This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor. Nine machine learning methods of logistic regression, random forest, Support Vector Machine (SVM), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), k-nearest neighbors (KNN), Voting, and Stacking were applied and compared for prediction of emergent CS during active labor. External validation was performed using a nationwide multicenter dataset for Korean fetal growth. A total of 6549 term nulliparous women was included in the analysis, and the emergent CS rate was 16.1%. The C-statistics values for KNN, Voting, XGBoost, Stacking, gradient boosting, random forest, LGBM, logistic regression, and SVM were 0.6, 0.69, 0.64, 0.59, 0.66, 0.68, 0.68, 0.7, and 0.69, respectively. The logistic regression model showed the best predictive performance with an accuracy of 0.78. The machine learning model identified nine significant variables of maternal age, height, weight at pre-pregnancy, pregnancy-associated hypertension, gestational age, and fetal sonographic findings. The C-statistic value for the logistic regression machine learning model in the external validation set (1391 term nulliparous women) was 0.69, with an overall accuracy of 0.68, a specificity of 0.83, and a sensitivity of 0.41. Machine learning algorithms with clinical and sonographic parameters at near term could be useful tools to predict individual risk of emergent CS during active labor in nulliparous women.

Background: Human papilloma virus infection and tobacco smoking are the major risk factors for cervical cancer. There are limited studies searching other risk factors for cervical cancer and the results are not consistent. This study investigated the relations between cervical cancer and possible risk factors, including secondhand cigarette smoke exposure, diabetes, body mass index (BMI), and work schedule. Methods: In this cross-sectional study, 29,557 women completed a cervical cancer questionnaire and were selected using 2010–2018 data from the Korea National Health and Nutrition Examination Survey. Details in secondhand smoke exposure, diabetes, BMI, and work schedule were assessed with participants’ health interviews and health-related surveys. Results: Two hundred sixty-two women (0.89%) in the sample were diagnosed with cervical cancer. Domestic secondhand smoke exposure, diabetes, and high BMI significantly increased cervical cancer risk. Respective odds ratios and 95% confidence intervals were: 1.547 (1.042–2.297), 2.156 (1.535–3.027), and 1.036 (1.006–1.067). Weekly work hours, and work schedule were not significantly related to cervical cancer incidence. Conclusion: Among Korean women, passive exposure to cigarette smoke at home, diabetes, and high BMI increase risk for cervical cancer.

The offspring of mothers with gestational diabetes mellitus (GDM) are at a higher risk for metabolic dysregulation and neurodevelopmental impairment. Evidence suggests that serotonin, which is present in both the placenta and the brain, programs the development and growth of the fetal brain. In the current study, we tested the hypothesis that GDM affects the methylation of the serotonin transporter gene (SLC6A4) and serotonin receptor gene (HTR2A) in the placenta. Ninety pregnant women were included in this study. Thirty mothers were diagnosed with GDM, and sixty mothers served as controls in a 1:2 ratio. Ten CpG sites within the promoter regions of SLC6A4 and HTR2A were analyzed using pyrosequencing. The relative expression of genes involved in DNA methylation was evaluated using real-time PCR. The average DNA methylation of placental SLC6A4 was higher in the GDM group than in the control group (2.29 vs. 1.16%, p < 0.001). However, the average DNA methylation level of HTR2A did not differ between the two groups. SLC6A4 methylation showed a positive correlation with maternal plasma glucose level and neonatal birth weight percentile and a negative correlation with the neonatal head circumference percentile. This finding suggests that epigenetic modification of the placental serotonin system may affect placental adaptation to a harmful maternal environment, thereby influencing the long-term outcome in the offspring.

Background: This study aimed to compare obstetric outcomes in Korean women with and without future cardiovascular disease (CVD) within 10 years after pregnancy, and assessed whether pregnancy complications are independent risk factors, and whether the combination of pregnancy complications has an additive function for risk factors for CVD. Methods: This was a nationwide population-based study combining the database of the Korea National Health Insurance claims and National Health Screening Programs to assess preeclampsia, low birth weight (LBW), and preterm delivery as risk factors for CVD. Cox proportional hazards models was used to evaluate the risk of total CVD, ischemic heart disease (IHD), and stroke after the pregnancy complications, with adjustment for potential confounding variables. Results: Women with CVD were likely to have a higher prevalence of pregnancy complications than women without CVD. The risk of total CVD was associated with preeclampsia (adjusted hazard ratio (HR), 1.60 [95% confidence interval (CI) 1.50–1.72]), LBW (1.20 [1.12–1.28]), and preterm delivery (1.32 [1.22–1.42]), after adjustment for confounders, including cardiovascular risk factors before pregnancy. The risk estimates of pregnancy complications for IHD were higher than those for stroke. In this study, the risk of total CVD was higher in the combined presence of preeclampsia and preterm delivery (2.23 [1.57–3.17] or all three complications (2.06 [1.76–2.40]), relative to no complications. The highest HR was noted in the risk of all pregnancy complications for IHD (2.39 [1.98–2.89]). Conclusion: Preeclampsia, preterm delivery, and LBW were independently associated with CVD in young Korean women. In addition, the combination of pregnancy complications had less-than-additive effects on CVD incidence.

Background: This study aimed to develop a clinical model to predict late-onset fetal growth restriction (FGR). Methods: This retrospective study included seven hospitals and was conducted between January 2009 and December 2020. Two sets of variables from the first trimester until 13 weeks (E1) and the early third trimester until 28 weeks (T1) were used to develop the FGR prediction models using a machine learning algorithm. The dataset was randomly divided into training and test sets (7:3 ratio). A simplified prediction model using variables with XGBoost’s embedded feature selection was developed and validated. Results: Precisely 32,301 patients met the eligibility criteria. In the prediction model for the whole cohort, the area under the curve (AUC) was 0.73 at E1 and 0.78 at T1 and the area under the precision-recall curve (AUPR) was 0.23 at E1 and 0.31 at T1 in the training set, while an AUC of 0.62 at E1 and 0.73 at T1 and an AUPR if 0.13 at E1, and 0.24 at T1 were obtained in the test set. The simplified prediction model performed similarly to the original model. Conclusions: A simplified machine learning model for predicting late FGR may be useful for evaluating individual risks in the early third trimester.