Can a prediction model for vaginal birth after cesarean also predict the probability of morbidity related to a trial of labor?

Abstract

Objective: The objective of the study was to determine whether a model for predicting vaginal birth after cesarean (VBAC) can also predict the probabilty of morbidity associated with a trial of labor (TOL). Study Design: Using a previously published prediction model, we categorized women with 1 prior cesarean by chance of VBAC. Prevalence of maternal and neonatal morbidity was stratfied by probability of VBAC success and delivery approach. Results: Morbidity became less frequent as the predicted chance of VBAC increased among women who underwent TOL (P < .001) but not elective repeat cesarean section (ERCS) (P > .05). When the predicted chance of VBAC was less than 70%, women undergoing a TOL were more likely to have maternal morbidity (relative risk [RR], 2.2; 95% confidence interval [CI], 1.5-3.1) than those who underwent an ERCS; when the predicted chance of VBAC was at least 70%, total maternal morbidity was not different between the 2 groups (RR, 0.8; 95% CI, 0.5-1.2). The results were similar for neonatal morbidity. Conclusion: A prediction model for VBAC provides information regarding the chance of TOL-related morbidity and suggests that maternal morbidity is not greater for those women who undergo TOL than those who undergo ERCS if the chance of VBAC is at least 70%. © 2009 Mosby, Inc. All rights reserved.