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Guise JM, Eden K, Emeis C, et al. Vaginal Birth After Cesarean: New Insights. Rockville (MD): Agency for Healthcare Research and Quality (US); 2010 Mar. (Evidence Reports/Technology Assessments, No. 191.)

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Vaginal Birth After Cesarean: New Insights.

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Appendix NDetailed Evaluation of Screening Tools for Predicting Vaginal Birth After Cesarean

Introduction

A brief description on the screening tools for predicting vaginal birth after cesarean (VBAC) is included in the text of the evidence report. Detailed description of the studies and analyses conducted on this topic is included in this appendix.

Screening Tools for Predicting Vaginal Birth After Cesarean

The purpose of a screening tool is to help providers and patients to better identify who will have a VBAC (and who is more likely to have a repeat cesarean delivery [RCD]). Two prospective cohort studies,1, 2 ten retrospective cohort studies3–12 and two case-control studies13, 14 that presented screening tools were identified. All included studies were either good or fair quality (Table N-1). These studies combined individual factors to predict the likelihood of VBAC (or cesarean delivery) when certain thresholds were reached. Predictive variables (historic, intrapartum or perinatal) of delivery route were first identified by univariate analyses. Significant variables (p≤0.05) were included in multiple logistic regression models and/or scored models.

Table N-1. Screening tools for predicting vaginal birth after cesarean.

Table N-1

Screening tools for predicting vaginal birth after cesarean.

In the strongest studies, the resulting models or scoring systems were then evaluated with a separate validation data set.15 Three of the scored models presented at the top of Table N-1 had one or more external validation studies (shaded) that tested the models with independent cohort data sets. The scored model by Flamm et al1 was externally validated by one retrospective cohort study.5 The scored model by Grobman et al2 was externally evaluated with a retrospective study.4 The Troyer model was externally validated by two retrospective studies.5, 11 In all validation studies, the scored model’s performance was similar to the originally reported performance. In a retrospective cohort study that evaluated three scored models using the same data set,1, 10, 16, Dinsmoor et al reported that all three models were accurate at predicting which women would have a VBAC but were not accurate at predicting who would have a RCD after a TOL. Using the three models, 50 percent of women with unfavorable risk factors (in these three models) had vaginal deliveries suggesting that other factors may be needed to identify women at risk for cesarean. A previous decision analysis of VBAC17 suggested that a scored model would be most useful clinically if it achieved a sensitivity and specificity over 85 percent5 which none of these tools achieved.

In four studies, a cross-validation approach was taken.1, 2, 7, 9 The data in these studies were randomly divided to create a score development group and a validation group. In a multi-center prospective cohort study by Flamm,1 48 percent of women were assigned VBAC estimates either below 60 percent or greater than 80 percent suggesting that almost half of the women gained new information after the screening. After being screened by the tool that included maternal age, prior vaginal delivery history, prior cesarean delivery indication, cervical effacement and dilation, 18 percent of women in the validation group had a less than 60 percent likelihood of VBAC which might discourage some of these women from attempting a TOL. Thirty percent of the women in the validation group had an estimated 87% likelihood of VBAC, which might prove to encourage more of these women to attempt a TOL.

In a retrospective study designed to predict risk of cesarean delivery for women at 40 to 42 weeks gestational age (GA),9 16.5 percent of women were assigned estimates of cesarean greater than 40 percent based on a screening tool that included maternal age, GA, induction method and gender of the infant. In the same study, 36 percent of women were assigned estimates of cesarean less than 20 percent, again suggesting that more than half of women gained knowledge after being screened.

