Development of a Modified Score System as Prediction Model for Successful Vaginal Birth After Cesarean Delivery

Mid-trimester cervical length and prediction of vaginal birth after cesarean delivery in Chinese parturients: A retrospective study

BACKGROUND

A successful trial of labor after cesarean (TOLAC) is linked with the best maternal/neonatal outcomes and is more cost-effective than elective repeat cesarean section (ERCS). Predictive models of vaginal birth after cesarean (VBAC) have been established worldwide to improve the success rate of TOLAC.

OBJECTIVE

To validate a VBAC prediction model (the updated Grobman's predictive model without ethnicity) and identify whether mid-trimester cervical lengths (MCL) improve the prediction of VBAC among Chinese women undergoing a TOLAC.

METHODS

In this retrospective cohort study, the inclusion criteria were a previous history of cesarean delivery (CD) as well as a singleton gestation in the vertex position with routine CL measurements between 20 and 24 weeks and the experience of a TOLAC. MCL as well as identifiable characteristics in early prenatal care that have been used in updated Grobman's predictive model (maternal age, height, pre-pregnancy weight, vaginal delivery history, VBAC history, arrest disorder in previous CD, and treated chronic hypertension) were obtained from the medical records. Associations of maternal characteristics and MCL with VBAC were evaluated using multivariate logistic regression. Two multivariable regression models with and without MCL as one of the risk factors were established and their predictive accuracy for VBAC was critically compared based on receiver-operating characteristic (ROC) curves.

RESULTS

This study involved 409 women, among which, 347 (84.8%) achieved a VBAC. The mean MCL was significantly shorter in women who had a successful VBAC than in those who required an intrapartum CD (4.16±0.49 cm vs. 4.35±0.46 cm, P=0.007). Multivariable logistic regression revealed that a longer MCL (cm) was significantly related to a lower success rate of TOLAC [adjusted odds ratio (aOR), 0.48; 95% confidence interval (CI), 0.26-0.88]. The areas under the ROCs of Grobman's model with and without MCL as one of the risk factors were 0.785 (95% CI, 0.725-0.844) and 0.774 (95% CI, 0.710-0.837), respectively, but not significantly different (Z = -0.968, P = 0.333).

CONCLUSIONS

We first evaluated the efficiency of the updated Grobman's model (without race and ethnicity) in the Chinese population. The area under the curve is relatively high, indicating that the model can be used efficiently in China. The shorter MCL was associated with a greater chance of VBAC and MCL was the independent factor from the factors of Grobman's model. However, the predictive capacity of the modified model by adding MCL as one of the risk factors did not improve significantly.

A predictive nomogram for a failed trial of labor after cesarean: A retrospective cohort study

AIM

To validate risk factors and a nomogram prediction model for the failure of a trial of labor after cesarean section (TOLAC) in a Chinese population.

METHODS

We included women who tried TOLAC between January 2017 and May 2019, grouped according to the success/failure of TOLAC. The patients were randomized 3:1 into the development and validation sets. Multivariable logistic regression analyses were used to develop a nomogram prediction model for TOLAC failure.

RESULTS

In total, 535 (86.3%) of the women (n = 620) aged 29-34 years had a successful vaginal birth after cesarean (VBAC). All women had a fully healed previous uterine incision. The univariable analyses showed that the cephalopelvic score (p < 0.001), BMI (p = 0.001), full engagement into the pelvis (p < 0.001), Bishop cervical maturity score (p < 0.001), and estimated fetal weight at admission (p < 0.001) could enter the multivariable model. Furthermore, the multivariable analysis showed that the cephalopelvic score (OR = 0.42, 95%CI: 0.23-0.77, p = 0.005), full engagement in the pelvis (OR = 0.16, 95%CI: 0.08-0.33, p < 0.001), and Bishop cervical maturity score (OR = 0.46, 95%CI: 0.35-0.59, p < 0.001) were independent predictors of the failure of TOLAC.

CONCLUSION

This study proposes a nomogram that can assess the risk of failure of TOLAC in Chinese pregnant women. The statistical model could help clinicians know the likelihood of successful TOLAC in the clinical setting.

Prediction models of vaginal birth after cesarean delivery: A systematic review

BACKGROUND

Cesarean section rates are rising in the world. Women with a history of cesarean section will select a cesarean section at the next pregnancy. An objective and accurate prediction about the success rate of vaginal delivery after cesarean section can help women to reduce the complications caused by cesarean section, shorten the time spent in the hospital, and effectively plan medical resources.

OBJECTIVE

To systematically review and critically assess the existing prediction models of vaginal delivery after cesarean section.

METHODS

Some databases (PubMed, Web of Science, EMBASE, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature) were searched from 2000 to 2021 for studies regarding the prediction model of vaginal birth after cesarean delivery. The researchers successively conducted independent literature screening, data extraction and quality evaluation of the included literature, and then utilized the Prediction model Risk of Bias Assessment Tool to assess the methodological quality of the models in the included studies.

RESULTS

A total of 33 studies were included, in which 20 prediction models were identified. Sixteen studies involved external validation of existing models (Grobman's models). In the 20 prediction models, 12 were internally validated, only three had external validation, and seven models were not explicitly reported, with the area under the curve ranging from 0.660 to 0.953; The most common predictors included in the model were body mass index and previous vaginal delivery, followed by maternal age, previous cesarean delivery indication, history of vaginal birth after cesarean, fetal weight, and Bishop's score, gestational age, history of vaginal birth after cesarean, maternal race; The prediction effect of Grobman's model was validated in multiple external populations; The majority of the studies(n = 27) had high risk of bias in the of the Prediction model Risk of Bias Assessment Tool.

