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61 active trials for Preeclampsia

sFlt1/PlGF and Selective Labor Induction to Prevent Preeclampsia at Term

Preeclampsia (PE) affects ~5% of pregnancies. Although improved obstetrical care has significantly diminished associated maternal mortality, PE remains a leading cause of maternal morbidity and mortality in the world. Term PE accounts for 70% of all PE and a large proportion of maternal-fetal morbidity related with this condition. Prediction and prevention of term PE remains unsolved. Previously proposed approaches are based on combined screening and/or prophylactic drugs, but these policies are unlikely to be implementable in many world settings. Recent evidence shows that sFlt1-PlGF ratio at 35-37w predicts term PE with 80% detection rate. Likewise, recent studies demonstrate that induction of labor (IOL) from 37w is safe. The investigators hypothesize that a single-step universal screening for term PE based on sFlt1/PlGF ratio at 35-37w followed by IOL from 37w would reduce the prevalence of term PE without increasing cesarean section rates or adverse neonatal outcomes. The investigators propose a randomized clinical trial to evaluate the impact of a screening of term PE with sFlt-1/PlGF ratio in asymptomatic nulliparous women at 35-37w. Women will be assigned to revealed (sFlt-1/PlGF known to clinicians) versus concealed (unknown) arms. A cutoff of >90th centile will be used to define high risk of PE and offer IOL from 37w. If successful, the results of this trial will provide evidence to support a simple universal screening strategy reducing the prevalence of term PE, which could be applicable in most healthcare settings and have enormous implications on perinatal outcomes and public health policies worldwide.

Start: March 2021
Risk Prediction Model of Preeclampsia

Preeclampsia is the main cause of increased maternal and perinatal mortality during pregnancy. Preeclampsia is mainly manifested as hypertension, urine protein, or damage symptoms of other target organs after 20 weeks of pregnancy. In preeclampsia high-risk group, early intervention and prevention of aspirin treatment can reduce preeclampsia or reduce its complications. Some serological biomarkers, such as placental protein 13 and placental growth factor, are closely related to preeclampsia. The clinical manifestations of preeclampsia are diverse, and the biomarkers distribution of early and late preeclampsia is also different. Multivariate models will be the trend for the prediction of risk of preeclampsia. The deep learning model can train the algorithm layer by layer by unsupervised learning method, and then use the supervised back propagation algorithm for tuning. It has strong capability and flexibility, and has been successfully applied in medical fields, such as the diagnosis of skin cancer. In this study, maternal clinical data, routine laboratory indicators and biological markers in early pregnancy will be combined, and a deep learning method based on multiple models will be adopted to establish a risk prediction model for early preeclampsia, so as to improve the clinical ability for early diagnosis of preeclampsia. The deep learning method reduces the number of parameters by using spatial relative relation, which can improve the prediction ability of the model. Multi-model method is a less commonly used modeling method, and the models established by this method generally have better stability. This project combines the above two methods to establish a risk prediction model for preeclampsia, and the research is of great significance.

Start: February 2021