Kenya heel prick and cord blood sample data

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. 

Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. 

Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick sample...

本研究前期依托加拿大安大略省（Ontario）的数据，开发了基于机器学习、整合新生儿筛查代谢物的胎龄（gestational age, GA）估计算法。本研究旨在于肯尼亚西亚亚县（Siaya county）的新生儿人群中评估该类算法的应用价值。
研究人员从肯尼亚新生儿中采集脐带血与足跟血样本，并由加拿大渥太华的安大略省新生儿筛查中心（Newborn Screening Ontario）完成代谢分析。本研究前期以安大略省数据为基础，采用弹性网络（ELASTIC NET）正则化的多元线性回归方法构建产后胎龄估测模型。通过将模型应用于肯尼亚采集的数据集，并将模型输出的胎龄估测值与早孕超声的参考估测值进行对比，以此评估模型的性能表现。
本次研究共采集1039名肯尼亚新生儿的足跟血样本，其中8.9%为早产儿，8.5%为小于胎龄（small for GA）儿；同时还收集了1012名新生儿的脐带血样本。足跟血样本数据集的分析结果……