CONCLUSIONS

This review provides obstetricians and midwives with important information about the prediction models of vaginal birth after cesarean section, which has been reported optimistic predictive performance and acceptable predictive power. However, the majority of the development studies have methodological limitations, which may hinder the widely application of these models by obstetricians. Further studies are supposed to develop predictive models with low risk of bias, and conduct internal and external validation, providing pragmatic and practical predictions to obstetricians.

PROSPERO REGISTRATION NUMBER

CRD42022299048.

Evaluation of factors that predict the success rate of trial of labor after the cesarean section

Background

For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC).

Methods

All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model.

Results

A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m², cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power.

Conclusion

The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-021-04004-z.

A prospective study of a new prediction model of vaginal birth after cesarean section at a tertiary care centre

Objective:

To create a new and simple model for predicting the likelihood of vaginal birth after cesarean (VBAC) section using variables available at the time of admission.

Materials and Methods:

A prospective observational study was performed at a tertiary care centre in Haryana over a period of 12 months (January 2018 - December 2018) in pregnant women attending the labour room with one previous cesarean section fulfilling the criteria for undergoing trial of labour after cesarean (TOLAC). The sample size was 150. A VBAC score was calculated for each patient using a new prediction model that included variables available at the time of admission such as maternal age, gestational age, Bishop’s score, body mass index, indication for primary cesarean section, and clinically estimated fetal weight. The results of the VBAC scores were correlated with outcomes i.e. successful VBAC or failed VBAC. The chi-square test and Student’s t-test was used for comparison among the groups. Descriptive and regression analysis was performed for the study variables.

Results:

Out of 150 TOLAC cases, 78% had successful VBAC and the remainder (22%) had failed VBAC. The observed probability of having a successful VBAC for a VBAC score of 0-3 was 34%, 4-6 was 68%, 7-9 was 90%, and ≥10 was 97%. The prediction model performed well with an area under the curve of 0.77 (95% CI: 0.68 to 0.85) of the receiver operating characteristics receiver operating characteristic curve.

Conclusion:

The present study shows that the proposed VBAC prediction model is a good tool to predict the outcome of TOLAC and can be used to counsel women regarding the mode of delivery in the current and subsequent pregnancies. Further studies of this model and other such models with different permutations and combinations of variables are required.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

BACKGROUND

Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method.

OBJECTIVE

This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians' decision making.

METHODS

Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2.

RESULTS

Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07).

CONCLUSIONS

Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines.

TRIAL REGISTRATION

PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106.

The Failure Rate, Related Factors, and Neonate Complications of Vaginal Delivery after Cesarean Section

Background

The rate of Cesarean Section (CS) is high in Iran. A successful Vaginal Birth After Cesarean (VBAC) section can protect mothers against the risk of having multiple CS. This study aimed to evaluate the success rate of VBAC, related factors, and the causes of failure.

Materials and Methods

This cross-sectional study was conducted on 150 pregnant women who were candidates for VBAC and admitted at maternity hospitals in Qom from 2016 to 2018. The required data were collected from the patients' records and entered into the checklist. Then, the success rate of VBAC was estimated, and related factors together with the causes of failure were determined by t-test, Chi-square, and independent-samples t-tests in SPSS v. 18 software.

Results

The mean (SD) maternal age was 32 (5.20) years and ranged from 21 to 45 years old. The success rate of VBAC was estimated to be 85.33%, and 14.67% of the patients had to repeat a CS after failure in vaginal delivery. The mean time between previous CS and present delivery was statistically significant between successful and failure groups (t ₁₂₅ = 2.32, p = 0.002). The results also revealed that the most important causes of VBAC failure were prolonged labor [odds ratio (OR) = 4.70)], full arrest (OR = 2.70), and decline fetal heart (OR = 5.31).

Conclusions

The success rate of VBAC in our study was high. However, VBAC was more successful when the interval between inter-deliveries was long, and lower complications were reported when the interval was 2-4 years.

Using healthcare failure mode and effect analysis as a method of vaginal birth after caesarean section management

Aims and objectives

This research was conducted to explore the effectiveness of employing the healthcare failure mode and effect analysis method in the management of trial of labour after caesarean, with the aims of increasing vaginal birth after caesarean section rate and reducing potential risks that might cause severe complications.

Background

Previously high caesarean section rate in China and the “two children” policy leads to the situation where multiparas are faced with the choice of another caesarean or trial of labour after caesarean. Despite evidences showing the benefits of vaginal birth after caesarean, obstetricians and midwives in China tend to be conservative due to limited experience and insufficient clinical routines. Thus, its management needs further optimisation in order to make the practice safe and sound.

Design

A prospective quality improvement programme using the healthcare failure mode and effect analysis.

Methods

With the structured methodology of healthcare failure mode and effect analysis, we determined core processes of antepartum and intrapartum management, conducted risk priority numbers and devised remedial protocols for failure modes with high risks. The programme was then implemented as a clinical routine under the agreement of the institutional review board and vaginal birth after caesarean success rates were compared before and after the quality improvement programme, both descriptively and statistically. Standards for Quality Improvement Reporting Excellence 2.0 checklist was chosen on reporting the study process.

Results

Seventy failure modes in seven core processes were identified in the management process, with 14 redressed for actions. The 1‐year follow‐up trial of labour after caesarean and vaginal birth after caesarean rate was increased compared with the previous 3 years, with a vaginal birth after caesarean rate of 86.36%, whereas the incidence of uterine rupture was not compromised.

Conclusions

The application of healthcare failure mode and effect analysis can not only promote trial of labour after caesarean and vaginal birth after caesarean rate, but also maintaining a low risk of uterine rupture.

Relevance to clinical practice

This modified vaginal birth after caesarean management protocol has been shown effective in increasing its successful rate, which can be continued for further comparison of severe complications to the previous practice.