Sylvester Karl G, Hao Shiying, You Jin, Zheng Le, Tian Lu, Yao Xiaoming, Mo Lihong, Ladella Subhashini, Wong Ronald J, Shaw Gary M, Stevenson David K, Cohen Harvey J, Whitin John C, McElhinney Doff B, Ling Xuefeng B
Department of Surgery, Stanford University School of Medicine, Stanford, California, USA
Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California, USA.
BMJ Open. 2020 Dec 2;10(12):e040647. doi: 10.1136/bmjopen-2020-040647.
The aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision.
A retrospective cohort study.
Two medical centres from the USA.
Thirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms.
Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry.
A model to determine gestational age was developed (R=0.98) and validated (R=0.81). 66.7% of the estimates fell within ±1 week of ultrasound results during model validation. Significant disruptions from full-term pregnancy metabolic patterns were observed in preterm pregnancies (R=-0.68). A separate algorithm to predict preterm birth was developed using a set of 10 metabolic pathways that resulted in an area under the curve of 0.96 and 0.92, a sensitivity of 0.88 and 0.86, and a specificity of 0.96 and 0.92 during development and validation testing, respectively.
In this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.
本研究的目的是开发一种单一血液检测方法,通过测量血清代谢物来确定孕周并估计早产风险。我们假设,对整个孕期血清分析物进行系列代谢建模可用于精确描述胎儿孕周并预测早产。
一项回顾性队列研究。
美国的两个医疗中心。
斯坦福大学招募的36名患者(20名足月产,16名早产)用于开发孕周和早产风险算法,阿拉巴马大学招募的22名患者(9名足月产,13名早产)用于验证算法。
在整个孕期连续采集孕妇血液。使用质谱法生成代谢数据集。
开发了一个确定孕周的模型(R = 0.98)并进行了验证(R = 0.81)。在模型验证期间,66.7%的估计值落在超声结果的±1周范围内。在早产妊娠中观察到与足月妊娠代谢模式的显著差异(R = -0.68)。使用一组10条代谢途径开发了一种单独的预测早产算法,在开发和验证测试期间,其曲线下面积分别为0.96和0.92,灵敏度分别为0.88和0.86,特异性分别为0.96和0.92。
在本研究中,代谢谱分析被用于开发和测试一个模型,以确定足月妊娠进展期间的孕周,并确定早产风险。通过更多的患者验证研究,这些算法可用于识别有风险的妊娠,促使临床护理做出改变,并深入了解早产的病理生理学的生物学机制。基于代谢途径的妊娠建模是一种用于研究和临床应用开发的新方法。