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The purpose of this study was to develop a model that predicts individual-specific risk of uterine rupture during an attempted vaginal birth after cesarean delivery.
Women with 1 previous low-transverse cesarean delivery who underwent a trial of labor with a term singleton were identified in a concurrently collected database of deliveries that occurred at 19 academic centers during a 4-year period. We analyzed different classification techniques in an effort to develop an accurate prediction model for uterine rupture.
Of the 11,855 women who were available for analysis, 83 women (0.7%) had had a uterine rupture. The optimal final prediction model, which was based on a logistic regression, included 2 variables: any previous vaginal delivery (odds ratio, 0.44; 95% CI, 0.27-0.71) and induction of labor (odds ratio, 1.73; 95% CI, 1.11-2.69). This model, with a c-statistic of 0.627, had poor discriminating ability and did not allow the determination of a clinically useful estimate of the probability of uterine rupture for an individual patient.
Factors that were available before or at admission for delivery cannot be used to predict accurately the relatively small proportion of women at term who will experience a uterine rupture during an attempted vaginal birth after cesarean delivery.
Even though uterine rupture during an attempted vaginal birth after cesarean delivery (VBAC) occurs infrequently, the potential ramifications (such as hysterectomy and neonatal neurodevelopmental disability or death) are of great clinical significance.
Correspondingly, when pregnant women with a previous cesarean delivery are confronted with the decision of whether to attempt a trial of labor (TOL), the possibility of a uterine rupture is particularly relevant to their decision-making process. In an effort to aid this process, investigators have studied the different factors that are associated with the occurrence of uterine rupture.
The identification of factors that are associated with uterine rupture may allow physicians to provide general guidance to a woman regarding her chance of uterine rupture during a TOL. However, even though certain factors may be associated with uterine rupture, it does not follow necessarily that those associations can be combined to allow accurate prediction of the probability of a uterine rupture for an individual woman. The development of such a prediction model could further aid women and their caregivers in counseling regarding the decision to attempt a VBAC.
attempted to develop a model for the prediction of uterine rupture but were unable to construct an accurate one. We recently demonstrated a methodologic technique for developing a graphic nomogram that can be used to predict accurately the probability of another important component of the decision to undergo a TOL, namely, the chance of achieving a vaginal delivery.
We hypothesized that this technique also could allow the development of an accurate prediction model for uterine rupture. In this study, we investigated that possibility.
Between 1999 and 2002, 19 academic medical centers that were participating in the National Institute of Child Health and Human Development's Maternal-Fetal Medicine Units Network participated in a study of pregnant women with a previous cesarean delivery. At each center, trained and certified research nurses concurrently identified women who were admitted for delivery and who had a history of cesarean delivery. For those women who were identified, the charts were abstracted for demographic data, medical and obstetric history, and intrapartum and postpartum events. Uterine rupture was defined as disruption of the uterine muscle and visceral peritoneum or a uterine muscle separation with extension to the bladder or broad ligament that was found at the time of cesarean delivery or laparotomy. Asymptomatic dehiscences of the uterine scar were not included in the primary outcome of interest. Reports of uterine rupture underwent secondary review at each clinical center and centrally to ensure accuracy of diagnosis. Further details of the methods of this study have been described previously.
Approval for the study was obtained from the Institutional Review Board of each institution.
This analysis concerns those women in the registry with a vertex singleton gestation and 1 previous low-transverse cesarean delivery who underwent a TOL after 36 weeks 6 days of gestation. Women with an antepartum intrauterine fetal death were excluded. For development of the predictive model, patient factors were considered for inclusion if they could be ascertained at the first prenatal visit or on admission for the TOL. These factors included demographic variables (maternal age, ethnicity, body mass index), variables related to the previous cesarean delivery (recurrent indication, length of time since cesarean), variables related to previous obstetric history (any previous vaginal delivery, number of previous vaginal deliveries, any vaginal delivery subsequent to cesarean delivery, previous preterm vaginal deliveries only, maximal birthweight of a previous child), variables that were reflective of medical conditions (pregestational or gestational diabetes mellitus, asthma, chronic hypertension, renal or heart disease, or a connective tissue disorder), and intrapartum variables that were ascertainable at the start of labor (induction of labor, medical indication for induction of labor, cervical examination on admission, estimated gestational age at delivery, pregnancy-induced hypertension). Recurrent indication for a cesarean delivery was defined as arrest of dilation or descent as the indication for the previous cesarean delivery. Marginal exploratory analysis was performed on continuous variables (such as maternal age and body mass index) to analyze whether these were best represented in the model as continuous or categoric variables. Variables that were related to events in previous cesarean deliveries (such as type of uterine closure and presence of infection) were not collected in the registry and accordingly were not included in the predictive modeling.
In the initial phase of the development of the predictive model, the original data set was divided randomly and approximately equally into a training set and a test set. The classification errors were estimated through a cross-validation procedure. Once the training set was used to identify predictive factors for uterine rupture and to build the predictive model, the independent test set was used to estimate the classification errors. We first built a logistic regression model based on the training set with stepwise variable selection and then used the test set to evaluate the model.
