Clinical Research Branch, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City, Mexico.
Obstetrics and Gynecology Department, The American British Cowdray Medical Center, Mexico City, Mexico.
Ultrasound Obstet Gynecol. 2024 Mar;63(3):350-357. doi: 10.1002/uog.27510.
Pre-eclampsia (PE) is a serious complication of pregnancy associated with maternal and fetal morbidity and mortality. As current prediction models have limitations and may not be applicable in resource-limited settings, we aimed to develop a machine-learning (ML) algorithm that offers a potential solution for developing accurate and efficient first-trimester prediction of PE.
We conducted a prospective cohort study in Mexico City, Mexico to develop a first-trimester prediction model for preterm PE (pPE) using ML. Maternal characteristics and locally derived multiples of the median (MoM) values for mean arterial pressure, uterine artery pulsatility index and serum placental growth factor were used for variable selection. The dataset was split into training, validation and test sets. An elastic-net method was employed for predictor selection, and model performance was evaluated using area under the receiver-operating-characteristics curve (AUC) and detection rates (DR) at 10% false-positive rates (FPR).
The final analysis included 3050 pregnant women, of whom 124 (4.07%) developed PE. The ML model showed good performance, with AUCs of 0.897, 0.963 and 0.778 for pPE, early-onset PE (ePE) and any type of PE (all-PE), respectively. The DRs at 10% FPR were 76.5%, 88.2% and 50.1% for pPE, ePE and all-PE, respectively.
Our ML model demonstrated high accuracy in predicting pPE and ePE using first-trimester maternal characteristics and locally derived MoM. The model may provide an efficient and accessible tool for early prediction of PE, facilitating timely intervention and improved maternal and fetal outcome. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
子痫前期（PE）是一种严重的妊娠并发症，与母婴发病率和死亡率有关。由于当前的预测模型存在局限性，并且可能不适用于资源有限的环境，我们旨在开发一种机器学习（ML）算法，为开发准确、高效的早期预测 PE 提供潜在解决方案。
我们在墨西哥城进行了一项前瞻性队列研究，使用 ML 开发了一种早期预测早产 PE（pPE）的模型。使用母体特征和局部中位数倍数（MoM）值来选择平均动脉压、子宫动脉搏动指数和血清胎盘生长因子作为变量。数据集分为训练集、验证集和测试集。使用弹性网络方法进行预测因子选择，使用接受者操作特征曲线下的面积（AUC）和 10%假阳性率（FPR）时的检测率（DR）来评估模型性能。
最终分析包括 3050 名孕妇，其中 124 名（4.07%）发生了 PE。ML 模型表现出良好的性能，pPE、早发型 PE（ePE）和任何类型的 PE（all-PE）的 AUC 分别为 0.897、0.963 和 0.778。10% FPR 时的 DR 分别为 pPE、ePE 和 all-PE 的 76.5%、88.2%和 50.1%。
我们的 ML 模型使用早期妊娠母体特征和局部 MoM 对 pPE 和 ePE 的预测具有很高的准确性。该模型可能为 PE 的早期预测提供一种高效、可及的工具，有助于及时干预和改善母婴结局。© 2023 作者。《超声医学杂志》由约翰威立父子出版公司代表国际妇产科超声学会出版。
