以色列威兹曼科学研究所Ran D. Balicer和拉宾医学中心Arnon Wiznitzer研究组合作，提出了基于全国电子健康记录的妊娠期糖尿病（GDM）预测模型。相关论文2020年1月13日发表在《自然—医学》上。

他们使用机器学习方法根据以色列588,622例妊娠的回顾性数据预测了GDM，这些数据具有全面的电子健康记录。模型预测，即使在妊娠开始时（接受者工作曲线下的面积（auROC）= 0.85），GDM仍具有很高的准确性，大大优于基线风险评分（auROC = 0.68）。他们使用以色列人口最多的城市耶路撒冷的未来验证集和地理验证集验证了他们的结果，从而模拟了实际性能。在验证他们的模型时，他们发现了以前未报告的风险因素，包括以前的妊娠葡萄糖激发试验的结果。最后，他们仅根据患者可以回答的九个问题设计了一个更简单的模型，而准确性仅适度降低（auROC = 0.80）。总体而言，他们的模型可能允许对高危妇女进行早期干预，以及一种成本有效的筛查方法，该方法可以通过识别低危妇女而避免进行糖耐受测试。需要未来的前瞻性研究和其他人群的研究来评估该模型在现实世界中的临床效用。

据了解， GDM对母亲和后代造成短期和长期并发症的风险增加。GDM通常在妊娠24-28周时被诊断出，但较早发现是可取的，因为这可以预防或显著降低不良妊娠预后的风险。

附：英文原文

Title: Prediction of gestational diabetes based on nationwide electronic health records

Author: Nitzan Shalom Artzi, Smadar Shilo, Eran Hadar, Hagai Rossman, Shiri Barbash-Hazan, Avi Ben-Haroush, Ran D. Balicer

Issue&Volume: 2020/01/13

Abstract: Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring14. GDM is typically diagnosed at 2428weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes5,6. Here we used a machine-learning approach to predict GDM on retrospective data of 588,622pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC)=0.85), substantially outperforming a baseline risk score (auROC=0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC=0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model. Leveraging the availability of nationwide electronic health records from over 500,000pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy.

DOI: 10.1038/s41591-019-0724-8

Source:https://www.nature.com/articles/s41591-019-0724-8