Third-trimester prediction of successful vaginal birth after one cesarean delivery-A Swedish model

Maternal age and body mass index and risk of labor dystocia after spontaneous labor onset among nulliparous women: A clinical prediction model

INTRODUCTION

Obstetrics research has predominantly focused on the management and identification of factors associated with labor dystocia. Despite these efforts, clinicians currently lack the necessary tools to effectively predict a woman's risk of experiencing labor dystocia. Therefore, the objective of this study was to create a predictive model for labor dystocia.

MATERIAL AND METHODS

The study population included nulliparous women with a single baby in the cephalic presentation in spontaneous labor at term. With a cohort-based registry design utilizing data from the Copenhagen Pregnancy Cohort and the Danish Medical Birth Registry, we included women who had given birth from 2014 to 2020 at Copenhagen University Hospital-Rigshospitalet, Denmark. Logistic regression analysis, augmented by a super learner algorithm, was employed to construct the prediction model with candidate predictors pre-selected based on clinical reasoning and existing evidence. These predictors included maternal age, pre-pregnancy body mass index, height, gestational age, physical activity, self-reported medical condition, WHO-5 score, and fertility treatment. Model performance was evaluated using the area under the receiver operating characteristics curve (AUC) for discriminative capacity and Brier score for model calibration.

RESULTS

A total of 12,445 women involving 5,525 events of labor dystocia (44%) were included. All candidate predictors were retained in the final model, which demonstrated discriminative ability with an AUC of 62.3% (95% CI:60.7-64.0) and Brier score of 0.24.

CONCLUSIONS

Our model represents an initial advancement in the prediction of labor dystocia utilizing readily available information obtainable upon admission in active labor. As a next step further model development and external testing across other populations is warranted. With time a well-performing model may be a step towards facilitating risk stratification and the development of a user-friendly online tool for clinicians.

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.

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.

What are the factors affecting parents' postnatal sense of security?

INTRODUCTION

The postpartum period is part of an important process for mothers and fathers. A sense of security is central as it might influence a parent's journey towards becoming a successful parent. The aim was to determine factors affecting parents' postnatal sense of security (PPSS) before postpartum discharge from a hospital in Konya, Turkey.

METHODS

A descriptive study was conducted. From January 2019 to March 2019, a questionnaire was given to a convenience sample of 188 couples discharged from a regional hospital in Turkey. The sense of security was assessed using the PPSS instrument, with low scores defined as those less than the mean.

RESULTS

Low and high sense of security was based on the mean in the population, for mothers 49.36 and for fathers 34.90. It was found that 43.6% of mothers and 69.7 % of fathers had a low score, which was linked to some specific factors in the postpartum period. These were the type of birth, being ready to take responsibility for baby care, being ready to be discharged, being healthy, having any concern about the baby's health, social support presence, having professional support, and presence of a sense of security.

CONCLUSIONS

Many parents, particularly fathers, have a low postnatal sense of security. In the postpartum period, it is very important for midwives, who are always with the family, to identify the risks for a low sense of security during this period and provide effective care. More studies in different settings with larger samples are recommended.

Implementation of a calculator to predict cesarean delivery during labor induction: a qualitative evaluation of the clinician perspective

BACKGROUND

We previously conducted a prospective cohort study (n=1610) demonstrating that the implementation of a validated calculator to predict likelihood of cesarean delivery during labor induction was associated with reduced maternal morbidity, reduced cesarean delivery rate, and improved birth satisfaction.

OBJECTIVE

To optimize future implementation, we used qualitative interviews to understand the clinician perspective on: (1) the cesarean delivery risk calculator implementation and (2) the mechanisms by which the use of the calculator resulted in the observed improved outcomes.

STUDY DESIGN

After completion of the prospective study (June 30, 2019), 20 trainees and attending clinicians (including nurse-midwives, obstetrical physicians, and family medicine physicians) at the study site participated in a single, brief semistructured interview from March 1, 2020, to June 30, 2020. Transcriptions were coded using a systematic approach.

RESULTS

Overall, clinicians had favorable perspectives regarding the cesarean delivery risk calculator. Clinicians described the calculator as offering "objective data" and a "standardized snapshot of the labor trajectory." Concerns were raised regarding "overreliance" on calculator output. Barriers to use included time for patient counseling and "awkwardness" around the interactions and perceived patient misunderstanding of the calculator result. Although most senior clinicians (n=8) reported that the calculator did not impact patient management, trainee clinicians (n=12) more often felt that the calculator influenced care at the extremes of cesarean delivery risk. Furthermore, more senior clinicians felt "neutral" regarding any impact of counseling patients on cesarean delivery risk compared with trainee clinicians, who felt that the counseling "built [patient-clinician] trust."

CONCLUSION

This qualitative evaluation characterized the generally positive clinician perspective around the cesarean delivery risk calculator, while identifying specific facilitators and barriers to implementation. In addition, we elucidated potential mechanisms by which the calculator may have been related to clinician decision making and patient-clinician interactions, leading to reduced maternal morbidity and improved patient birth satisfaction. This information is important as widespread implementation of the cesarean delivery risk calculator begins.

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.