A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort.

Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed racial bias-free machine learning models that predict the onset of preeclampsia with severe features or eclampsia at discrete time points in a nulliparous pregnant study cohort.

Early prediction of preeclampsia using the first trimester vaginal microbiome.

Preeclampsia is a severe obstetrical syndrome which contributes to 10-15% of all maternal deaths. Although the mechanisms underlying systemic damage in preeclampsia-such as impaired placentation, endothelial dysfunction, and immune dysregulation-are well studied, the initial triggers of the condition remain largely unknown. Furthermore, although the pathogenesis of preeclampsia begins early in pregnancy, there are no early diagnostics for this life-threatening syndrome, which is typically diagnosed much later, after systemic damage has already manifested.

Dilapan-S versus Standard Methods for Cervical Ripening in Term Pregnancies: An Individual Patient Data Meta-Analysis.

Objectives: Dilapan-S is a cervical ripening agent approved by the FDA that has been found to be just as effective as other agents and can be utilized for outpatient ripening. No large-scale studies have been conducted to compare cesarean delivery rates between Dilapan-S and other ripening methods. Our objective was to combine these trials to compare cesarean delivery rates for Dilapan-S with other cervical ripening methods, overall and in sub-groups.

The association between perinatal depressive symptoms and child neurodevelopment.

Background: Perinatal depression has been suggested to adversely impact child neurodevelopment. However, the complexity of the early childhood environment challenges conclusive findings.

Objective: To evaluate whether there is an association between perinatal depressive symptoms and child intelligence quotient (IQ) at 5 years of age.