A receiver operating characteristic (ROC) curve was generated from this regression and a c-statistic (representing the “area under the curve”) was determined. In addition to the logistic regression, other classification techniques (such as classification tree, random forest, support vector machine, and boosting analysis) were evaluated to determine whether they provided a more accurate model.
These techniques, however, did not improve predictive capability.
The logistic regression model that was developed from the training set was validated in the following manner: After application of the regression model to the test set, the predicted probabilities of uterine rupture were partitioned into 10 groups (ie, 0-0.5%, 0.5%-1.0%,…4.5%-5%). The mid points of these probability ranges (eg, 0.25%, 0.75%) were used to represent these groups. In each group, the proportion of women with a uterine rupture was calculated to estimate the empiric probability of uterine rupture. The scatter plots of the predicted and empiric VBAC probabilities were connected smoothly to form a curve. The ideal validation would generate a 45-degree straight line. Corresponding 95% CIs for the curve were calculated from the normal approximation. The validation curve provides additional and important insight into the predictive strength of a model that the ROC curve alone cannot provide. The ROC curve indicates how effectively one can predict whether an individual will or will not have a uterine rupture. Yet, even if a prediction model could not predict this dichotomous outcome accurately, it may allow a good estimation of an individual's specific risk for an outcome. It is the validation curve that provides insight into this estimation of specific risk. SAS software (version 8.2; SAS Institute, Cary, NC) was used for analysis.
During the study period, 11,855 women met inclusion criteria. Uterine rupture occurred in 83 of these women (0.7%). Descriptive characteristics of the population are presented in Table 1. Eleven thousand six hundred seventy-six women had a complete set of values for all variables under consideration that comprised the final population that was used for analysis.
TABLE 1Descriptive characteristics of the study population
The following specific regression equation was determined to best predict the probability of uterine rupture: exp (w)/[1 + exp(w)], where w equals − 4.81 − 0.82 (previous vaginal delivery) + 0.55 (induction of labor). As can be seen in this equation, only 2 factors, namely the occurrence of a previous vaginal delivery or induction of labor, were found to contribute to an optimal final prediction model. The addition of further factors did not improve materially the ability to predict uterine rupture accurately. Table 2 shows the odds ratio and 95% CI that were derived from the regression equation for each predictive factor. As indicated by the odds ratios, a history of a previous vaginal delivery was associated with a lower probability of uterine rupture, and induction of labor was associated with a higher probability of uterine rupture. The corresponding ROC curve, with an area under the curve of 0.627 (95% CI, 0.568-0.686) is presented in Figure 1.
TABLE 2Factors that are associated with uterine rupture in multivariable logistic regression
Any previous vaginal delivery
Induction of labor
Grobman. Prediction of uterine rupture associated with attempted vaginal birth after cesarean delivery. Am J Obstet Gynecol 2008.
The cross-validation procedure showed that the performance of the model on the test set was similar to that originally determined from the training set, with an area under the curve that was determined to be 0.603. Figure 2 compares the predicted rates of uterine rupture with the empiric probabilities of uterine rupture for women in the test set. The estimated curve and its 95% confidence band demonstrate 2 important features. First, the predicted probabilities of uterine rupture do not reflect accurately the empiric probabilities that the patient experienced. Second, the 95% CIs are relatively wide, given the point probability of uterine rupture. For example, an empiric probability of rupture of 1.3% has a 95% CI that ranges from 0.6%-1.8%. Although the absolute difference in these frequencies is small, the range of the CI is nearly 100% of the point estimate. Thus, the prediction model is neither accurate nor discriminating. Given that an adequate prediction model could not be obtained, a graphic nomogram
was not constructed, because it would have little clinical value.
Faced with the prospect of undergoing delivery after a low-transverse cesarean delivery, women must choose an intended route of delivery. The decision to undergo a TOL depends on how women balance the benefits and risks of that trial. On 1 hand, women can achieve a vaginal delivery, an outcome with a shorter recovery, and fewer long-term reproductive consequences than a cesarean delivery.
If women could be provided with individual-specific probabilities for the benefits and risks of a TOL, the counseling provided to them and their ability to make the most informed decision would be enhanced. To date, however, a predictive model has not been developed that allows the discernment of individual-specific risks of uterine rupture.
Recently, we have demonstrated how a technique that uses a graphic nomogram can be used to establish and validate a predictive model for the probability of achieving a VBAC after a TOL.
The results of this approach suggested that it could be useful equally in the establishment of a predictive model for individual-specific risks of uterine rupture; we attempted to develop such a model. In an effort to optimize the model, we incorporated patient characteristics that were available both at the initiation of prenatal care and at the time of admission to labor and delivery. However, this approach still failed to yield a model that could establish an accurate estimate of uterine rupture and discriminate 1 woman from another with regard to this risk.
