van Eekhout Jacintha C A, Becking Ellis C, Scheffer Peter G, Koutsoliakos Ioannis, Bax Caroline J, Henneman Lidewij, Bekker Mireille N, Schuit Ewoud
Department of Genetics, Erasmus Medical Centre, Rotterdam, The Netherlands.
Department of Obstetrics and Gynecology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
BJOG. 2025 Feb;132(3):243-265. doi: 10.1111/1471-0528.17983. Epub 2024 Oct 24.
Early risk stratification can facilitate timely interventions for adverse pregnancy outcomes, including preeclampsia (PE), small-for-gestational-age neonates (SGA), spontaneous preterm birth (sPTB) and gestational diabetes mellitus (GDM).
To perform a systematic review and meta-analysis of first-trimester prediction models for adverse pregnancy outcomes.
The PubMed database was searched until 6 June 2024.
First-trimester prediction models based on maternal characteristics were included. Articles reporting on prediction models that comprised biochemical or ultrasound markers were excluded.
Two authors identified articles, extracted data and assessed risk of bias and applicability using PROBAST.
A total of 77 articles were included, comprising 30 developed models for PE, 15 for SGA, 11 for sPTB and 35 for GDM. Discriminatory performance in terms of median area under the curve (AUC) of these models was 0.75 [IQR 0.69-0.78] for PE models, 0.62 [0.60-0.71] for SGA models of nulliparous women, 0.74 [0.72-0.74] for SGA models of multiparous women, 0.65 [0.61-0.67] for sPTB models of nulliparous women, 0.71 [0.68-0.74] for sPTB models of multiparous women and 0.71 [0.67-0.76] for GDM models. Internal validation was performed in 40/91 (43.9%) of the models. Model calibration was reported in 21/91 (23.1%) models. External validation was performed a total of 96 times in 45/91 (49.5%) of the models. High risk of bias was observed in 94.5% of the developed models and in 58.3% of the external validations.
Multiple first-trimester prediction models are available, but almost all suffer from high risk of bias, and internal and external validations were often not performed. Hence, methodological quality improvement and assessment of the clinical utility are needed.
早期风险分层有助于及时干预不良妊娠结局，包括子痫前期（PE）、小于胎龄儿（SGA）、自发性早产（sPTB）和妊娠期糖尿病（GDM）。
对孕早期不良妊娠结局预测模型进行系统评价和荟萃分析。
检索PubMed数据库至2024年6月6日。
纳入基于孕妇特征的孕早期预测模型。排除报告包含生化或超声标志物的预测模型的文章。
两名作者识别文章、提取数据，并使用PROBAST评估偏倚风险和适用性。
共纳入77篇文章，包括30个已开发的PE模型、15个SGA模型、11个sPTB模型和35个GDM模型。这些模型的曲线下面积（AUC）中位数的判别性能在PE模型中为0.75[四分位间距0.69 - 0.78]，初产妇SGA模型中为0.62[0.60 - 0.71]，经产妇SGA模型中为0.74[0.72 - 0.74]，初产妇sPTB模型中为0.65[0.61 - 0.67]，经产妇sPTB模型中为0.71[0.68 - 0.74]，GDM模型中为0.71[0.67 - 0.76]。40/91（43.9%）的模型进行了内部验证。21/91（23.1%）的模型报告了模型校准情况。45/91（49.5%）的模型共进行了96次外部验证。在94.5%的已开发模型和58.3%的外部验证中观察到高偏倚风险。
有多种孕早期预测模型可用，但几乎所有模型都存在高偏倚风险且常未进行内部和外部验证。因此，需要改进方法学质量并评估临床效用。
