Obstetrics & Gynaecology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands,
Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Fetal Diagn Ther. 2021;48(2):103-111. doi: 10.1159/000512335. Epub 2021 Jan 5.
The aim of the study was to evaluate the performance of the first-trimester Fetal Medicine Foundation (FMF) screening algorithm, including maternal characteristics and medical history, blood pressure, pregnancy-associated plasma protein A and placenta growth factor, crown rump length, and uterine artery pulsatility index, for the prediction of preeclampsia in a high-risk population in the Netherlands.
This is a prospective cohort including nulliparous women and women with preeclampsia or intrauterine growth restriction in previous pregnancy. We screened patients at 11-14 weeks of gestation to calculate the risk for preeclampsia. The primary outcome was preeclampsia and gestational age at delivery. Performance of the model was evaluated by area under the receiver operating characteristic (ROC) curves (AUCs) and calibration graphs; based on the ROC curves, optimal predicted risk cutoff values for our study population were defined.
We analyzed 362 women, of whom 22 (6%) developed preeclampsia. The algorithm showed fair discriminative performance for preeclampsia <34 weeks (AUC 0.81; 95% CI 0.65-0.96) and moderate discriminative performance for both preeclampsia <37 weeks (AUC 0.71; 95% CI 0.51-0.90) and <42 weeks (AUC 0.71; 95% CI 0.61-0.81). Optimal cutoffs based on our study population for preeclampsia <34, <37, and <42 weeks were 1:250, 1:64, and 1:22, respectively. Calibration was poor.
Performance of the FMF preeclampsia algorithm was satisfactory to predict early and preterm preeclampsia and less satisfactory for term preeclampsia in a high-risk population. However, by addressing some of the limitations of the present study, the performance can potentially improve. This is essential before implementation is considered.
本研究旨在评估第一孕期胎儿医学基金会（FMF）筛查算法的效能，该算法包含母体特征和病史、血压、妊娠相关血浆蛋白 A 和胎盘生长因子、头臀长和子宫动脉搏动指数，以预测荷兰高危人群的子痫前期。
这是一项前瞻性队列研究，纳入了初产妇和既往妊娠有子痫前期或胎儿宫内生长受限的妇女。我们在 11-14 孕周对患者进行筛查，以计算子痫前期的风险。主要结局是子痫前期和分娩时的孕龄。通过接受者操作特征（ROC）曲线下面积（AUC）和校准图来评估模型的性能；根据 ROC 曲线，为我们的研究人群定义了最佳预测风险截断值。
我们分析了 362 名妇女，其中 22 名（6%）发生了子痫前期。该算法对<34 孕周的子痫前期具有较好的判别性能（AUC 0.81；95%CI 0.65-0.96），对<37 孕周和<42 孕周的子痫前期具有中度判别性能（AUC 分别为 0.71；95%CI 0.51-0.90 和 0.71；95%CI 0.61-0.81）。基于本研究人群，子痫前期<34 周、<37 周和<42 周的最佳截断值分别为 1:250、1:64 和 1:22。校准效果不佳。
在高危人群中，FMF 子痫前期算法预测早发型和早产子痫前期的性能令人满意，但预测足月子痫前期的性能较差。然而，通过解决本研究的一些局限性，该算法的性能可能会得到改善。在考虑实施之前，这是必要的。