Their method was somewhat different than ours because they used a case-control design and evaluated a logistic regression only. Also, they included data from women with > 1 previous cesarean delivery and women who were having a preterm delivery. By studying women at term with only 1 previous cesarean delivery, we hoped that the greater homogeneity of the group and the reduced potential for variable interactions would enhance our model's predictive accuracy. Nevertheless, our results were similar to theirs and confirm their findings. In both studies, the predictive models that were developed revealed previous vaginal delivery as a factor to be associated with a decreased risk of uterine rupture and labor induction as a factor to be associated with an increased risk of uterine rupture. The model of Macones et al also suggested that increasing maternal age was associated with an increased uterine rupture risk. Regardless of the similarities and differences, neither model ultimately could provide an accurate prediction of this relatively uncommon event.
The inability of the present investigation to establish an accurate prediction model for uterine rupture raises the concern that such a model may not be achievable. We had the benefit of a data set that was gathered over 4 years at multiple academic sites with large delivery volumes. Such a collection of concurrently collected data from women who were undertaking a TOL is unlikely to be accumulated again. Also, we evaluated multiple types of statistical analyses (eg, logistic regression, random forest analysis) to discern the optimal statistical basis for a model, and we used a technique that has a demonstrated ability to establish a usable predictive model for certain outcomes.
The difficulty in achieving a useful prediction model highlights the fact that associations between exposures and outcomes cannot always be translated into an accurate prediction of these outcomes. Several authors, including in the present study, have demonstrated that multiple factors may be associated with uterine rupture.
Yet, because these associations are not of great magnitude and the occurrence of uterine rupture is relatively uncommon, these associations cannot be translated into accurate person-specific risks. As shown in the results of our analysis, although a “risk” can be calculated, this probability has a CI that is so wide and a potential deviation from the observed risk that is so large that a truly accurate prediction cannot be achieved.
These results do not imply that a woman who considers a TOL can be given no information regarding uterine rupture. The associations that do exist allow at least population-level insight into factors that may decrease or increase a woman's risk for a uterine rupture. Yet, in contrast to the prediction of successful VBAC, these associations cannot be used to provide an accurate woman-specific probability of uterine rupture.
The authors thank the following people for their contribution to the manuscript: protocol/data management and statistical analysis (Elizabeth Thom, PhD; Sharon Gilbert, MS, MBA), protocol development and coordination between clinical research centers (Francee Johnson, BSN; Julia Gold BSN/APN), and manuscript oversight (Alan M. Peaceman, MD). In addition to the authors, the following persons are members of the National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network: Ohio State University: J. Iams, F. Johnson, S. Meadows, H. Walker; University of Alabama at Birmingham: J. Hauth, A. Northen, S. Tate; University of Texas Southwestern Medical Center: S. Bloom, J. Gold, D. Bradford; University of Utah: M. Belfort, F. Porter, B. Oshiro, K. Anderson, A. Guzman; University of Chicago: J. Hibbard, P. Jones, M. Ramos-Brinson, M. Moran, D. Scott; University of Pittsburgh: K. Lain, M. Cotroneo, D. Fischer, M. Luce; Wake Forest University: P. Meis, M. Swain, C. Moorefield, K. Lanier, L. Steele; Thomas Jefferson University: A. Sciscione, M. DiVito, M. Talucci, M. Pollock; Wayne State University: M. Dombrowski, G. Norman, A. Millinder, C. Sudz, B. Steffy; University of Cincinnati: T. Siddiqi, H. How, N. Elder; Columbia University: F. Malone, M. D'Alton, V. Pemberton, V. Carmona, H. Husami; Brown University: H. Silver, J. Tillinghast, D. Catlow, D. Allard; Northwestern University: A. Peaceman, M. Socol, D. Gradishar, G. Mallett; University of Miami, Miami, FL: G. Burkett, J. Gilles, J. Potter, F. Doyle, S. Chandler; University of Tennessee: W. Mabie, R. Ramsey; University of Texas at San Antonio: D. Conway, S. Barker, M. Rodriguez; University of North Carolina: K. Moise, K. Dorman, S. Brody, J. Mitchell; University of Texas at Houston: L. Gilstrap, M. Day, M. Kerr, E. Gildersleeve; Case Western Reserve University: P. Catalano, C. Milluzzi, B. Slivers, C. Santori; The George Washington University Biostatistics Center: E. Thom, S. Gilbert, H. Juliussen-Stevenson, M. Fischer; National Institute of Child Health and Human Development: D. McNellis, K. Howell, S. Pagliaro.
Maternal and perinatal outcomes associated with a trial of labor after prior cesarean delivery.
Supported by grants from the National Institute of Child Health and Human Development (HD21410, HD21414, HD27860, HD27861, HD27869, HD27905, HD27915, HD27917, HD34116, HD34122, HD34136, HD34208, HD34210, HD40500, HD40485, HD40544, HD40545, HD40560, HD40512, and HD36801).
Cite this article as: Grobman WA, Lai Y, Landon MB, et al. Prediction of uterine rupture associated with attempted vaginal birth after cesarean delivery. Am J Obstet Gynecol 2008;199:30.e1-30.e5.