BackgroundEarly 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).ObjectivesTo perform a systematic review and meta‐analysis of first‐trimester prediction models for adverse pregnancy outcomes.Search StrategyThe PubMed database was searched until 6 June 2024.Selection CriteriaFirst‐trimester prediction models based on maternal characteristics were included. Articles reporting on prediction models that comprised biochemical or ultrasound markers were excluded.Data Collection and AnalysisTwo authors identified articles, extracted data and assessed risk of bias and applicability using PROBAST.Main resultsA 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.ConclusionsMultiple 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.

中文翻译：

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基于孕产妇特征的孕早期不良妊娠结局预测模型： 系统评价和荟萃分析


背景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） 和妊娠糖尿病 （GDM）。目的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。检索策略检索 PubMed 数据库至 2024 年 6 月 6 日。报告包含生化或超声标志物的预测模型的文章被排除在外。资料收集与分析两位作者使用 PROBAST 确定文章、提取资料并评估偏倚风险和适用性。主要结果共纳入 77 篇文章，包括 30 篇 PE 开发模型、15 篇 SGA 模型、11 篇 sPTB 模型和 35 篇 GDM 模型。这些模型在曲线下中位面积 （AUC） 方面的判别性能为 PE 模型为 0.75 [IQR 0.69-0.78]，未产妇 SGA 模型为 0.62 [0.60-0.71]，经产妇 SGA 模型为 0.74 [0.72-0.74]，未产妇 sPTB 模型为 0.65 [0.61-0.67]，经产妇 sPTB 模型为 0.71 [0.68-0.74]，GDM 模型为 0.71 [0.67-0.76]。在 40/91 （43.9%） 的模型中进行了内部验证。在 21/91 （23.1%） 的模型中报告了模型校准。在 45/91 （49.5%） 的模型中总共进行了 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。结论有多个孕早期预测模型可用，但几乎所有模型都存在高偏倚风险，并且通常不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。