Predicting preterm birth using electronic medical records from multiple prenatal visits.

This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation on data from 8,830 women in the Nulliparous Pregnancy Outcomes Study: New Mothers-to-Be (nuMoM2b) dataset at three prenatal visits: - , - , and - weeks of gestational age (GA). The models' performance, assessed using Area Under the Curve (AUC), sensitivity, specificity, and accuracy, consistently improved with the incorporation of data from later prenatal visits.

Two-dimensional fetal speckle tracking; a learning curve study for offline strain analysis.

Objectives: Two-dimensional speckle tracking (2D-STE) strain analysis holds promise for assessing fetal cardiac function. Understand the learning curve before introducing 2D-STE into obstetrics is crucial. This study examined the learning curve for offline analysis of fetal left (LV) and right ventricular (RV) global longitudinal strain (GLS) using 2D-STE.

From intra- to extra-uterine: early phase design of a transfer to extra-uterine life support through medical simulation.

Introduction: Extra-uterine life support technology could provide a more physiologic alternative for the treatment of extremely premature infants, as it allows further fetal growth and development ex utero. Animal studies have been carried out which involved placing fetuses in a liquid-filled incubator, with oxygen supplied through an oxygenator connected to the umbilical vessels. Hence, by delaying lung exposure to air, further lung development and maturation can take place.

Feasibility and reliability of fetal two dimensional speckle tracking echocardiography at 16 weeks gestational age: A pilot study.

Background: Fetal two-dimensional speckle tracking echocardiography (2D-STE) is an emerging technique for assessing fetal cardiac function by measuring global longitudinal strain. Alterations in global longitudinal strain may serve as early indicator of pregnancy complications, making 2D-STE a potentially valuable tool for early detection. Early detection can facilitate timely interventions to reduce fetal and maternal morbidity and mortality.

Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors.

Objective: To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.

Methods: To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands.