Background We aimed to develop and validate a nomogram for effective prediction of vaginal birth after cesarean (VBAC) and guide future clinical application. Methods We retrospectively analyzed data from hospitalized pregnant women who underwent trial of labor after cesarean (TOLAC), at the Fujian Provincial Maternity and Children’s Hospital, between October 2015 and October 2017. Briefly, we included singleton pregnant women, at a gestational age above 37 weeks who underwent a primary cesarean section, in the study. We then extracted their sociodemographic data and clinical characteristics, and randomly divided the samples into training and validation sets. We employed the least absolute shrinkage and selection operator (LASSO) regression to select variables and construct VBAC success rate in the training set. Thereafter, we validated the nomogram using the concordance index (C-index), decision curve analysis (DCA), and calibration curves. Finally, we adopted the Grobman’s model to perform comparisons with published VBAC prediction models. Results Among the 708 pregnant women included according to inclusion criteria, 586 (82.77%) patients were successfully for VBAC. Multivariate logistic regression models revealed that maternal height (OR, 1.11; 95% CI, 1.04 to 1.19), maternal BMI at delivery (OR, 0.89; 95% CI, 0.79 to 1.00), fundal height (OR, 0.71; 95% CI, 0.58 to 0.88), cervix Bishop score (OR, 3.27; 95% CI, 2.49 to 4.45), maternal age at delivery (OR, 0.90; 95% CI, 0.82 to 0.98), gestational age (OR, 0.33; 95% CI, 0.17 to 0.62) and history of vaginal delivery (OR, 2.92; 95% CI, 1.42 to 6.48) were independently associated with successful VBAC. The constructed predictive model showed better discrimination than that from the Grobman’s model in the validation series (c-index 0.906 VS 0.694, respectively). On the other hand, decision curve analysis revealed that the new model had better clinical net benefits than the Grobman’s model. Conclusions VBAC will aid in reducing the rate of cesarean sections in China. In clinical practice, the TOLAC prediction model will help improve VBAC’s success rate, owing to its contribution to reducing secondary cesarean section.

Background We aimed to investigate whether maternal chronic hepatitis B virus (HBV) infection affects preterm birth (PTB) in pregnant women. Methods We retrospectively analyzed HBV-infected and non-infected pregnant women attending antenatal care at Fujian Maternity and Child Health Hospital, Fuzhou, China between January 1, 2016 to December 31, 2018. Participants were divided into HBV infection (n = 1302) and control (n = 12,813) groups. We compared baseline data, pregnancy and perinatal complications, and preterm delivery outcomes between groups. Performed multiple logistics regression analysis to adjust for confounding factors. Finally, we compared early PTB outcome between different HBV DNA level groups. Results The incidence of preterm birth (gestation less than 37 weeks) was similar between the groups, early preterm birth (gestation less than 34 weeks) were significantly more among the HBV infection group than among the controls (1.6% VS. 0.8%; P = 0.003). After adjusting for confounding factors through logistics regression, HBV infection was found to be an independent early PTB risk factor gestation (adjusted odds ratio 1.770; 95% confidence interval [1.046–2.997]). The incidence of early PTB in < 500 group, 500 ~ 2.0 × 10e5 group and > 2.0 × 10e5 group was not statistically significant (P = 0.417). Conclusion HBV infection is an independent risk factor for early PTB, and the risk did not seem to be influenced by the levels of HBV DNA. Comprehensive programs focusing on pregnant women with HBV infection would reduce the incidence of adverse outcomes.

In this study, we assessed the effects of pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) on the pregnancy outcomes of women of advanced age using a back-propagation (BP) artificial neural network. We conducted a retrospective analysis on postpartum and hospital delivery data from 1,015 women of advanced maternal age (AMA) hospitalized at the Fujian Provincial Maternity and Children's Hospital from January to June, 2017. Pre-pregnancy overweight was found to increase the incidence of gestational diabetes mellitus (GDM), hypertensive disorders complicating pregnancy (HDCP) and fetal macrosomia. In addition, poor weight gain during pregnancy increased the chances of pre-term births (PTBs). Furthermore, excessive weight gain during pregnancy increased the incidence of macrosomia in women of AMA. On the whole, the findings of this study suggest that controlling the pre-pregnancy BMI and the GWG may reduce the incidence of adverse pregnancy outcomes in women of AMA. The BP neural network is suitable for the study of weight changes in this population.

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