New insights on vaginal birth after cesarean: can it be predicted?
OBJECTIVE
To evaluate existing vaginal birth after cesarean (VBAC) screening tools and to identify additional factors that may predict VBAC or failed trial of labor.


METHODS
Relevant studies were identified through MEDLINE, Database of Abstracts of Reviews of Effectiveness, and the Cochrane databases (1980-September 2009), and from recent systematic reviews, reference lists, reviews, editorials, web sites, and experts.


METHODS
Inclusion criteria limited studies to those of humans, written in English, studies conducted in the United States and developed countries, and those rated good or fair quality by the U.S. Preventive Services Task Force criteria. Studies of individual predictors were combined using a random effects model when the estimated odds ratios were comparable across included studies.


RESULTS
We identified 3,134 citations and reviewed 963 papers, of which 203 met inclusion criteria and were quality-rated. Twenty-eight provided evidence on predictors of VBAC and 16 presented information on scored models for predicting VBAC (or failed trial of labor). Six of the 11 scored models for predicting VBAC (or failed trial of labor) were validated by separated dataset, cross-validation, or both. Whereas accuracy remained high across all models for predicting VBAC, with predictive values ranging from 88% to 95%, accuracy for predicting failed trial of labor was low, ranging from 33% to 58%. Individual predictors including Hispanic ethnicity, African-American race, advanced maternal age, no previous vaginal birth history, birth weight heavier than 4 kg, and use of either augmentation or induction were all associated with reduced likelihood of VBAC.


CONCLUSIONS
Current scored models provide reasonable predictability for VBAC, but none provides consistent ability to identify women at risk for failed trial of labor. A scoring model is needed that incorporates known antepartum factors and can be adjusted for current obstetric factors and labor patterns if induction or augmentation is needed. This would allow women and clinicians to better determine individuals most likely to require repeat cesarean delivery.
Eden
Karen B
KB
From the Oregon Evidence-based Practice Center, Department of Medical Informatics and Clinical Epidemiology, Department of Obstetrics and Gynecology, School of Nursing, and Department of Public Health and Preventive Medicine, Oregon Health & Science University, Portland, Oregon.
McDonagh
Marian
M
Denman
Mary Anna
MA
Marshall
Nicole
N
Emeis
Cathy
C
Fu
Rongwei
R
Janik
Rosalind
R
Walker
Miranda
M
Guise
Jeanne-Marie
JM
D019540:Area Under Curve; D015992:Body Mass Index; D015897:Comorbidity; D005260:Female; D006801:Humans; D015233:Models, Statistical; D016017:Odds Ratio; D011247:Pregnancy; D011248:Pregnancy Complications / Q000453:epidemiology; D011256:Pregnancy Outcome; D018570:Risk Assessment; D014219:Trial of Labor; D016064:Vaginal Birth after Cesarean