Flatley Christopher, Gibbons Kristen, Hurst Cameron, Flenady Vicki, Kumar Sailesh
Mater Research, Mater Research Institute/University of Queensland, Brisbane, Queensland, Australia.
Mater Medical Research Institute, South Brisbane, Queensland, Australia.
BMJ Paediatr Open. 2019 Mar 15;3(1):e000424. doi: 10.1136/bmjpo-2018-000424. eCollection 2019.
The aim of this study was to develop a predictive model using maternal, intrapartum and ultrasound variables for a composite of severe adverse neonatal outcomes (SANO) in term infants.
Prospectively collected observational study. Mixed effects generalised linear models were used for modelling. Internal validation was performed using the K-fold cross-validation technique.
This was a study of women that birthed at the Mater Mother's Hospital in Brisbane, Australia between January 2010 and April 2017.
We included all term, non-anomalous singleton pregnancies that had an ultrasound performed between 36 and 38 weeks gestation and had recordings for the umbilical artery pulsatility index, middle cerebral artery pulsatility index and the estimated fetal weight (EFW).
The components of the SANO were: severe acidosis arterial, admission to the neonatal intensive care unit, Apgar score of ≤3 at 5 min or perinatal death.
There were 5439 women identified during the study period that met the inclusion criteria, with 11.7% of this cohort having SANO. The final generalised linear mixed model consisted of the following variables: maternal ethnicity, socioeconomic score, nulliparity, induction of labour, method of birth and z-scores for EFW and cerebroplacental ratio. The final model had an area under the receiver operating characteristic curve of 0.71.
The results of this study demonstrate it is possible to predict infants that are at risk of SANO at term with moderate accuracy using a combination of maternal, intrapartum and ultrasound variables. Cross-validation analysis suggests a high calibration of the model.
本研究旨在利用母亲、分娩期及超声变量建立一个预测足月儿严重不良新生儿结局（SANO）综合指标的预测模型。
前瞻性收集的观察性研究。采用混合效应广义线性模型进行建模。使用K折交叉验证技术进行内部验证。
本研究对象为2010年1月至2017年4月在澳大利亚布里斯班 Mater Mother's 医院分娩的女性。
我们纳入了所有足月、非畸形单胎妊娠，这些妊娠在孕36至38周期间进行了超声检查，并记录了脐动脉搏动指数、大脑中动脉搏动指数和估计胎儿体重（EFW）。
SANO的组成部分包括：严重动脉酸中毒、入住新生儿重症监护病房、5分钟时Apgar评分≤3或围产期死亡。
研究期间共识别出5439名符合纳入标准的女性，其中11.7%的队列发生了SANO。最终的广义线性混合模型由以下变量组成：母亲种族、社会经济评分、初产、引产、分娩方式以及EFW和脑胎盘比率的z评分。最终模型的受试者工作特征曲线下面积为0.71。
本研究结果表明，结合母亲、分娩期及超声变量，可以以中等准确度预测足月时存在SANO风险的婴儿。交叉验证分析表明该模型具有较高的校准度。
