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Abstract The main treatment goal for immune thrombocytopenia (ITP) is bleeding risk reduction, particularly during procedures. While adjusting platelet thresholds with ITP treatments is recommended, platelet transfusions are commonly used despite controversial benefits. We evaluated the effectiveness of platelet transfusion in reducing post-procedure bleeding risk and identified predictive indicators of bleeding. A nationally representative database was used to develop a model predicting post-procedure bleeding risk in patients with ITP. Machine learning analyses, including random forest feature importance and Shapley additive explanations (SHAP) values, assessed 34 risk factors, including the platelet transfusion amount. The random forest model had an area under the receiver-operating characteristic curve of 93.6%. Key variables influencing bleeding risk included platelet transfusion amount, high-risk procedure, anticoagulant use, anemia, age, ITP treatment, and newly diagnosed ITP, all positively correlated with bleeding risk. Conversely, no antiplatelet or anticoagulant use and moderate- or low-risk procedures were negatively associated with bleeding risk. SHAP plots showed that platelet transfusion amount correlated with high-risk procedures, and bleeding risk increased with age in high-risk procedures. Bleeding risk in patients with ITP is primarily determined by procedural risk and patient condition, rather than platelet transfusion. Minimizing unnecessary platelet transfusions and addressing bleeding risk factors pre-procedure is crucial.

Abstract Preterm birth (PTB) is one of the most common and serious complications of pregnancy, leading to mortality and severe morbidities that can impact lifelong health. PTB could be associated with various maternal medical condition and dental status including periodontitis. The purpose of this study was to identify major predictors of PTB among clinical and dental variables using machine learning methods. Prospective cohort data were obtained from 60 women who delivered singleton births via cesarean section (30 PTB, 30 full-term birth [FTB]). Dependent variables were PTB and spontaneous PTB (SPTB). 15 independent variables (10 clinical and 5 dental factors) were selected for inclusion in the machine learning analysis. Random forest (RF) variable importance was used to identify the major predictors of PTB and SPTB. Shapley additive explanation (SHAP) values were calculated to analyze the directions of the associations between the predictors and PTB/SPTB. Major predictors of PTB identified by RF variable importance included pre-pregnancy body mass index (BMI), modified gingival index (MGI), preeclampsia, decayed missing filled teeth (DMFT) index, and maternal age as in top five rankings. SHAP values revealed positive correlations between PTB/SPTB and its major predictors such as premature rupture of the membranes, pre-pregnancy BMI, maternal age, and MGI. The positive correlations between these predictors and PTB emphasize the need for integrated medical and dental care during pregnancy. Future research should focus on validating these predictors in larger populations and exploring interventions to mitigate these risk factors.

Abstract Neurodevelopmental disorders (NDD) in offspring are associated with a complex combination of pre-and postnatal factors. This study uses machine learning and population data to evaluate the association between prepregnancy or perinatal risk factors and the NDD of offspring. Population-based retrospective cohort data were obtained from Korea National Health Insurance Service claims data for 209,424 singleton offspring and their mothers who gave birth for the first time in 2007. The dependent variables were motor development disorder (MDD), cognitive development disorder (CDD) and combined overall neurodevelopmental disorder (NDD) from offspring. Seventeen independent variables from 2002 to 2007 were included. Random forest variable importance and Shapley Additive Explanation (SHAP) values were calculated to analyze the directions of its associations with the predictors. The random forest with oversampling registered much higher areas under the receiver-operating-characteristic curves than the logistic regression of interaction and non-linearity terms, 79% versus 50% (MDD), 82% versus 52% (CDD) and 74% versus 50% (NDD). Based on random forest variable importance, low socioeconomic status and age at birth were highly ranked. In SHAP values, there was a positive association between NDD and pre- or perinatal outcomes, especially, fetal male sex with growth restriction associated the development of NDD in offspring.

