Yang Xiaotong, Ballard Hailey K, Mahadevan Aditya D, Xu Ke, Garmire David G, Langen Elizabeth S, Lemas Dominick J, Garmire Lana X
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
Nat Commun. 2025 Apr 12;16(1):3496. doi: 10.1038/s41467-025-58437-7.
Preeclampsia is a major cause of maternal and perinatal mortality with no known cure. Delivery timing is critical to balancing maternal and fetal risks. We develop and externally validate PEDeliveryTime, a class of clinically informative models which resulted from deep-learning models, to predict the time from PE diagnosis to delivery using electronic health records. We build the models on 1533 PE cases from the University of Michigan and validate it on 2172 preeclampsia cases from the University of Florida. PEDeliveryTime full model contains only 12 features yet achieves high c-index of 0.79 and 0.74 on the Michigan and Florida data set respectively. For the early-onset preeclampsia subset, the full model reaches 0.76 and 0.67 on the Michigan and Florida test sets. Collectively, these models perform an early assessment of delivery urgency and might help to better prioritize medical resources.
先兆子痫是孕产妇和围产期死亡的主要原因，目前尚无已知的治愈方法。分娩时机对于平衡母婴风险至关重要。我们开发并外部验证了PEDeliveryTime，这是一类基于深度学习模型产生的具有临床信息的模型，用于使用电子健康记录预测从先兆子痫诊断到分娩的时间。我们基于密歇根大学的1533例先兆子痫病例构建模型，并在佛罗里达大学的2172例子痫前期病例上进行验证。PEDeliveryTime完整模型仅包含12个特征，但在密歇根和佛罗里达数据集上分别达到了0.79和0.74的高c指数。对于早发型子痫前期亚组，完整模型在密歇根和佛罗里达测试集上分别达到0.76和0.67。总体而言，这些模型对分娩紧迫性进行了早期评估，可能有助于更好地优化医疗资源的分配。
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