Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy
Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy
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早产先兆子痫风险模型：怀孕前检查血流动力学、生化和生物物理标志物
DOI：
10.1109/embc40787.2023.10340404
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
McGinnis, Ryan S.
中科院分区：
文献类型：
--
作者：
Loftness, Bryn C.;Bernstein, Ira;McBride, Carole A.;Cheney, Nick;McGinnis, Ellen W.;McGinnis, Ryan S.
先兆子痫（PE）是全球孕产妇和围产期死亡的主要原因，可导致计划外早产。预测早产或早发性PE的风险主要在受孕后进行研究，特别是在妊娠早期和中期。然而，有一个明显的临床优势，在确定个人在怀孕前的PE风险，当一个更广泛的预防干预措施。在这项工作中，我们利用机器学习技术来识别80名女性样本中潜在的妊娠前PE生物标志物，其中10名女性在随后的妊娠期间被诊断为早产先兆子痫。我们探讨了来自血流动力学，生物物理学和生化测量和几种建模方法的前瞻性生物标志物。使用随机梯度下降优化的支持向量机（SVM）在受试者交叉验证中具有最高的整体性能，ROC AUC和检测率分别高达.88和.70。性能最佳的模型利用生物物理学和血液动力学生物标志物。虽然是初步的，但这些结果表明，基于机器学习的方法有望在怀孕前检测出有早产PE风险的个体。这些努力可以为妊娠计划和护理提供信息，降低PE相关不良结果的风险。临床相关性-这项工作考虑开发和优化孕前生物标志物，以改善受孕前早产（早发）先兆子痫风险的识别。
Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore prospective biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.Clinical Relevance— This work considers the development and optimization of pre-pregnancy biomarkers for improving the identification of preterm (early-onset) preeclampsia risk prior to conception.
影响因子：
3.7
作者：
Jhee, Jong Hyun;Lee, SungHee;Park, Jung Tak
通讯作者：
Park, Jung Tak