Background: Lifestyle changes, such as those related to the COVID-19 pandemic, including alterations in physical activity and dietary habits, are known to affect pregnancy outcomes. In particular, suboptimal intrauterine conditions during pregnancy are known to influence not only fetal growth but also growth during infancy. However, research on the impact of the environmental changes caused by the COVID-19 pandemic on the growth of infants and children during their early years is lacking. To address this issue, this study evaluated the effect of the COVID-19 pandemic on obesity in infants. Methods: This retrospective cohort study used the data collected from the Korea National Health Insurance (KNHI) claims database. The data of 1985,678 women who delivered infants between 2015 and 2021 were collected. Women who delivered during the pandemic and those who delivered during the pre-pandemic period were matched in a 1:1 frequency-matched pair procedure for factors such as age, hypertension, diabetes mellitus, preeclampsia, gestational diabetes mellitus, mode of delivery, gestational age at delivery, offspring sex, and birth weight. Finally, 197,580 women were enrolled. The weight and head circumference of infants (4–6 months of age) of the COVID-19 pandemic group were compared with those of the pre-pandemic group. Results: The COVID-19 pandemic group infants exhibited significantly higher weight and prevalence of obesity at 4–6 months of age compared to infants in the pre-pandemic group. After adjustment for covariates, pandemic group infants had a higher risk of obesity (odds ratio: 1.54, 95% confidence interval: 1.51–1.57) compared to the pre-pandemic group infants. Conclusion: The COVID-19 pandemic has had a notable impact on the weight of infants aged 4–6 months. This suggests that pandemic conditions may influence the growth of newborns, underscoring the importance of monitoring and assessing trends in the growth of infants born during such crises.

Abstract Background Breastfeeding resets insulin resistance caused by pregnancy however, studies on the association between breastfeeding and diabetes mellitus (DM) have reported inconsistent results. Therefore, we aimed to investigate the risk of DM according to breastfeeding duration in large-scale population-based retrospective study. In addition, machine-learning prediction models for DM and hemoglobin A1c (HbA1c) were developed to further evaluate this association. Methods We used the Korean National Health and Nutrition Examination Surveys database, a nationwide and population-based health survey from 2010 to 2020. We included 15,946 postmenopausal women with a history of delivery, whom we divided into three groups according to the average breastfeeding duration: (1) no breastfeeding, (2) < 12 months breastfeeding, and (3) ≥ 12 months breastfeeding. Prediction models for DM and HbA1c were developed using an artificial neural network, decision tree, logistic regression, Naïve Bayes, random forest, and support vector machine. Results In total, 2248 (14.1%) women had DM and 14,402 (90.3%) had a history of breastfeeding. The prevalence of DM was the lowest in the < 12 breastfeeding group (no breastfeeding vs. < 12 months breastfeeding vs. ≥ 12 months breastfeeding; 161 [10.4%] vs. 362 [9.0%] vs. 1,725 [16.7%], p

Abstract This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naïve Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010–2019. The dependent variable was metabolic syndrome. The 86 independent variables included demographic/socioeconomic determinants, cardiovascular disease, breastfeeding duration and other medical/obstetric information. The random forest had the best performance in terms of the area under the receiver-operating-characteristic curve, e.g., 90.7%. According to random forest variable importance, the top predictors of metabolic syndrome included body mass index (0.1032), medication for hypertension (0.0552), hypertension (0.0499), cardiovascular disease (0.0453), age (0.0437) and breastfeeding duration (0.0191). Breastfeeding duration is a major predictor of metabolic syndrome for women together with body mass index, diagnosis and medication for hypertension, cardiovascular disease and age.

Abstract This study aimed to examine the impact of term LBW on short-term neonatal and long-term neurodevelopmental outcomes in children 5–7 years of age. This is a population-based cohort study that merged national data from the Korea National Health Insurance claims and National Health Screening Program for Infants and Children. The participants were women who gave birth at a gestational age of ≥ 37 weeks between 2013 and 2015 in the Republic of Korea, and were tracked during 2020 for the neurodevelopmental surveillance of their children. Among 830,806 women who gave birth during the study period, 31,700 (3.8%) of their babies weighed less than 2500 g. By Cox proportional hazard analysis, children aged 5–7 years who had LBW were associated with any developmental, motor developmental delay, cognitive developmental delay, autism spectrum, attention deficit hyperactivity disorders, and epileptic and febrile seizures.Children born with term LBW were more vulnerable to neurodevelopmental disorders at 5–7 years of age than those with normal and large birth weights. This study further substantiates counseling parents regarding the long-term outcomes of children being born underweight.