The remaining two cross-validated studies2, 7 evaluated tools based on factors that are known before labor begins in women who delivered at term. In a retrospective study of a statewide population database, women in this study with any of the following—recurrent cesarean delivery indication, history of a macrosmic infant, or current anemia—had a likelihood of VBAC of less than 55 percent. In the prospective cohort study,2 women were screened based on maternal age, pre-pregnancy BMI, race, ethnicity, prior vaginal delivery history, and recurring indication for cesarean delivery. These factors were then used to create a graphical nomogram. This nomogram not only provided a point estimate for VBAC but also 95 percent confidence interval around the estimate. The authors present four case studies using the nomogram (two in which the women are assigned estimates in the expected range of 60 to 80 percent; and two in which the estimates are outside the quoted range). In one case, a 25 year-old African American woman of pre-pregnancy body mass index (BMI) of 25, with a prior vaginal delivery and a prior VBAC, was assigned an estimate of VBAC of 92.4 percent (95% CI: 91.1 to 93.6 percent). In a second case, a 35 year-old white woman with a pre-pregnancy BMI of 30, with no prior vaginal delivery and a recurring cesarean indication received an estimate of 49 percent (95% CI: 46.1 to 51.9 percent).

In evaluating the performance of screening tools, the studies report inconsistent accuracy for predicting overall delivery route (Gonen, 2004: 84.3 percent;6 Weinstein, 1996: 80 percent;12 Macones, 2001: 71.9 percent;13 Jakobi, 1993: 68 percent8). It is important to note in looking at Table N-1, that most studies are designed to predict VBAC. The ability of these tools to identify women best suited for VBAC is noted by sensitivity levels12 and positive predictive values.6, 8 The only study to report sensitivity, the proportion of women who delivered vaginally who were correctly categorized to deliver vaginally, achieved a sensitivity of 85.5 percent.12 Two studies reported high positive predictive values for VBAC (Jakobi, 1993: 94.5 percent8 and Gonen, 2004: 88.2 percent6), meaning that about 90 percent of women predicted to have a vaginal birth from the tools actually had a vaginal birth. These same studies were not as accurate at predicting cesarean delivery with predictive values (for cesarean) ranging from 33.3 percent8 to 58.1 percent.6

Other studies are modeled to predict cesarean delivery (RCD after a TOL).9, 13, 14 In these studies high levels of specificity mean the model is reasonable at estimating the proportion of women who had a VBAC who were identified to have a VBAC.9, 13, 14 Specificity across these studies ranged from 65 percent13 to 89 percent.9 These studies had inconsistent ability to identify women at risk for cesarean as shown by sensitivity levels ranging from 3814 to 77 percent.13

Three of the studies evaluated the screening tool performance by examining the area under the Receiver Operating Characteristic curve (AUROC).2, 9, 13 This curve is a plot of the true-positive rate (sensitivity) against the false-positive rate (1-specificity). Screening tools with more area under the ROC are considered better tests. The AUROC ranged from 699 to 77 percent.13

Summary of Screening Tools to Predict Vaginal Birth After Cesarean

Since the last VBAC report,20 five new scored models have been created and evaluated to identify women for VBAC (or for RCD).2, 3, 6, 7, 9 Two of the studies created scored tools that can be used the prenatal setting.2, 7 All scored models provide reasonable ability to identify women who are good candidates for VBAC but none have discriminating ability to consistently identify women who are at risk for cesarean.

