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

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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%.

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