Development and Validation of Predictive Models for Vaginal Birth After Cesarean Delivery in China

Abstract

BACKGROUND The rate of delivery by cesarean section is rising in China, where vaginal birth after cesarean (VBAC) is in its early stages. There are no validated screening tools to predict VBAC success in China. The objective of this study was to identify the variables predicting the likelihood of successful VBAC to create a predictive model. MATERIAL AND METHODS This multicenter, retrospective study included 1013 women at ≥28 gestational weeks with a vertex singleton gestation and 1 prior low-transverse cesarean from January 2017 to December 2017 in 11 public tertiary hospitals within 7 provinces of China. Two multivariable logistic regression models were developed: (1) at a first-trimester visit and (2) at the pre-labor admission to hospital. The models were evaluated with the area under the receiver operating characteristic curve (AUC) and internally validated using k-fold cross-validation. The pre-labor model was calibrated and a graphic nomogram and clinical impact curve were created. RESULTS A total of 87.3% (884/1013) of women had successful VBAC, and 12.7% (129/1013) underwent unplanned cesarean delivery after a failed trial of labor. The AUC of the first-trimester model was 0.661 (95% confidence interval [CI]: 0.61-0.712), which increased to 0.758 (95% CI: 0.715-0.801) in the pre-labor model. The pre-labor model showed good internal validity, with AUC 0.743 (95% CI: 0.694-0.785), and was well calibrated. CONCLUSIONS VBAC provides women the chance to experience a vaginal delivery. Using a pre-labor model to predict successful VBAC is feasible and may help choose mode of birth and contribute to a reduction in cesarean delivery rate.

Figure 1

Flow diagram of the patients’ enrollment process.

Figure 2

The receiver operating characteristic (ROC) curves of the 4 models. The first trimester period: (A–C); 5-fold cross validation of full model; 5-fold cross validation of first trimester model. The pre-labor period: (D–F); 5-fold cross validation of full model; 5-fold cross validation of pre-labor model.

Figure 3

The calibration curve of the pre-labor model.

Figure 4

The nomogram of the pre-labor model. The nomogram converts each risk predictor into a 0 to 100 scale that is proportional to the derived adjusted log odds. These points are added across predictors to derive the „total points”, which are converted to predict the probabilities of vaginal birth.

Figure 5

The clinical impact curve for the pre-labor model. Of 1000 patients, the red solid line shows the total number who would be deemed high risk for each risk threshold. The blue dashed line shows how many of those would be true positives (cases).