Patient blood management is an evidence-based concept that seeks to minimize blood loss by maintaining adequate hemoglobin levels and optimizing hemostasis during surgery. Since the coronavirus disease 2019 pandemic, patient blood management has gained significance due to fewer blood donations and reduced amounts of blood stored for transfusion. Recently, the prevalence of postpartum hemorrhage (PPH), as well as the frequency of PPH-associated transfusions, has steadily increased. Therefore, proper blood transfusion is required to minimize PPH-associated complications while saving the patient’s life. Several guidelines have attempted to apply this concept to minimize anemia during pregnancy and bleeding during delivery, prevent bleeding after delivery, and optimize recovery methods from anemia. This study systematically reviewed various guidelines to determine blood loss management in pregnant women.

Abstract Background Cleansing of the vulva and perineum is recommended during preparation for vaginal delivery, and special attention is paid to cleansing before episiotomy because episiotomy is known to increase the risk of perineal wound infection and/or dehiscence. However, the optimal method of perineal cleansing has not been established, including the choice of antiseptic agent. To address this issue, we designed a randomized controlled trial to examine whether skin preparation with chlorhexidine-alcohol is superior to povidone-iodine for the prevention of perineal wound infection after vaginal delivery. Methods In this multicenter randomized controlled trial, term pregnant women who plan to deliver vaginally after episiotomy will be enrolled. The participants will be randomly assigned to use antiseptic agents for perineal cleansing (povidone-iodine or chlorhexidine-alcohol). The primary outcome is superficial or deep perineal wound infection within 30 days after vaginal delivery. The secondary outcomes are the length of hospital stay, physician office visits, or hospital readmission for infection-related complications, endometritis, skin irritations, and allergic reactions. Discussion This study will be the first randomized controlled trial aiming to determine the optimal antiseptic agent for the prevention of perineal wound infections after vaginal delivery. Trial registration ClinicalTrials.gov NCT05122169. First submitted date on 8 November 2021. First posted date on 16 November 2021

Objective The aim of this study was to evaluate the changes in twin pregnancy outcomes between 2007 and 2016 in a Korean population. Methods The data for this nationwide population-based study was obtained from the national birth registry of the Korean National Statistical Office and the Health Insurance Review & Assessment Service of Korea. Women with twin pregnancies who gave birth between 2007 and 2016 were included. Results From 2007 to 2016, the rate of twin pregnancies increased (1.37% vs. 1.91%, respectively, P

Objective This study systematically analyzed coronavirus disease 2019 (COVID-19) and vaccination details during pregnancy by using the national health insurance claims data. Methods Population-based retrospective cohort data of 12,399,065 women aged 15–49 years were obtained from the Korea National Health Insurance Service claims database between 2019 and 2021. Univariate analysis was performed to compare the obstetric outcomes of pregnant women (ICD-10 O00–O94) and their newborns (ICD-10 P00–P96) with and without COVID-19. Univariate analysis was also performed to compare the age and obstetric outcomes of pregnant women receiving different types of vaccines. Results The percentage of pregnant women with COVID-19 during pregnancy was 0.11%. Some obstetric outcomes of pregnant women with COVID-19, including the rates of preterm birth or cesarean delivery, were significantly better than those of pregnant women without COVID-19. The rate of miscarriage was higher in pregnant women with COVID-19 than without COVID-19. However, the outcomes of newborns of women with and without COVID-19 were not significantly different. Regarding vaccination type, obstetric outcomes of pregnant women appeared to be worse with the viral vector vaccine than with the mRNA vaccine. Conclusion To the best of our knowledge, this is the first study to systematically analyze COVID-19 and vaccination details during pregnancy using the national health insurance claims data in Korea. The obstetric outcomes in pregnant women with and without COVID-19 and their newborns were similar.

