Suppr超能文献

孕药网:孕期用药对新生儿并发症的多方面影响

PregMedNet: Multifaceted Maternal Medication Impacts on Neonatal Complications.

作者信息

Kim Yeasul, Marić Ivana, Kashiwagi Chloe M, Han Lichy, Chung Philip, Reiss Jonathan D, Butcher Lindsay D, Caoili Kaitlin J, Berson Eloïse, Xue Lei, Espinosa Camilo, James Tomin, Shome Sayane, Xie Feng, Ghanem Marc, Seong David, Chang Alan L, Reincke S Momsen, Mataraso Samson, Shu Chi-Hung, De Francesco Davide, Becker Martin, Kumar Wasan M, Wong Ron, Gaudilliere Brice, Angst Martin S, Shaw Gary M, Bateman Brian T, Stevenson David K, Prince Lance S, Aghaeepour Nima

机构信息

Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine.

Department of Pediatrics, Stanford School of Medicine.

出版信息

medRxiv. 2025 Feb 14:2025.02.13.25322242. doi: 10.1101/2025.02.13.25322242.

Abstract

While medication intake is common among pregnant women, medication safety remains underexplored, leading to unclear guidance for patients and healthcare professionals. PregMedNet addresses this gap by providing a multifaceted maternal medication safety framework based on systematic analysis of 1.19 million mother-baby dyads from U.S. claims databases. A novel confounding adjustment pipeline was applied to systematically control confounders for multiple medication-disease pairs, robustly identifying both known and novel maternal medication effects. Notably, one of the newly discovered associations was experimentally validated, demonstrating the reliability of claims data and machine learning for perinatal medication safety studies. Additionally, potential biological mechanisms of newly identified associations were generated using a graph learning method. These findings highlight PregMedNet's value in promoting safer medication use during pregnancy and maternal-neonatal outcomes.

摘要

虽然孕妇服药很常见,但药物安全性仍未得到充分研究,导致患者和医疗保健专业人员缺乏明确的指导。PregMedNet通过基于对来自美国理赔数据库的119万母婴二元组进行系统分析,提供了一个多方面的孕产妇药物安全框架,解决了这一差距。一种新颖的混杂因素调整流程被应用于系统地控制多种药物-疾病对的混杂因素,有力地识别已知和新的孕产妇药物效应。值得注意的是,新发现的关联之一经过了实验验证,证明了理赔数据和机器学习在围产期药物安全性研究中的可靠性。此外,使用图学习方法生成了新发现关联的潜在生物学机制。这些发现凸显了PregMedNet在促进孕期更安全用药以及母婴结局方面的价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ce/11844599/cfc27a25d6dd/nihpp-2025.02.13.25322242v1-f0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验