We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period. We collected the data of pregnant women who were admitted to the obstetric clinic in the first trimester. The maternal age, body mass index, gravida, parity, previous birth weight, smoking status, the first-visit venous plasma glucose level, the family history of diabetes mellitus, and the results of an oral glucose tolerance test of the patients were evaluated. The women were categorized into groups based on having and not having a GDM diagnosis and also as being nulliparous or primiparous. 7 common ML algorithms were employed to construct the predictive model. 97 mothers were included in the study. 19 and 26 nulliparous were with and without GDM, respectively. 29 and 23 primiparous were with and without GDM, respectively. It was found that the greatest feature importance variables were the venous plasma glucose level, maternal BMI, and the family history of diabetes mellitus. The eXtreme Gradient Boosting (XGB) Classifier had the best predictive value for the two models with the accuracy of 66.7% and 72.7%, respectively. The XGB classifier model constructed with maternal sociodemographic findings and the obstetric history could be used as an early prediction model for GDM especially in low-income countries.

Objective: This study was aimed to identify the most effective machine learning (ML) algorithm for predicting preeclampsia based on sociodemographic and obstetric factors during the preconception period. Methods: Data from pregnant women admitted to the obstetric clinic during their first trimester were analyzed, focusing on maternal age, body mass index (BMI), smoking status, history of diabetes mellitus, gestational diabetes mellitus, and mean arterial pressure. The women were grouped by whether they had a preeclampsia diagnosis and by whether they had one or two live births. Predictive models were then developed using five commonly applied ML algorithms. Results: The study included 100 mothers divided into four groups: 22 nulliparous mothers with preeclampsia, 25 nulliparous mothers without preeclampsia, 28 parous mothers with preeclampsia, and 25 parous mothers without preeclampsia. Analysis showed that maternal BMI and family history of diabetes mellitus were the most significant predictive variables. Among the predictive models, the extreme gradient boosting (XGB) classifier demonstrated the highest accuracy, achieving 70% and 72.7% in the respective groups. Conclusions: A predictive model utilizing an ML algorithm based on maternal sociodemographic data and obstetric history could serve as an early detection tool for preeclampsia.

Comparison of conventional karyotype analysis and CMA results with ultrasound findings in pregnancies with normal QF-PCR results - Turkish Journal of Obstetrics and Gynecology

Postpartum depression (PPD) is a prevalent maternal mental health condition with significant consequences for mothers, infants, and families. The determina

Aims: To assess the indications of termination of pregnancies (TOP) for fetal anomaly beyond 22 weeks of gestation. Methods: It is a retrospective study conducted at Eskişehir City Hospital between January 2021 and December 2023. Our hospital which It serves as a tertiary referral center in the city. During the research period, 4143 births took place, and 495 women were treated in our high-risk unit. During the study period, 34 women underwent elective TOP at at least 22 weeks’ gestation. Routine 18–20-week anomaly screening was recommended for all pregnant women. Results: The sonographic findings revealed the presence of 15 cases of central nervous system malformation, 4 cases of micromelia and thorax hypoplasia, 1 case of diafragma hernia, 3 case of early IUGG, 2 cases of cardiac anomaly, 2 cases of neural tube defect, 3 cases of isolated thorax hypoplasia, 1 case of scoliosis, 1 case of renal agenesis, 1 case of thick nuchal fold, 1 case of partial corpus collosum agenesis. Among 34 cases, there were 1 case of mosaic trisomy 21, case of trisomy 21, 1 case of trisomy 1, 3 cases of trisomy 13, 2 cases of trisomy 18. Array analysis revealed a case of 14 p deletion, 1 case of 16 p 11.2 deletion, 1 case of 1p36 deletion. In all study population 5 cases underwent cesarean delivery. Majority of the terminations were achieved by misoprostol or cervical balloon application. Conclusion: In conclusion, although late termination of pregnancy by fetocide procedure seems to be safe, earlier detection of fetal structural and chromosomal anomalies is mandatory.