Predicting Intensive Care Unit Admission in COVID-19-Infected Pregnant Women Using Machine Learning.

: The rapid onset of COVID-19 placed immense strain on many already overstretched healthcare systems. The unique physiological changes in pregnancy, amplified by the complex effects of COVID-19 in pregnant women, rendered prioritization of infected expectant mothers more challenging. This work aims to use state-of-the-art machine learning techniques to predict whether a COVID-19-infected pregnant woman will be admitted to ICU (Intensive Care Unit).

Characterization of COVID-19 infected pregnant women with ICU admission and the risk of preterm: A cluster analysis.

Background: The unique physiological changes during pregnancy present challenges in understanding the full scope and effects of COVID-19 on pregnant women, adding complexity to their medical management. Given the significant changes in the immune, circulatory, respiratory, and hormonal systems during the progression of the pregnancy, and the specific factors with higher risk of COVID-19, like metabolic, vascular, and endothelial factors, typically also associated with maternal and neonatal unfavorable outcomes, the full understanding of how COVID-19 affects pregnant women is not clarified yet.

Methods: In this study, anonymous data from medical records of pregnant women with lab-confirmed COVID-19 in Astana, Kazakhstan from May 1, 2021, to July 14, 2021, were collected retrospectively.

Placental small extracellular vesicles from normal pregnancy and gestational diabetes increase insulin gene transcription and content in β cells.

Insulin secretion increases progressively during pregnancy to maintain normal maternal blood glucose levels. The placenta plays a crucial role in this process by releasing hormones and extracellular vesicles into the maternal circulation, which drive significant changes in pregnancy physiology. Placental extracellular vesicles, which are detectable in the plasma of pregnant women, have been shown to signal peripheral tissues and contribute to pregnancy-related conditions.