Predicting vaginal birth after previous cesarean: Using machine-learning models and a population-based cohort in Sweden

Application of Predictive Analytics in Pregnancy, Birth, and Postpartum Nursing Care

ABSTRACT

Predictive analytics has emerged as a promising approach for improving reproductive health care and patient outcomes. During pregnancy and birth, the ability to accurately predict risks and complications could enable earlier interventions and reduce adverse events. However, there are challenges and ethical considerations for implementing predictive models in perinatal care settings. We introduce major concepts in predictive analytics and describe application of predictive modeling to perinatal care topics such as fertility, preeclampsia, labor onset, vaginal birth after cesarean, uterine rupture, induction outcomes, postpartum hemorrhage, and postpartum mood disorders. Although some predictive models have achieved adequate accuracy (AUC 0.7-0.9), most require additional external validation across diverse populations and practice settings. Bias, particularly racial bias, remains a key limitation of current models. Nurses and advanced practice nurses, including nurse practitioners certified registered nurse anesthetists, and nurse-midwives, play a vital role in ensuring high-quality data collection and communicating predictive model outputs to clinicians and users of the health care system. Addressing the ethical challenges and limitations of predictive analytics is imperative to equitably translate these tools to support patient-centered perinatal care.

Models for predicting vaginal birth after cesarean section: scoping review

BACKGROUND

Women who are pregnant again after a prior cesarean section are faced with the choice between a vaginal trial and another cesarean section. Vaginal delivery is safer for mothers and babies, but face the risk of trial labor failure. Predictive models can evaluate the success rate of vaginal trial labor after cesarean section, which will help obstetricians and pregnant women choose the appropriate delivery method.

OBJECTIVE

To review the existing prediction models of vaginal delivery after cesaean.

METHODS

Seven databases, including CNKI, Wanfang Data, Chinese Science and Technology Periodical Database, China Biomedical Literature Database, PubMed, Embase, and Web of Science, were searched for studies on the predictive model of VBAC from inception to July 20, 2022. Two researchers independently screened the literature and extracted the data. The risk of bias and applicability of the included researches was evaluated using the Prediction model Risk of Bias Assessment Tool.

RESULTS

Twenty-six studies that covered 26 models were included. The overall property of the included models was good, but validation of the included models was insufficient. The methodological quality of the included studies was generally low, with 3 studies rated as having a low risk of bias and 23 studies rated as having a high risk of bias. The main predictors in the models were the Bishop score, history of vaginal delivery, neonatal weight, maternal age, and BMI.

CONCLUSIONS

Although a variety of prediction models have been developed globally, the methodology of these studies has limitations and the models have not been adequately validated. In the future, more prospective and high-quality research is needed to develop visual models to serve clinical work more effectively and conveniently. Obstetricians or midwives could use predictive models to help a woman choose the right delivery method.

Artificial Intelligence in Predicting the Mode of Delivery: A Systematic Review

The integration of artificial intelligence (AI) into obstetric care offers significant potential to enhance clinical decision-making and optimize maternal and neonatal outcomes. Traditional prediction methods for mode of delivery often rely on subjective clinical judgment and limited statistical models, which may not fully capture complex patient data. This systematic review aims to evaluate the current state of research on AI applications in predicting the mode of delivery, comparing the performance of AI models with traditional methods, and identifying gaps for future research. A comprehensive literature search was conducted across PubMed, Google Scholar, Web of Science, and Scopus databases, covering publications from January 2010 to July 2024. Inclusion criteria were studies employing AI techniques to predict the mode of delivery, published in peer-reviewed journals, and involving human subjects. Studies were assessed for quality using the Prediction Model Risk of Bias Assessment Tool (PROBAST), and data were synthesized narratively due to heterogeneity. In total, 18 studies met the inclusion criteria, employing various AI models such as logistic regression, random forest, gradient boosting, and neural networks. Sample sizes ranged from 40 to 94,480 participants across diverse geographic settings. AI models demonstrated high accuracy rates, often exceeding 90%, and strong predictive metrics (area under the curve (AUC) values from 0.745 to 0.932). Key predictors included maternal age, gravidity, parity, gestational age, labor induction type, and fetal weight. Notable models like the Adana System and Categorical Boosting (CatBoost, Yandex LLC, Moscow, Russia) highlighted the effectiveness of AI in enhancing prediction accuracy and supporting clinical decisions. AI models significantly outperform traditional statistical methods in predicting the mode of delivery, providing a robust tool for obstetric care. Future research should focus on standardizing data collection, improving model interpretability, addressing ethical concerns, and ensuring fairness in AI predictions to enhance clinical trust and application.

Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics

BACKGROUND

Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care.

OBJECTIVE

The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process.

METHODS

A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion.

RESULTS

According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload.

CONCLUSION

The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.

Construction and evaluation of nursing-sensitive quality indicators for vaginal birth after cesarean: A Delphi study based on Chinese population

Aim

To develop scientific, systematic and clinically applicable nursing-sensitive quality indicators for vaginal birth after cesarean in obstetrics, which provide a theoretical and clinical basis for monitoring and improving the nursing quality of vaginal birth after cesarean in China.

Methods

A modified Delphi-consensus technique was used in this study. Based on literature retrieval published between January 2012 and December 2022 and group discussion, the preliminary nursing-sensitive quality indicators were selected using a structural-process-outcome model. Then a questionnaire was designed on the preliminary indicators. The modified Delphi method was used to conduct two rounds of expert consultation among 26 hospitals in China. The survey data of experts' opinions were collected and analyzed to determine the final nursing-sensitive quality indicators. The importance of indicators, rationality of calculation formula and operability of data collection were analyzed and discussed.

Results

A total of 33 nursing-sensitive quality indicators were determined. The indicators were composed of 3-level ones, including 3 first-level indicators (structural, process and outcome indicators), 9 s-level ones and 33 third-level ones. The positive coefficients in the two rounds of expert consultation were 95.56 % and 97.67 %, respectively, and the authoritative coefficients were 0.88 and 0.94. The coefficients of variation ranged from 0.05 to 0.28.

Conclusion

The nursing-sensitive quality indicators were successfully developed using the modified Delphi method. The indicators are scientific, systematic and clinically operable, and play an important role in improving the nursing quality for pregnant women with vaginal birth after cesarean.

Prediction of cesarean delivery in class III obese nulliparous women: An externally validated model using machine learning

BACKGROUND

class III obese women, are at a higher risk of cesarean section during labor, and cesarean section is responsible for increased maternal and neonatal morbidity in this population.

OBJECTIVE

the objective of this project was to develop a method with which to quantify cesarean section risk before labor.

METHODS

this is a multicentric retrospective cohort study conducted on 410 nulliparous class III obese pregnant women who attempted vaginal delivery in two French university hospitals. We developed two predictive algorithms (a logistic regression and a random forest models) and assessed performance levels and compared them.

RESULTS

the logistic regression model found that only initial weight and labor induction were significant in the prediction of unplanned cesarean section. The probability forest was able to predict cesarean section probability using only two pre-labor characteristics: initial weight and labor induction. Its performances were higher and were calculated for a cut-point of 49.5% risk and the results were (with 95% confidence intervals): area under the curve 0.70 (0.62,0.78), accuracy 0.66 (0.58, 0.73), specificity 0.87 (0.77, 0.93), and sensitivity 0.44 (0.32, 0.55).

CONCLUSIONS

this is an innovative and effective approach to predicting unplanned CS risk in this population and could play a role in the choice of a trial of labor versus planned cesarean section. Further studies are needed, especially a prospective clinical trial.

FUNDING

French state funds "Plan Investissements d'Avenir" and Agence Nationale de la Recherche.

Middle eastern college of obstetricians and gynecologists (MCOG) practice guidelines: Role of prediction models in management of trial of labor after cesarean section. Practice guideline no. 05-O-22✰,✰✰,★,★★

Cesarean delivery rates have been steadily rising since the beginning of the 21st century. The growing incidence is even more prominent in developing countries owing to lack of evidence-based guidance and audit, and the expansion of private practice. The uprise in Cesarean delivery rate has been associated with considerable financial burden and has increased the risk otherwise uncommon serious complications such as placenta accreta disorders and uterine rupture. In addition to primary prevention of Cesarean delivery, trial of labor after cesarean section is one of the most successful strategies to reduce Cesarean deliveries and minimize risks associated with higher order Cesarean deliveries. This guideline appraises patient selection strategies and use of prediction model to promote counseling and enhance safety in women considering vaginal birth after Cesarean.

