Prediction of vaginal birth after cesarean delivery in Southeast China: a retrospective cohort study

Abstract

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.

Intrapartum ultrasound measurement of the lower uterine segment thickness in parturients with previous scar in labor: a cross-sectional study

“…The argument for the relationship between maternal and perinatal factors and the success of vaginal birth after cesarean section is conflicting regarding the factors reported in published studies. Examples of these factors include age, maternal education level, gestational age, parity, number of abortions, obesity, diabetes, hypertensive disorders in pregnancy, Bishop score, labor induction, previous vaginal birth before cesarean section, previous vaginal birth after a cesarean section, and the indications for the previous cesarean section [ 34 – 36 ]. The current study assessed the effect of maternal age groups, gestational age, BMI, inter pregnancy and inter delivery interval, and birth weight.…”

A predictive nomogram for a failed trial of labor after cesarean: A retrospective cohort study

“… 26 However, these standards might not be suitable for Chinese women. The only nomogram for a Chinese population included seven variables and showed a C‐index of 0.89 for the internal validation, 27 which could not be practical because of too many indicators. Meanwhile, the present nomogram was developed for a Chinese population with only three variables and a relatively reliable AUC (0.898–0.902).…”

Evaluation of factors that predict the success rate of trial of labor after the cesarean section

“…Whether weight gain during pregnancy affects TOLAC is currently unclear. Previous study has provided evidence that weight gain during pregnancy and maternal BMI both associate with successful VBAC [ 20 , 21 ]. Our results were consistent with a previous retrospective cohort study which emphasized that excessive weight gain during pregnancy was not a risk factor for failed TOLAC, even in obese patients [ 22 ].…”

“…A satisfactory prediction model could be clinically important to identify women with greater opportunities of a successful TOLAC. In a previous study, a nomogram model established had good performance at the high estimated probability of successful TOLAC for about 93% of women with an estimated ≥ 90% having a vaginal birth [ 21 ]. Although the probability in our study was relatively low, the TOLAC prediction model constructed here was useful to terminate the pregnancy in a timely manner, and to monitor closely during the labor process, which would increase the success rate of TOLAC and reduce the maternal and fetal complications related to cesarean section.…”