This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.

BackgroundMaternal heart disease is suspected to affect preterm birth (PTB); however, validated studies on the association between maternal heart disease and PTB are still limited. This study aimed to build a prediction model for PTB using machine learning analysis and nationwide population data, and to investigate the association between various maternal heart diseases and PTB.MethodsA population-based, retrospective cohort study was conducted using data obtained from the Korea National Health Insurance claims database, that included 174,926 primiparous women aged 25-40 years who delivered in 2017. The random forest variable importance was used to identify the major determinants of PTB and test its associations with maternal heart diseases, i.e., arrhythmia, ischemic heart disease (IHD), cardiomyopathy, congestive heart failure, and congenital heart disease first diagnosed before or during pregnancy.ResultsAmong the study population, 12,701 women had PTB, and 12,234 women had at least one heart disease. The areas under the receiver-operating-characteristic curves of the random forest with oversampling data were within 88.53 to 95.31. The accuracy range was 89.59 to 95.22. The most critical variables for PTB were socioeconomic status and age. The random forest variable importance indicated the strong associations of PTB with arrhythmia and IHD among the maternal heart diseases. Within the arrhythmia group, atrial fibrillation/flutter was the most significant risk factor for PTB based on the Shapley additive explanation value.ConclusionsCareful evaluation and management of maternal heart disease during pregnancy would help reduce PTB. Machine learning is an effective prediction model for PTB and the major predictors of PTB included maternal heart disease such as arrhythmia and IHD.

Although preterm birth (PTB), a birth before 34 weeks of gestation accounts for only less than 3% of total births, it is a critical cause of various perinatal morbidity and mortality. Several studies have been conducted on the association between maternal exposure to PM and PTB, but the results were inconsistent. Moreover, no study has analyzed the risk of PM on PTB among women with cardiovascular diseases, even though those were thought to be highly susceptible to PM considering the cardiovascular effect of PM. Therefore, we aimed to evaluate the effect of PM10 on early PTB according to the period of exposure, using machine learning with data from Korea National Health Insurance Service (KNHI) claims. Furthermore, we conducted subgroup analysis to compare the risk of PM on early PTB among pregnant women with cardiovascular diseases and those without. A total of 149,643 primiparous singleton women aged 25 to 40 years who delivered babies in 2017 were included. Random forest feature importance and SHAP (Shapley additive explanations) value were used to identify the effect of PM10 on early PTB in comparison with other well-known contributing factors of PTB. AUC and accuracy of PTB prediction model using random forest were 0.9988 and 0.9984, respectively. Maternal exposure to PM10 was one of the major predictors of early PTB. PM10 concentration of 5 to 7 months before delivery, the first and early second trimester of pregnancy, ranked high in feature importance. SHAP value showed that higher PM10 concentrations before 5 to 7 months before delivery were associated with an increased risk of early PTB. The probability of early PTB was increased by 7.73%, 10.58%, or 11.11% if a variable PM10 concentration of 5, 6, or 7 months before delivery was included to the prediction model. Furthermore, women with cardiovascular diseases were more susceptible to PM10 concentration in terms of risk for early PTB than those without cardiovascular diseases. Maternal exposure to PM10 has a strong association with early PTB. In addition, in the context of PTB, pregnant women with cardiovascular diseases are a high-risk group of PM10 and the first and early second trimester is a high-risk period of PM10.

Preterm birth (PTB) is one of the most important problems that pose dilemmas for both the obstetrician and neonatologist, placing a heavy burden psychologically and financially on the families involved, and triggering high socio-economic costs to the public healthcare. The rate of PTB in Asian countries has been ranked at top globally. To reduce the PTB rate, to promote the prevention and intervention for PTB, and to better understand the pathophysiology underlying PTB, the Preterm Birth International Collaborative Australia branch (PREBIC-AA) was launched in 2017. A series scientific activities including organizing annual research symposiums has been planned and organized among Australasian countries. Here we briefly updated the current progress in clinical management and translational research on PTB in Australasian countries that have been participated in PREBIC-AA.

