The George Institute for Global Health, University of Oxford Le Gros Clark Building, South Parks Road, Oxford OX1 3QX, UK; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
The George Institute for Global Health, University of Oxford Le Gros Clark Building, South Parks Road, Oxford OX1 3QX, UK; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
Pregnancy Hypertens. 2019 Apr;16:48-66. doi: 10.1016/j.preghy.2019.03.005. Epub 2019 Mar 11.
Preeclampsia is a disease specific to pregnancy that can cause severe maternal and foetal morbidity and mortality. Early identification of women at higher risk for preeclampsia could potentially aid early prevention and treatment. Although a plethora of preeclampsia prediction models have been developed in recent years, individualised prediction of preeclampsia is rarely used in clinical practice.
The objective of this systematic review was to provide an overview of studies on preeclampsia prediction.
Relevant research papers were identified through a MEDLINE search up to 1 January 2017. Prognostic studies on the prediction of preeclampsia or preeclampsia-related disorders were included. Quality screening was performed with the Quality in Prognostic Studies (QUIPS) tool.
Sixty-eight prediction models from 70 studies with 425,125 participants were selected for further review. The number of participants varied and the gestational age at prediction varied widely across studies. The most frequently used predictors were medical history, body mass index, blood pressure, parity, uterine artery pulsatility index, and maternal age. The type of predictor (maternal characteristics, ultrasound markers and/or biomarkers) was not clearly associated with model discrimination. Few prediction studies were internally (4%) or externally (6%) validated.
To date, multiple and widely varying models for preeclampsia prediction have been developed, some yielding promising results. The high degree of between-study heterogeneity impedes selection of the best model, or an aggregated analysis of prognostic models. Before multivariable preeclampsia prediction can be clinically implemented universally, further validation and calibration of well-performing prediction models is needed.
子痫前期是一种妊娠特有的疾病，可导致严重的母婴发病率和死亡率。早期识别出患有子痫前期风险较高的女性，可能有助于早期预防和治疗。尽管近年来已经开发出许多子痫前期预测模型，但在临床实践中很少使用个体化的子痫前期预测。
本系统评价的目的是提供子痫前期预测研究的概述。
通过 MEDLINE 搜索，截至 2017 年 1 月 1 日，确定了相关的研究论文。纳入了关于子痫前期或子痫前期相关疾病预测的预后研究。使用预后研究质量（QUIPS）工具进行质量筛选。
从 70 项研究中选择了 68 个预测模型，共有 425,125 名参与者。参与者数量不一，预测时的孕周在各研究中差异很大。最常使用的预测因子是病史、体重指数、血压、产次、子宫动脉搏动指数和母亲年龄。预测因子的类型（母体特征、超声标志物和/或生物标志物）与模型区分度无明显相关性。很少有预测研究进行了内部（4%）或外部（6%）验证。
迄今为止，已经开发出多种广泛使用的子痫前期预测模型，其中一些取得了有希望的结果。研究间的高度异质性阻碍了最佳模型的选择，或对预后模型进行综合分析。在多变量子痫前期预测能够普遍应用于临床之前，需要进一步验证和校准表现良好的预测模型。
