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In an 8-year period at two medical center, 138 patients underwent uterine artery embolization, and 11 of them were diagnosed with uterine necrosis. Among them, three were successfully conceived. However, one of them developed an arteriovenous malformation after an artificial abortion, and another experienced complications, including placenta previa and placenta accreta spectrum, which resulted in early preterm delivery and recurrent postpartum hemorrhage, necessitating subtotal hysterectomy. Therefore, it is crucial to prepare for potential adverse pregnancy outcomes in subsequent pregnancies for patients with a history of uterine necrosis.

Abstract Clinical implication of a single abnormal value (SAV) in the 100 g oral glucose tolerance test during pregnancy has not been established. We aimed to evaluate the risk of postpartum type 2 diabetes mellitus (T2DM) and investigate adverse pregnancy outcomes in women with SAV, using a retrospective database, from seven medical centers of Korea. Based on the Carpenter-Coustan criteria using two-step approach, pregnancy and postpartum outcomes were compared, among normoglycemic, SAV, and gestational diabetes mellitus (GDM) groups. Among 9353 women, 342 (3.66%) and 418(4.47%) women were included in SAV and GDM groups, respectively. SAV and GDM groups showed significantly higher rates of postpartum T2DM than normoglycemic group (7.60%, 14.83%, and 1.82%, respectively, p

Abstract Background Although pregnancy-associated heart failure with preserved ejection fraction (HFpEF) is increasing and contributing to maternal morbidity, little is known about its impact on pregnancy. We examined the risk factors for and adverse pregnancy outcomes of HFpEF in pregnant women. Methods We conducted a cross-sectional analysis of pregnancy-related hospitalizations from 2009 to 2020 using the perinatal database of seven multicenters. Cases of HFpEF were identified using the International Classification of Diseases and echocardiography findings. The patients were categorized into the HFpEF and control groups. Risk factors were evaluated using multivariate logistic regression analysis to generate odds ratios (OR) and 95% confidence intervals (CI). Furthermore, adjusted associations between HFpEF and adverse pregnancy outcomes were determined. Risk scores for the stratification of women at a high risk of HFpEF were calculated using a statistical scoring model. Results Of the 34,392 women identified, 258 (0.76%) were included in the HFpEF group. In multivariate analysis, HFpEF was significantly associated with old maternal age (OR, 1.04; 95% CI 1.02–1.07), multiple pregnancy (OR, 2.22; 95% CI 1.53–3.23), rheumatic disease (OR, 2.56; 95% CI 1.54–4.26), pregnancy induce hypertension (OR 6.02; 95% CI 3.61–10.05), preeclampsia (OR 24.66; 95% CI 18.61–32.66), eclampsia or superimposed preeclampsia (OR 32.74; 95% CI 21.60–49.64) and transfusion in previous pregnancy (OR 3.89; 95% CI 1.89–8.01). A scoring model to predict HFpEF with those factors achieved an area under the curve of 0.78 at cutoff value of 3. Women with HFpEF also had increased odds ratios of intensive care unit admission during the perinatal period (odds ratio, 5.98; 95% confidence interval, 4.36–8.21) and of postpartum hemorrhage (odds ratio, 5.98; 95% confidence interval, 2.02–3.64). Conclusions Pregnancy-associated HFpEF is associated with adverse pregnancy outcomes. A scoring model may contribute to screening HFpEF using echocardiography and preparing adverse pregnancy outcomes.

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.

Preterm birth (PTB) is a leading cause of perinatal morbidity and mortality globally. PTB rates have increased in South Korea despite reduction in birth rates. A history of PTB is a strong predictor of subsequent PTB and screening of cervical length between 16 0/7 weeks and 24 0/7 weeks of gestation is recommended in women with a singleton pregnancy and a prior spontaneous PTB. However, the prediction and prevention of spontaneous PTBs in women without a prior PTB remain a matter of debate. The scope of this review article comprises cervical screening and prevention strategies for PTB in asymptomatic women without a prior PTB, based on recent evidence and guidelines.

This study aimed to assess the current status of gestational diabetes mellitus (GDM) diagnosis and management, and the demand for a digital healthcare system, in order to develop an optimal digital-based management model for GDM. An anonymous online survey was conducted targeting pregnant/postpartum women (Group W), internal medicine physicians (Group P), and obstetricians (group O) from September 6, 2022 to December 31, 2022. The survey assessed the women’s knowledge of GDM and gathered information about healthcare professionals’ (HCPs) current GDM management practices. All groups were asked about their acceptance of and demands for a digital healthcare system for GDM. Statistical comparisons between groups were conducted using the chi-square test or Fisher’s exact test where appropriate. A total of 168 participants were in Group W, 185 in Group P, and 256 in Group O. Participants from all groups recognized the need for a digital healthcare system for GDM (Group W: 95.8%, Group P: 85.9%, Group O: 60%). However, HCPs showed less willingness to integrate these systems into their clinics than pregnant/postpartum women. Essential features identified were recording blood glucose levels and insulin, along with automatic data linkage from self-monitoring devices. Group W showed a higher preference for lab test access, search functionality, and fetal weight assessment than groups P and O (all P

Abstract This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks’ gestation), E1 (11–13 weeks’ gestation) and M1 (14–24 weeks’ gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model.

Abstract Background Cervical cancer is the third most common gynecological malignancy. Conventional treatment options are known to be ineffective for the majority of patients with advanced or recurrent cervical cancer. Therefore, novel therapeutic agents for cervical cancer are necessary. In this study, the effects of CKD-602 in cervical cancer were investigated. Methods Three established human, immortalized, cervical cancer cell lines (CaSki, HeLa and SiHa) were used in this study. Following treatment with CKD-602, apoptosis was quantified using fluorescein isothiocyanate Annexin V-FITC and propidium iodide (PI) detection kit and cell cycle analysis was analyzed using fluorescence activated cell sorting (FACS). Transwell chambers were used for invasion assays. Western blot assay was performed to analyze proteomics. CaSki cells were subcutaneously injected into BALB/c-nude mice and cervical cancer xenograft model was established to elucidate the antitumor effect of CKD-602 in vivo. Results Treatment with CKD-602 induced apoptosis and increased expression of the enzyme PARP, cleaved PARP, and BAX. In addition, expression of phosphorylated p53 increased. Cell cycle arrest at G2/M phase and inhibition of invasion were detected after treatment with CKD-602. A significant decrease in cervical cancer tumor volume was observed in this in vivo model, following treatment with CKD-602. Conclusions This is the first report of CKD-602 having an antitumor effect in cervical cancer in both an in vitro and in vivo models. The results of this study indicate that CKD-602 may be a novel potential drug, targeting cervical cancer, providing new opportunities in the development of new therapeutic strategies.