Validation of the first-trimester machine learning model for predicting pre-eclampsia in an Asian population.

Objectives: To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first-trimester screening for pre-eclampsia in a large Asian population.

Methods: This was a secondary analysis of a multicenter prospective cohort study in 10 935 participants with singleton pregnancies attending for routine pregnancy care at 11-13 weeks of gestation in seven regions in Asia between December 2016 and June 2018. We applied the AI+ML model for the first-trimester prediction of preterm pre-eclampsia (<37 weeks), term pre-eclampsia (≥37 weeks), and any pre-eclampsia, which was derived and tested in a cohort of pregnant participants in the UK (Model 1).

A critical evaluation of the external and internal maneuvers for resolution of shoulder dystocia.

In the management of shoulder dystocia, it is often recommended to start with external maneuvers, such as the McRoberts maneuver and suprapubic pressure, followed by internal maneuvers including rotation and posterior arm delivery. However, this sequence is not based on scientific evidence of its success rates, the technical simplicity, or the related complication rates. Hence, this review critically evaluates the success rate, technique, and safety of different maneuvers.

Dynamic changes in maternal immune biomarkers during labor in nulliparous vs multiparous women.

Background: Immunomodulation is observed in human parturition. However, data from longitudinal studies for the prelabor phase and the active phase of labor are lacking, and no study had compared the immune responses during labor between nulliparous and multiparous women.

Objective: This study aimed to investigate the temporal changes of immune biomarkers in maternal blood from the prelabor phase to the latent and active phases of labor and to compare the dynamic changes between nulliparous and multiparous women.