References

1.
Flamm BL, Geiger AM. Vaginal birth after cesarean delivery: an admission scoring system. Obstetrics & Gynecology. 1997;90(6):907–910. [PubMed: 9397100]
2.
Grobman WA, Lai Y, Landon MB, et al. Development of a nomogram for prediction of vaginal birth after cesarean delivery. Obstetrics & Gynecology. 2007;109(4):806–812. [PubMed: 17400840]
3.
Bujold E, Blackwell SC, Hendler I, Berman S, Sorokin Y, Gauthier RJ. Modified Bishop's score and induction of labor in patients with a previous cesarean delivery. American Journal of Obstetrics & Gynecology. 2004;191(5):1644–1648. [PubMed: 15547536]
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Costantine MM, Fox K, Byers BD, et al. Validation of the prediction model for success of vaginal birth after cesarean delivery. Obstetrics and Gynecology. 2009;114(5):1029–1033. [PubMed: 20168103]
5.
Dinsmoor MJ, Brock EL. Predicting failed trial of labor after primary cesarean delivery. Obstetrics & Gynecology. 2004;103(2):282–286. [PubMed: 14754696]
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Gonen R, Tamir A, Degani S, Ohel G. Variables associated with successful vaginal birth after one cesarean section: a proposed vaginal birth after cesarean section score. American Journal of Perinatology. 2004;21(8):447–453. [PubMed: 15580540]
7.
Hashima JN, Guise J-M. Vaginal birth after cesarean: a prenatal scoring tool. American Journal of Obstetrics & Gynecology. 2007;196(5):e22–23. [PubMed: 17466668]
8.
Jakobi P, Weissman A, Peretz BA, Hocherman I. Evaluation of prognostic factors for vaginal delivery after cesarean section. Journal of Reproductive Medicine. 1993;38(9):729–733. [PubMed: 8254598]
9.
Smith GCS, White IR, Pell JP, Dobbie R. Predicting cesarean section and uterine rupture among women attempting vaginal birth after prior cesarean section. PLoS Medicine/Public Library of Science. 2005;2(9):e252. [PMC free article: PMC1201366] [PubMed: 16146414]
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Troyer LR, Parisi VM. Obstetric parameters affecting success in a trial of labor: designation of a scoring system. American Journal of Obstetrics & Gynecology. 1992;167(4 Pt 1):1099–1104. [PubMed: 1415398]
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Vinueza CA, Chauhan SP, Barker L, Hendrix NW, Scardo JA. Predicting the success of a trial of labor with a simple scoring system. Journal of Reproductive Medicine. 2000;45(4):332–336. [PubMed: 10804491]
12.
Weinstein D, Benshushan A, Tanos V, Zilberstein R, Rojansky N. Predictive score for vaginal birth after cesarean section. American Journal of Obstetrics & Gynecology. 1996;174(1 Pt 1):192–198. [PubMed: 8572005]
13.
Macones GA, Hausman N, Edelstein R, Stamilio DM, Marder SJ. Predicting outcomes of trials of labor in women attempting vaginal birth after cesarean delivery: a comparison of multivariate methods with neural networks. American Journal of Obstetrics & Gynecology. 2001;184(3):409–413. [PubMed: 11228495]
14.
Pickhardt MG, Martin JN Jr, Meydrech EF, et al. Vaginal birth after cesarean delivery: are there useful and valid predictors of success or failure? American Journal of Obstetrics & Gynecology. 1992;166(6 Pt 1):1811–1815. discussion 1815–1819. [PubMed: 1615990]
15.
Altman D. Prognosis and prognostic research: validating prognostic model. BMJ. 2009;338:1430–1435. [PubMed: 19477892]
16.
Alamia VJ, Meyer BA, Selioutski O, Vohra N. Can a VBAC scoring system predict uterine rupture in patients attempting a trial of labor?. Paper presented at: ACOG 47th Annual Clinical Meeting; May 19, 1999.
17.
Macones GA. The utility of clinical tests of eligibility for a trial of labour following a caesarean section: a decision analysis. British Journal of Obstetrics & Gynaecology. 1999;106(7):642–646. [PubMed: 10428518]
18.
Flamm BL. Once a cesarean, always a controversy. Obstetrics & Gynecology. 1997;90(2):312–315. [PubMed: 9241315]
19.
Grobman WA, Gilbert S, Landon MB, et al. Outcomes of induction of labor after one prior cesarean. Obstetrics & Gynecology. 2007;109(2 Pt 1):262–269. [PubMed: 17267822]
20.
Guise JM, McDonagh MS, Hashima J, et al. Vaginal birth after cesarean (VBAC) Evidence Report: Technology Assessment. 2003;(71):1–8. [PubMed: 12696509]
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