Abstract Background This study introduced machine learning approaches to predict newborn’s body mass index (BMI) based on ultrasound measures and maternal/delivery information. Methods Data came from 3159 obstetric patients and their newborns enrolled in a multi-center retrospective study. Variable importance, the effect of a variable on model performance, was used for identifying major predictors of newborn’s BMI among ultrasound measures and maternal/delivery information. The ultrasound measures included biparietal diameter (BPD), abdominal circumference (AC) and estimated fetal weight (EFW) taken three times during the week 21 - week 35 of gestational age and once in the week 36 or later. Results Based on variable importance from the random forest, major predictors of newborn’s BMI were the first AC and EFW in the week 36 or later, gestational age at delivery, the first AC during the week 21 - the week 35, maternal BMI at delivery, maternal weight at delivery and the first BPD in the week 36 or later. For predicting newborn’s BMI, linear regression (2.0744) and the random forest (2.1610) were better than artificial neural networks with one, two and three hidden layers (150.7100, 154.7198 and 152.5843, respectively) in the mean squared error. Conclusions This is the first machine-learning study with 64 clinical and sonographic markers for the prediction of newborns’ BMI. The week 36 or later is the most effective period for taking the ultrasound measures and AC and EFW are the best predictors of newborn’s BMI alongside gestational age at delivery and maternal BMI at delivery.

Abstract Background Obstetric hemorrhage is one of the most common causes of obstetrical morbidity and mortality, and transfusion is the most important management for hemorrhage. The aim of our study was to investigate the pre-pregnancy and pregnancy risk factors for peripartum transfusion. Methods Women who delivered a baby from 2010 to 2014 in Korea and participated in the Korean National Health Screening Program for Infants and Children were included. To analyze pre-pregnant risk factors for peripartum transfusion, an additional analysis was done for women who underwent a National Health Screening Examination within 1 year before pregnancy, including maternal waist circumference, body mass index, blood pressure, laboratory tests and history of smoking. Multivariable logistic regression analysis was used to estimate the risk factors for peripartum transfusion. Results Of the total 1,980,126 women who met the inclusion criteria, 36,868 (1.86%) were transfused at peripartum. In a multivariable regression model, the pregnancy risk factors for peripartum transfusion included maternal age above 35 years [odds ratio (OR): 1.41; 95% confidence interval (CI): 1.32–1.50], preterm birth (OR: 2.39; 95% CI: 2.15–2.65), and maternal hypertension (OR: 2.49; 95% CI: 2.24–2.77). Pre-pregnancy risk factors including fasting glucose level of more than 126 mg/dL (OR: 1.11; 95% CI: 1.02–1.20), current-smoker status (OR: 1.20; 95% CI: 1.06–1.37), and waist-circumference less than 80 cm (OR: 1.18; 95% CI: 1.06–1.30) were independently associated with peripartum blood transfusion. Conclusions Several pre-pregnancy and pregnancy risk factors were associated with peripartum blood transfusion. Some identified factors are modifiable before conception, and our study validated peripartum blood transfusion as a form of triage.

