Development of a Nomogram for Prediction of Vaginal Birth After Cesarean Delivery

Abstract

In Brief OBJECTIVE: To develop a model based on factors available at the first prenatal visit that predicts chance of successful vaginal birth after cesarean delivery (VBAC) for individual patients who undergo a trial of labor. METHODS: All women with one prior low transverse cesarean who underwent a trial of labor at term with a vertex singleton gestation were identified from a concurrently collected database of deliveries at 19 academic centers during a 4-year period. Using factors identifiable at the first prenatal visit, we analyzed different classification techniques in an effort to develop a meaningful prediction model for VBAC success. After development and cross-validation, this model was represented by a graphic nomogram. RESULTS: Seven-thousand six hundred sixty women were available for analysis. The prediction model is based on a multivariable logistic regression, including the variables of maternal age, body mass index, ethnicity, prior vaginal delivery, the occurrence of a VBAC, and a potentially recurrent indication for the cesarean delivery. After analyzing the model with cross-validation techniques, it was found to be both accurate and discriminating. CONCLUSION: A predictive nomogram, which incorporates six variables easily ascertainable at the first prenatal visit, has been developed that allows the determination of a patient-specific chance for successful VBAC for those women who undertake trial of labor. LEVEL OF EVIDENCE: II Using six patient characteristics easily ascertainable at the first prenatal visit, we have developed a model for prediction of vaginal birth after cesarean for women who undergo a trial of labor.