Deep learning-based prognosis models accurately predict the time to delivery among preeclamptic pregnancies using electronic health record

Abstract

Structured AbstractBackgroundPreeclampsia (PE) is one of the leading factors in maternal and perinatal mortality and morbidity worldwide. Delivery timing is key to balancing the risk between severe maternal and neonatal morbidities in pregnancies complicated by PE.MethodIn this study, we constructed and validated first-of-their-kind deep learning models that can forecast the time to delivery among patients with PE using electronic health records (EHR) data. The discovery cohort consisted of 1,533 preeclamptic pregnancies, including 374 cases of early-onset preeclampsia (EOPE), that were delivered at University of Michigan Health System (UM) between 2015 and 2021. The validation cohort contained 2,172 preeclamptic pregnancies (including 547 EOPE) from University of Florida Health System (UF) in the same period. Using Cox-nnet, a neural network-based prognosis prediction algorithm, we built baseline models of all PE patients and of the subset of EOPE patients, using 47 features on demographics, medical history, comorbidities, the severity of PE, and gestational age of initial PE diagnosis. We also built full models using 62 features, combining those in baseline models and additional features on lab tests and vital signs, on the same PE patients and EOPE subset. The models were re-trained and re-validated using reduced sets of the most important features, to improve their interpretability and clinical applicability.FindingsThe 7-feature baseline models on all PE patients reached C-indices of 0·73, 0·74 and 0·73 on UM training, hold-out testing and UF validation dataset respectively, whereas the 12-feature full model had improved C-indices of 0·78, 0·79 and 0·74 on the same datasets. For the EOPE cases, the 6-feature baseline model achieved C-indices of 0·67, 0·68 and 0·63 on the training, hold-out testing and UF validation dataset respectively, while its 13-feature full model counterpart reached C-indices of 0·74, 0·76 and 0·67 in the same datasets. Besides confirming the prognostic importance of gestational age at the time of diagnosis and of sPE status, all four models identified parity and PE in prior pregnancies as important features, which are not in the current guidelines for PE delivery timing. Laboratory results and vital signs such as platelet count, the standard deviation of respiratory rate within a 5-day observation window, and mean diastolic blood pressure are critical to increase the accuracy of predicting time to delivery, in addition to testing aspartate aminotransferase and creatinine levels. For EOPE time to delivery prediction, comorbidities such as pulmonary circulation disorders and coagulopathy as defined in Elixhauser Comorbidity Index are important to consider.InterpretationWe set up a user-friendly web interface to allow personalized PE time to delivery prediction. The app is available athttp://garmiregroup.org/PE-prognosis-predictor/appThese actionable models may help providers to plan antepartum care in these pregnancies and significantly improve the management/clinical outcomes of pregnancies affected by PE.FundingThis study is funded by the National Institutes of HealthResearch in contextEvidence before this studyDetermining the optimal delivery time is essential in preeclampsia management to balance the risk of maternal and neonatal morbidities. Current clinical guidelines for delivery timing in preeclampsia, according to the American College of Obstetricians and Gynecologists (ACOG), mainly depend on the gestational age at diagnosis and the severity of PE. However, the current knowledge doesn’t provide a quantitative prediction of patients’ risk of delivery, nor does it discuss the effect of some important phenotypic factors (eg. patients’ demographics, lifestyles and comorbidities) on delivery time. Rather, according to a systematic review published in 2021, 18 prior studies predicted the timing of delivery for preeclampsia using biomarkers, which are yet to be implemented in routine checkups in pregnancy. On the other hand, EHR data are routinely collected but often overlooked information, with huge potential to predict challenging time to delivery problems such as those in PE.Added value of this studyTo our knowledge, these are the first deep-learning-based time to delivery prediction models for PE and EOPE patients using routine clinical and demographic variables. We enlist the quantitative values of critical EHR features informative of delivery time among PE patients, many of which are newly reported clinical features. We disseminate these models by the web tool “PE time to delivery Predictor”.Implications of all the available evidenceAll models are externally validated with a large EHR dataset from the University of Florida Health System. Adopting these models may provide clinicians and patients with valuable management plans to predict and prepare for the best delivery times of pregnancies complicated by PE, especially for EOPE cases in which consequences of early delivery are more significant. Further prospective investigation of these models’ performance is necessary to provide feedback and potential improvement of this model.