This study presents the most comprehensive machine-learning analysis for the predictors of blood transfusion, all-cause mortality, and hospitalization period in COVID-19 patients. Data came from Korea National Health Insurance claims data with 7943 COVID-19 patients diagnosed during November 2019–May 2020. The dependent variables were all-cause mortality and the hospitalization period, and their 28 independent variables were considered. Random forest variable importance (GINI) was introduced for identifying the main factors of the dependent variables and evaluating their associations with these predictors, including blood transfusion. Based on the results of this study, blood transfusion had a positive association with all-cause mortality. The proportions of red blood cell, platelet, fresh frozen plasma, and cryoprecipitate transfusions were significantly higher in those with death than in those without death (p-values < 0.01). Likewise, the top ten factors of all-cause mortality based on random forest variable importance were the Charlson Comorbidity Index (53.54), age (45.68), socioeconomic status (45.65), red blood cell transfusion (27.08), dementia (19.27), antiplatelet (16.81), gender (14.60), diabetes mellitus (13.00), liver disease (11.19) and platelet transfusion (10.11). The top ten predictors of the hospitalization period were the Charlson Comorbidity Index, socioeconomic status, dementia, age, gender, hemiplegia, antiplatelet, diabetes mellitus, liver disease, and cardiovascular disease. In conclusion, comorbidity, red blood cell transfusion, and platelet transfusion were the major factors of all-cause mortality based on machine learning analysis. The effective management of these predictors is needed in COVID-19 patients.

We investigated changes in gene expression of cervical collagens, matrix metalloproteinases (MMPs), and tissue inhibitors of metalloproteinases (TIMPs) during pre-gestational uterine cervical excision and/or inflammation-induced preterm labor in mice. Forty sexually mature female mice were uniformly divided into four groups: sham, cervical excision, lipopolysaccharide (LPS) injection, and cervical excision plus LPS injection. Partial cervical tissue excision was performed at five weeks of age before mating. LPS was injected into the lower right uterine horn near the cervix on gestational day 16. Mice were sacrificed immediately postpartum. Uterine cervices were collected and subjected to quantitative real-time PCR. Col4α1 and Col5α1 expression increased significantly in the cervical excision plus LPS injection group compared to the sham group (p < 0.01 and p = 0.024, respectively). MMP-14 expression levels increased in the cervical excision plus LPS injection group compared to the sham group (p < 0.01). TIMP-1 expression was not significantly decreased in this group. Increased expression levels of Col4α1, Col5α1, and MMP-14 were associated with cervical excision plus inflammation-induced preterm labor. Thus, pre-gestational cervical remodeling through specific collagen metabolism and MMP activation may involve the pathogenesis of spontaneous preterm labor.

Abstract Background The aim of this study was to determine the effect of cerclage in women who underwent cervical conization. Methods Study data were collected from the Korea National Health Insurance Claims Database of the Health Insurance Review and Assessment Service for 2009–2013. Women who had a conization in 2009 and a subsequent first delivery between 2009 and 2013 in Korea were enrolled. Results Among the women who had conization in 2009, 1075 women had their first delivery between 2009 and 2013. A cerclage was placed in 161 of the women who were treated by conization. The rate of preterm birth was higher in the women who were treated with cerclage following a conization compared with those without cerclage (10.56 vs 4.27, p

There has been no machine learning study with a rich collection of clinical, sonographic markers to compare the performance measures for a variety of newborns’ weight-for-height indicators. This study compared the performance measures for a variety of newborns’ weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. The source of data for this study was a multi-center retrospective study with 2949 mother–newborn pairs. The mean-squared-error-over-variance measures of five machine learning approaches were compared for newborn’s weight, newborn’s weight/height, newborn’s weight/height2 and newborn’s weight/hieght3. Random forest variable importance, the influence of a variable over average node impurity, was used to identify major predictors of these newborns’ weight-for-height indicators among ultrasonographic data and maternal/delivery information. Regarding ultrasonographic fetal biometry, newborn’s weight, newborn’s weight/height and newborn’s weight/height2 were better indicators with smaller mean-squared-error-over-variance measures than newborn’s weight/height3. Based on random forest variable importance, the top six predictors of newborn’s weight were the same as those of newborn’s weight/height and those of newborn’s weight/height2: gestational age at delivery time, the first estimated fetal weight and abdominal circumference in week 36 or later, maternal weight and body mass index at delivery time, and the first biparietal diameter in week 36 or later. These six predictors also ranked within the top seven for large-for-gestational-age and the top eight for small-for-gestational-age. In conclusion, newborn’s weight, newborn’s weight/height and newborn’s weight/height2 are more suitable for ultrasonographic fetal biometry with smaller mean-squared-error-over-variance measures than newborn’s weight/height3. Machine learning with ultrasonographic data would be an effective noninvasive approach for predicting newborn’s weight, weight/height and weight/height2.

