Apr;214(4):513.e1-513.e9. doi: 10.1016/j.ajog.2015.10.017. Epub 2015 Oct 28.
Identification of preterm births and accurate estimates of gestational age for newborn infants is vital to guide care. Unfortunately, in developing countries, it can be challenging to obtain estimates of gestational age. Routinely collected newborn infant screening metabolic analytes vary by gestational age and may be useful to estimate gestational age.
We sought to develop an algorithm that could estimate gestational age at birth that is based on the analytes that are obtained from newborn infant screening.
We conducted a population-based cross-sectional study of all live births in the province of Ontario that included 249,700 infants who were born between April 2007 and March 2009 and who underwent newborn infant screening. We used multivariable linear and logistic regression analyses to build a model to predict gestational age using newborn infant screening metabolite measurements and readily available physical characteristics data (birthweight and sex).
The final model of our metabolic gestational dating algorithm had an average deviation between observed and expected gestational age of approximately 1 week, which suggests excellent predictive ability (adjusted R-square of 0.65; root mean square error, 1.06 weeks). Two-thirds of the gestational ages that were predicted by our model were accurate within ±1 week of the actual gestational age. Our logistic regression model was able to discriminate extremely well between term and increasingly premature categories of infants (c-statistic, >0.99).
Metabolic gestational dating is accurate for the prediction of gestational age and could have value in low resource settings.
识别早产并准确估计新生儿的胎龄对于指导护理至关重要。不幸的是，在发展中国家，获取胎龄估计可能具有挑战性。常规收集的新生儿筛查代谢分析物会因胎龄而异，可能有助于估计胎龄。
我们试图开发一种算法，该算法可以根据从新生儿筛查中获得的分析物来估计出生时的胎龄。
我们对安大略省所有活产进行了一项基于人群的横断面研究，其中包括2007年4月至2009年3月期间出生且接受新生儿筛查的249,700名婴儿。我们使用多变量线性和逻辑回归分析来建立一个模型，使用新生儿筛查代谢物测量值和容易获得的身体特征数据（出生体重和性别）来预测胎龄。
我们的代谢胎龄算法的最终模型在观察到的和预期的胎龄之间的平均偏差约为1周，这表明具有出色的预测能力（调整后的R平方为0.65；均方根误差为1.06周）。我们模型预测的胎龄中有三分之二在实际胎龄的±1周内是准确的。我们的逻辑回归模型能够在足月儿和越来越早产的婴儿类别之间进行非常好的区分（c统计量，>0.99）。
代谢胎龄测定对于预测胎龄是准确的，并且在资源匮乏的环境中可能具有价值。
