Models for predicting vaginal birth after cesarean section: scoping review.

Abstract

Background: Women who are pregnant again after a prior cesarean section are faced with the choice between a vaginal trial and another cesarean section. Vaginal delivery is safer for mothers and babies, but face the risk of trial labor failure. Predictive models can evaluate the success rate of vaginal trial labor after cesarean section, which will help obstetricians and pregnant women choose the appropriate delivery method.

Objective: To review the existing prediction models of vaginal delivery after cesaean.

Methods: Seven databases, including CNKI, Wanfang Data, Chinese Science and Technology Periodical Database, China Biomedical Literature Database, PubMed, Embase, and Web of Science, were searched for studies on the predictive model of VBAC from inception to July 20, 2022. Two researchers independently screened the literature and extracted the data. The risk of bias and applicability of the included researches was evaluated using the Prediction model Risk of Bias Assessment Tool.

Results: Twenty-six studies that covered 26 models were included. The overall property of the included models was good, but validation of the included models was insufficient. The methodological quality of the included studies was generally low, with 3 studies rated as having a low risk of bias and 23 studies rated as having a high risk of bias. The main predictors in the models were the Bishop score, history of vaginal delivery, neonatal weight, maternal age, and BMI.

Conclusions: Although a variety of prediction models have been developed globally, the methodology of these studies has limitations and the models have not been adequately validated. In the future, more prospective and high-quality research is needed to develop visual models to serve clinical work more effectively and conveniently. Obstetricians or midwives could use predictive models to help a woman choose the right delivery method.