This study uses machine learning and population data to analyze major determinants of preterm birth including depression and particulate matter. Retrospective cohort data came from Korea National Health Insurance Service claims data for 405,586 women who were aged 25–40 years and gave births for the first time after a singleton pregnancy during 2015–2017. The dependent variable was preterm birth during 2015–2017 and 90 independent variables were included (demographic/socioeconomic information, particulate matter, disease information, medication history, obstetric information). Random forest variable importance was used to identify major determinants of preterm birth including depression and particulate matter. Based on random forest variable importance, the top 40 determinants of preterm birth during 2015–2017 included socioeconomic status, age, proton pump inhibitor, benzodiazepine, tricyclic antidepressant, sleeping pills, progesterone, gastroesophageal reflux disease (GERD) for the years 2002–2014, particulate matter for the months January–December 2014, region, myoma uteri, diabetes for the years 2013–2014 and depression for the years 2011–2014. In conclusion, preterm birth has strong associations with depression and particulate matter. What is really needed for effective prenatal care is strong intervention for particulate matters together with active counseling and medication for common depressive symptoms (neglected by pregnant women).

Hyperemesis gravidarum is known to be associated with poor perinatal outcomes. This study aimed to identify pre-pregnancy risk factors for hospital admission in women with hyperemesis gravidarum. We enrolled women who had delivered between 1 January 2013 and 31 December 2015, and had undergone a national health screening examination through the National Health Insurance Corporation 1–2 years before their first delivery. Multiple logistic regression analysis was performed to estimate the risk factors for hospital admission due to hyperemesis gravidarum. Of the 216,373 study participants with hyperemesis gravidarum, 2210 (1.02%) pregnant women were hospitalized. These women had lower waist circumference and were underweight based on body mass index compared to pregnant women who did not require hospitalization due to hyperemesis gravidarum. On multivariate analysis, primiparity, multiple pregnancies, female fetus, alcohol consumption, and pre-pregnancy underweight status were associated with an increased risk of hospitalization due to the condition. In this population-based cohort study, we found that hospitalization due to hyperemesis gravidarum was associated with pre-pregnancy lifestyle characteristics. Early recognition and management of these pre-pregnancy factors may help control the need for hospitalization in women with the condition in subsequent pregnancies.

This study reviews the current status and future prospective of knowledge on the use of artificial intelligence for the prediction of spontaneous preterm labor and birth (“preterm birth” hereafter). The summary of review suggests that different machine learning approaches would be optimal for different types of data regarding the prediction of preterm birth: the artificial neural network, logistic regression and/or the random forest for numeric data; the support vector machine for electrohysterogram data; the recurrent neural network for text data; and the convolutional neural network for image data. The ranges of performance measures were 0.79–0.94 for accuracy, 0.22–0.97 for sensitivity, 0.86–1.00 for specificity, and 0.54–0.83 for the area under the receiver operating characteristic curve. The following maternal variables were reported to be major determinants of preterm birth: delivery and pregestational body mass index, age, parity, predelivery systolic and diastolic blood pressure, twins, below high school graduation, infant sex, prior preterm birth, progesterone medication history, upper gastrointestinal tract symptom, gastroesophageal reflux disease, Helicobacter pylori, urban region, calcium channel blocker medication history, gestational diabetes mellitus, prior cone biopsy, cervical length, myomas and adenomyosis, insurance, marriage, religion, systemic lupus erythematosus, hydroxychloroquine sulfate, and increased cerebrospinal fluid and reduced cortical folding due to impaired brain growth.

