Molecular and Clinical Sciences Research Institute, St George's, University of London and St George's University Hospitals NHS Foundation Trust, London, UK.
Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, London, UK.
BJOG. 2021 Jan;128(2):238-250. doi: 10.1111/1471-0528.16510. Epub 2020 Oct 13.
Stillbirth accounts for over 2 million deaths a year worldwide and rates remains stubbornly high. Multivariable prediction models may be key to individualised monitoring, intervention or early birth in pregnancy to prevent stillbirth.
To collate and evaluate systematic reviews of factors associated with stillbirth in order to identify variables relevant to prediction model development.
MEDLINE, Embase, DARE and Cochrane Library databases and reference lists were searched up to November 2019.
We included systematic reviews of association of individual variables with stillbirth without language restriction.
Abstract screening and data extraction were conducted in duplicate. Methodological quality was assessed using AMSTAR and QUIPS criteria. The evidence supporting association with each variable was graded.
The search identified 1198 citations. Sixty-nine systematic reviews reporting 64 variables were included. The most frequently reported were maternal age (n = 5), body mass index (n = 6) and maternal diabetes (n = 5). Uterine artery Doppler appeared to have the best performance of any single test for stillbirth. The strongest evidence of association was for nulliparity and pre-existing hypertension.
We have identified variables relevant to the development of prediction models for stillbirth. Age, parity and prior adverse pregnancy outcomes had a more convincing association than the best performing tests, which were PAPP-A, PlGF and UtAD. The evidence was limited by high heterogeneity and lack of data on intervention bias.
Review shows key predictors for use in developing models predicting stillbirth include age, prior pregnancy outcome and PAPP-A, PLGF and Uterine artery Doppler.
全球每年仍有超过 200 万人死于死产，且这一比例仍然居高不下。多变量预测模型可能是实现个体监测、干预或妊娠早期分娩以预防死产的关键。
整理和评估与死产相关的因素的系统评价，以确定与预测模型开发相关的变量。
检索了 MEDLINE、Embase、DARE 和 Cochrane 图书馆数据库以及参考文献列表，检索时间截至 2019 年 11 月。
我们纳入了关于个体变量与死产相关性的系统评价，无语言限制。
通过双盲法进行摘要筛选和数据提取。使用 AMSTAR 和 QUIPS 标准评估方法学质量。对每个变量的关联证据进行分级。
搜索共识别出 1198 条引文。纳入了 69 篇报告 64 个变量的系统评价。报告最多的变量是母亲年龄（n=5）、体重指数（n=6）和糖尿病（n=5）。子宫动脉多普勒似乎是预测死产的所有单一测试中性能最好的。与任何单一测试相比，关联最强的证据是原发性高血压和原发性高血压。
我们已经确定了与开发死产预测模型相关的变量。年龄、产次和既往不良妊娠结局与表现最佳的测试（PAPP-A、PlGF 和 UtAD）相比，具有更令人信服的关联。由于存在高度异质性和缺乏干预偏倚数据，证据有限。
综述表明，用于开发预测模型的关键预测因素包括年龄、既往妊娠结局以及 PAPP-A、PlGF 和子宫动脉多普勒。
