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.

Purpose: Cervical insufficiency is a significant risk factor for preterm birth and miscarriage during the second trimester; cervical cerclage is a treatment option. This study seeks to evaluate the predictive roles of various clinical factors and to develop predictive models for immediate and long-term outcomes after rescue cerclage.

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.

BACKGROUND Acute liver failure (ALF) is known to be a rapidly progressive and fatal disease. Various models which could help to estimate the post-transplant outcome for ALF have been developed; however, none of them have been proved to be the definitive predictive model of accuracy. We suggest a new predictive model, and investigated which model has the highest predictive accuracy for the short-term outcome in patients who underwent living donor liver transplantation (LDLT) due to ALF. MATERIAL AND METHODS Data from a total 88 patients were collected retrospectively. King's College Hospital criteria (KCH), Child-Turcotte-Pugh (CTP) classification, and model for end-stage liver disease (MELD) score were calculated. Univariate analysis was performed, and then multivariate statistical adjustment for preoperative variables of ALF prognosis was performed. A new predictive model was developed, called the MELD conjugated serum phosphorus model (MELD-p). The individual diagnostic accuracy and cut-off value of models in predicting 3-month post-transplant mortality were evaluated using the area under the receiver operating characteristic curve (AUC). The difference in AUC between MELD-p and the other models was analyzed. The diagnostic improvement in MELD-p was assessed using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS The MELD-p and MELD scores had high predictive accuracy (AUC >0.9). KCH and serum phosphorus had an acceptable predictive ability (AUC >0.7). The CTP classification failed to show discriminative accuracy in predicting 3-month post-transplant mortality. The difference in AUC between MELD-p and the other models had statistically significant associations with CTP and KCH. The cut-off value of MELD-p was 3.98 for predicting 3-month post-transplant mortality. The NRI was 9.9% and the IDI was 2.9%. CONCLUSIONS MELD-p score can predict 3-month post-transplant mortality better than other scoring systems after LDLT due to ALF. The recommended cut-off value of MELD-p is 3.98.

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 < 0.001). And SAV group showed significantly higher rates of pregnancy associated hypertension, preterm birth, and neonatal hypoglycemia and sepsis, compared to normoglycemic group (neonatal sepsis, p = 0.008; the others, p < 0.001). In multivariate analysis, postpartum T2DM was associated with SAV, GDM (with/without insulin), nulliparity, pre-pregnancy BMI, chronic hypertension, hyperlipidemia, and DM family history. A scoring model to predict postpartum T2DM within 5 years, achieved an area under the curve of 0.74. This study demonstrated that not only GDM, but also SAV is a significant risk factor for postpartum T2DM.

Background: Simultaneous presentation of pregnancy and chronic myeloid leukemia (CML) is rare. Tyrosine kinase inhibitors (TKIs) are rarely used in pregnancy. There is almost no information on the effect of recently developed TKIs on the fetus. Case Report: A 22-year-old woman became pregnant while using radotinib, a novel TKI for CML. She was concerned about the possible teratogenic effect of radotinib; hence the first pregnancy was terminated. The patient underwent full-term delivery at the second pregnancy. The infant had facial deformity and congenital laryngomalacia. Conclusion: We witnessed structural abnormality when the patient used radotinib during the first trimester of pregnancy. When radotinib is prescribed for women of childbearing age, thorough education about contraception is necessary.