Prediction of gestational diabetes based on nationwide electronic health records

被引:0
作者
Nitzan Shalom Artzi
Smadar Shilo
Eran Hadar
Hagai Rossman
Shiri Barbash-Hazan
Avi Ben-Haroush
Ran D. Balicer
Becca Feldman
Arnon Wiznitzer
Eran Segal
机构
[1] Weizmann Institute of Science,Department of Computer Science and Applied Mathematics
[2] Weizmann Institute of Science,Department of Molecular Cell Biology
[3] Pediatric Diabetes Unit,Sackler Faculty of Medicine
[4] Ruth Rappaport Children’s Hospital,Department of Public Health, Faculty of Health Sciences
[5] Rambam Healthcare Campus,undefined
[6] Helen Schneider Hospital for Women,undefined
[7] Rabin Medical Center,undefined
[8] Tel Aviv University,undefined
[9] Clalit Research Institute,undefined
[10] Clalit Health Services,undefined
[11] Ben-Gurion University,undefined
来源
Nature Medicine | 2020年 / 26卷
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摘要
Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring1–4. GDM is typically diagnosed at 24–28 weeks 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 588,622 pregnancies 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.
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页码:71 / 76
页数:5
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