Graduation Year

2015

Document Type

Open Access Senior Thesis

Degree Name

Bachelor of Arts

Department

W.M. Keck Science Department

Second Department

Biology

Reader 1

Lizellen La Follette

Reader 2

Sarah Gilman

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Rights Information

© 2014 Jenna A. Koblentz

Abstract

For most of the 20th century, the saying “once a cesarean, always a cesarean” was a rule in the United States. Today, the National Institutes of Health (NIH) opposes the dictum and urges women to consider trial of labor after cesarean (TOLAC). However, the factors that lead to a successful outcome remain unclear, as research continues to be conducted in hopes of creating a predictive model for vaginal birth after cesarean (VBAC) success.

The NIH’s request for more research in this area of obstetrics led to this retrospective cohort study of all TOLACs at Marin General Hospital (MGH) from 2000-2013. All labor trials were studied for patient demographics, details of labor, maternal and neonatal morbidities, insurance, and provider type. After confirming the quality of the data, verifying inclusion criteria and ignoring cases with missing data, a data set of 745 TOLACs with 13 explanatory variables of interest was prepared. A forward stepwise (Likelihood Ratio) binary logistic regression was run in IBM® SPSS® Statistics in order to create a model that could determine which variables were most predictive of delivery outcome in TOLAC patients.

Ultimately, seven variables were predictive and were included in the model. Of the seven, the most predictive variable in determining VBAC success was provider type. The model concluded that a woman’s odds of having a successful VBAC were almost four times greater if she began her delivery with a certified nurse midwife, than if she began her deliver with a physician (odds ratio 0.27, 95% CI 0.17-0.44; < 0.01). The results from this study mimic the results of other models, and introduce labor support as a key factor in predicting VBAC success.

Download

Share

COinS