Establishment and validation of a prediction model for vaginal delivery after cesarean and its pregnancy outcomes—Based on a prospective study. (November 2019) - Lin, Jing; Hou, Yanyan; Ke, Yiling; Zeng, Weijian; Gu, Wei
Abstract: Objective: To explore factors that can be used to predict successful vaginal births after cesarean (VBAC) and its outcome. Methods: This is a prospective study involving women with a previous low-segment cesarean section, singleton pregnancy and cephalic presentation who desire for vaginal trial delivery. Delivery modes were observed and the pregnancy outcomes were followed up. The data were analyzed to identify the factors associated with the success of vaginal births after cesarean (VBAC). Then, there were elaborated the models, and their predictive capacity was determined by receiver-operator curve (ROC). Results: The multivariate logistic regression showed Bishop's score and spontaneous labour independently influenced vaginal births after cesarean (VBAC) success. The prediction model is established and validated. The fitting degree and prediction accuracy of the model is good. The vaginal births after cesarean (VBAC) group had less postpartum hemorrhage (Median 270 ml vs. 300 ml, P < 0.05), a lower puerperal infection rate (1.62% vs 5.88%, P < 0.01), and shorter postpartum hospitalization (Median 2 days vs. 3 days, P < 0.01) than the trial of labor after cesarean (TOLAC)-failure groups. It also had less postpartum hemorrhage (Median 270 ml vs. 320 ml, P < 0.01), a lower puerperal infection rate (1.62% vs 6.23%, P < 0.05), and shorter postpartum hospitalization (Median 2 days vs. 3 days, P < 0.01) than the elective repeat cesarean section (ERCS) groups. The use of labor analgesia in the vaginal births after cesarean (VBAC) group had no effect on pregnancy outcomes. Conclusion: The predictive factors are conducive to making rational choices about delivery mode and should improve pregnancy outcomes.