This study investigated whether anterior placenta previa in the second trimester is associated with neonatal respiratory distress syndrome (RDS). The neonates delivered by 2067 women between 2007 and 2015 were evaluated for the presence of RDS through birth records. The location of the placenta and the presence of placenta previa during the second and third trimesters were assessed and recorded. Demographic, prenatal, and perinatal records were reviewed. Anterior placenta previa in the second and third trimesters was correlated with RDS. Infants with lower gestational ages and birth weights had higher rates of RDS. Multivariate logistic regression analysis identified a significant association between anterior placenta previa in the second trimester and neonatal RDS. Anterior placenta previa in the second trimester is associated with neonatal RDS. Obstetricians should be aware that anterior placenta previa detected during the second trimester, irrespective of whether the placenta will migrate in the third trimester, may be an independent risk factor for neonatal RDS.

This study aimed to determine the rate of repeat uterine artery embolization (UAE) in women with a previous UAE. Study data were collected from the Korea National Health Insurance Claims Database of the Health Insurance Review and Assessment Service for 2009-2013. We enrolled women who had a first delivery in 2009 and a second delivery between 2010 and 2013. Among 226,408 women who had a first delivery in 2009, 296 underwent UAE. A total of 127,506 women had a second delivery between 2010 and 2013. Of 296 women who underwent UAE after the first delivery, 94 had a second delivery between 2010 and 2013. Women with a previous UAE had a higher rate of UAE at the second delivery than women without a previous UAE. Multivariate adjusted analysis showed that a UAE at the first delivery increased the rate of UAE at the second delivery (odds ratio 25.56, 95% confidence interval 9.86-66.23). Women with a previous UAE should be appropriately counseled and monitored for the need for a repeat UAE.

Preeclampsia and gestational diabetes (GDM) have several mechanisms in common. The aim of this study was to determine whether women with preeclampsia have an increased risk of GDM in a subsequent pregnancy. Study data were collected from the Korea National Health Insurance Claims Database of the Health Insurance Review and Assessment Service for 2007-2012. Patients who had their first delivery in 2007 and a subsequent delivery between 2008 and 2012 in Korea were enrolled. A model of multivariate logistic regression analysis was performed with GDM as the final outcome to evaluate the risk of GDM in the second pregnancy. Among the 252,276 women who had their first delivery in 2007, 150,794 women had their second delivery between 2008 and 2012. On the multivariate regression analysis, women with preeclampsia alone in the first pregnancy had an increased risk of GDM in the second pregnancy when compared with women who had neither of these conditions in their first pregnancy (OR 1.2, 95% CI, 1.1-1.3). Women with GDM alone in the first pregnancy were at an increased risk for GDM in the second pregnancy (OR 3.3, 95% CI 3.1-3.4). The co-presence of preeclampsia and GDM in the first pregnancy further increased the risk of GDM in the second pregnancy (OR 5.9, 95% CI, 4.0-8.6). Our study showed that a history of preeclampsia may serve as an additional risk factor for GDM in a subsequent pregnancy.

Although there is accumulating evidence regarding the additional protective effect of folic acid against adverse pregnancy outcomes other than neural tube defects, these effects have not been elucidated in detail. We evaluated whether folic acid supplementation is associated with favorable maternal and fetal outcomes. This was a secondary analysis of 215 pregnant women who were enrolled in our prior study. With additional data from telephone interviews regarding prenatal folic acid supplementation, existing demographic, maternal and fetal data were statistically analyzed. The concentration of folic acid in maternal blood was significantly higher following folic acid supplementation (24.6 ng/mL vs.11.8 ng/mL). In contrast, homocysteine level in maternal blood decreased with folic acid supplementation (5.5 µmol/mL vs. 6.8 µmol/mL). The rates of both preeclampsia (odds ratio [OR], 0.27; 95% confidence interval [CI], 0.09-0.76) and small for gestational age (SGA; 9.2% vs. 20.0%; OR, 0.42; 95% CI, 0.18-0.99) were lower in the folic acid supplementation group than those in the control group. Other pregnancy outcomes had no association with folic acid supplementation. The findings indicate that folic acid supplementation may help to prevent preeclampsia and SGA. Further studies are warranted to elucidate the favorable effects of folic acid supplementation on pregnancy outcomes.