External validation of a prediction model on vaginal birth after caesarean in a The Netherlands: a prospective cohort study.

OBJECTIVES: Discussing the individual probability of a successful vaginal birth after caesarean (VBAC) can support decision making. The aim of this study is to externally validate a prediction model for the probability of a VBAC in a Dutch population. METHODS: In this prospective cohort study in 12 Dutch hospitals, 586 women intending VBAC were included. Inclusion criteria were singleton pregnancies with a cephalic foetal presentation, delivery after 37 weeks and one previous caesarean section (CS) and preference for intending VBAC. The studied prediction model included six predictors: pre-pregnancy body mass index, previous vaginal delivery, previous CS because of non-progressive labour, Caucasian ethnicity, induction of current labour, and estimated foetal weight ≥90th percentile. The discriminative and predictive performance of the model was assessed using receiver operating characteristic curve analysis and calibration plots. RESULTS: The area under the curve was 0.73 (CI 0.69-0.78). The average predicted probability of a VBAC according to the prediction model was 70.3% (range 33-92%). The actual VBAC rate was 71.7%. The calibration plot shows some overestimation for low probabilities of VBAC and an underestimation of high probabilities. CONCLUSIONS: The prediction model showed good performance and was externally validated in a Dutch population. Hence it can be implemented as part of counselling for mode of delivery in women choosing between intended VBAC or planned CS after previous CS.

Practice variation of vaginal birth after cesarean and the influence of risk factors at patient level: a retrospective cohort study.

INTRODUCTION: Large practice variation exists in mode of delivery after cesarean section, suggesting variation in implementation of contemporary guidelines. We aim to evaluate this practice variation and to what extent this can be explained by risk factors at patient level. MATERIAL AND METHODS: This retrospective cohort study was performed among 17 Dutch hospitals in 2010. Women with one prior cesarean section without a contraindication for a trial of labor were included. We used multivariate logistic regression analysis to develop models for risk factor adjustments. One model was derived to adjust the elective repeat cesarean section rates; a second model to adjust vaginal birth after cesarean rates. Standardized rates of elective repeat cesarean section and vaginal birth after cesarean per hospital were compared. Pseudo-R2 measures were calculated to estimate the percentage of practice variation explained by the models. Secondary outcomes were differences in practice variation between hospital types and the correlation between standardized elective repeat cesarean section and vaginal birth after cesarean rates. RESULTS: In all, 1068 women had a history of cesarean section, of whom 71% were eligible for inclusion. A total of 515 women (67%) had a trial of labor, of whom 72% delivered vaginally. The elective repeat cesarean section rate at hospital level ranged from 6 to 54% (mean 29.8, standard deviation 11.8%). Vaginal birth after cesarean rates ranged from 50 to 90% (mean 71.8%, standard deviation 11.1%). More than 85% of this practice variation could not be explained by risk factors at patient level. CONCLUSION: A large practice variation exists in elective repeat cesarean section and vaginal birth after cesarean rates that can only partially be explained by risk factors at patient level.

Current predictors for morbidity regarding choice of birth after a previous caesarean section, show poor predictive value in prediction modelling.

INTRODUCTION: After a previous caesarean section, morbidity in the subsequent delivery in general is considered to depend on the probability of a vaginal birth after caesarean. However counselling could be improved by adding individualized probability of serious morbidity following either trial of labour or elective repeat caesarean section. The objective of this study was to develop prediction models for morbidity for both a repeat caesarean section and a trial of labor for a Dutch population. MATERIAL AND METHODS: In this cohort study, data were joined from three previous studies (SIMPLE 1, SIMPLE 2 and SIMPLE 2-implementation study). A cohort of 2592 women with one previous caesarean section and a singleton pregnancy who delivered ≥37 weeks, without a contraindication for vaginal delivery was formed. Maternal morbidity was defined as postpartum hemorrhage, blood transfusion, uterine rupture, ICU admittance or death. Neonatal morbidity was defined as asphyxia, NICU-admittance or death. Potential predictors for morbidity were chosen based on literature and expert opinion. Logistic regression was used to develop the models. Internal validation was intended using bootstrapping techniques. Main outcome measures were predictors for morbidity and for validation of the model we used the area under the receiver operating characteristic curve for discriminative capacity and calibration for accuracy. RESULTS: In 324 out of the 2592 cases (12.7 %) maternal or fetal complications occurred. In general total morbidity was higher in women choosing TOL as compared to ERCS (p < 0.001). The performance of the several developed models was insufficient, the area under the receiver operating characteristic curve did not rise above 0.6. Due to poor model performance, before correction for overfitting, interval validation was not conducted. CONCLUSION: In this large cohort, developing a Dutch population based prediction model that aimed to improve counselling on the mode of delivery, by predicting individual chances of morbidity for different delivery modes was not possible, due to lack of performance. Further study could be directed to cut off on VBAC success rates to a more general advice regarding the safest mode of delivery.

The effect of the use of a decision aid with individual risk estimation on the mode of delivery after a caesarean section: A prospective cohort study.

OBJECTIVE: After one previous caesarean section (CS), pregnant women can deliver by elective repeat CS or have a trial of labor which can end in a vaginal birth after caesarean (VBAC) or an unplanned CS. Despite guidelines describing women's rights to make an informed choice, trial of labor and VBAC rates vary greatly worldwide. Many women are inadequately informed due to caregivers' fear of an increase in CS rates in a high VBAC rate setting. We compared counseling with a decision aid (DA) including a prediction model on VBAC to care as usual. We hypothesize that counselling with the DA does not decrease VBAC rates. In addition, we aimed to study the effects on unplanned CS rate, patient involvement in decision-making and elective repeat CS rates. METHODS: We performed a prospective cohort study. From 2012 to 2014, 483 women in six hospitals, where the DA was used (intervention group), were compared with 441 women in six matched hospitals (control group). Women with one previous CS, pregnant of a singleton in cephalic presentation, delivering after 37 weeks 0 days were eligible for inclusion. RESULTS: There was no significant difference in VBAC rates between the intervention (45%) and control group (46%) (adjusted odds ratio 0,92 (95% Confidence interval 0.69-1.23)). In the intervention group more women (42%) chose an elective repeat CS compared to the control group (31%) (adjusted odds ratio 1.6 (95% Confidence interval 1.18-2.17)). Of women choosing trial of labor, in the intervention group 77% delivered vaginally compared to 67% in the control group, resulting in an unplanned CS adjusted odds ratio of 0,57 (0.40-0.82) in the intervention group. In the intervention group, more women reported to be involved in decision-making (98% vs. 68%, P< 0.001). CONCLUSIONS: Implementing a decision aid with a prediction model for risk selection suggests unchanged VBAC rates, but 40% reduction in unplanned CS rates, increase in elective repeat CS and improved patient involvement in decision-making.