The Stockholm-Gotland perinatal cohort-A population-based cohort including longitudinal data throughout pregnancy and the postpartum period

BACKGROUND

Register-based reproductive and perinatal databases rarely contain detailed information from medical records or repeated measurements throughout pregnancy and delivery. This lack of enriched pregnancy and birth data led to the initiation of the Swedish Stockholm-Gotland Perinatal Cohort (SGPC).

OBJECTIVES

To describe the strengths of the SGPC, as well as the unique research questions that can be addressed using this cohort.

POPULATION

The SGPC is a prospectively collected, population-based cohort that includes all births (from 22 completed gestational weeks onwards) between 1 January 2008 and 15 June 2020 in the Stockholm and Gotland regions of Sweden (N 335,153 singleton and N 11,025 multiple pregnancies).

DESIGN

Descriptive study.

METHODS

The SGPC is based on the electronic medical records of women and their infants. The medical record system is used for all antenatal clinic visits and admissions, delivery and neonatal admissions, as well as postpartum clinical visits. SGPC has been further enriched with data linkages to 10 Swedish National Health Care and Quality Registers.

PRELIMINARY RESULTS

In contrast to other reproductive and perinatal databases available in Sweden, including the Medical Birth Register and the Pregnancy Register, SGPC contains highly detailed medical record data, including time-varying serial measurements for physiological parameters throughout pregnancy, delivery, and postpartum, for both mother and infant. These strengths have enabled studies that were previously inconceivable; the effects of serial measurements of pregnancy weight gain, changes in haemoglobin counts and blood pressure during pregnancy, fetal weight estimations by ultrasound, duration of stages and phases of labour, cervical dilatation and oxytocin use during delivery, and constructing reference curves for umbilical cord pH.

CONCLUSIONS

The SGPC-with its rich content, repeated measurements and linkages to numerous health care and quality registers-is a unique cohort that enables high-quality perinatal studies that would otherwise not be possible.

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study

Background

The use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new reforms and advanced technologies of artificial intelligence have enabled data analysts to transmute raw data generated by these sectors into meaningful insights for an effective decision-making process. Health care is one of the integral sectors where a large amount of data is generated daily, and making effective decisions based on these data is therefore a challenge. In this study, cases related to childbirth either by the traditional method of vaginal delivery or cesarean delivery were investigated. Cesarean delivery is performed to save both the mother and the fetus when complications related to vaginal birth arise.

Objective

The aim of this study was to develop reliable prediction models for a maternity care decision support system to predict the mode of delivery before childbirth.

Methods

This study was conducted in 2 parts for identifying the mode of childbirth: first, the existing data set was enriched and second, previous medical records about the mode of delivery were investigated using machine learning algorithms and by extracting meaningful insights from unseen cases. Several prediction models were trained to achieve this objective, such as decision tree, random forest, AdaBoostM1, bagging, and k-nearest neighbor, based on original and enriched data sets.

Results

The prediction models based on enriched data performed well in terms of accuracy, sensitivity, specificity, F-measure, and receiver operating characteristic curves in the outcomes. Specifically, the accuracy of k-nearest neighbor was 84.38%, that of bagging was 83.75%, that of random forest was 83.13%, that of decision tree was 81.25%, and that of AdaBoostM1 was 80.63%. Enrichment of the data set had a good impact on improving the accuracy of the prediction process, which supports maternity care practitioners in making decisions in critical cases.

Conclusions

Our study shows that enriching the data set improves the accuracy of the prediction process, thereby supporting maternity care practitioners in making informed decisions in critical cases. The enriched data set used in this study yields good results, but this data set can become even better if the records are increased with real clinical data.

Obstetricians' perspectives on trial of labor after cesarean (TOLAC) under the two-child policy in China: a cross-sectional study

Background

As the birth policy has been adjusted from one-child-one-couple to universal two-child-one-couple in China, there is an increasing number of women undergoing a second pregnancy after a previous cesarean section (CS). Undertaking an elective repeat CS (ERCS) has been taken for granted and has thus become a major contributor to the increasing CS rate in China. Promoting trial of labor after CS (TOLAC) can reduce the CS rate without compromising delivery outcomes. This study aimed to investigate Chinese obstetricians’ perspectives regarding TOLAC, and the factors associated with their decision-making regarding recommending TOLAC to pregnant women with a history of CS under the two-child policy.

Methods

A cross-sectional survey was carried out between May and July 2018. Binary logistic regression was used to determine the factors associated with the obstetricians’ intention to recommend TOLAC to pregnant women with a history of CS. The independent variables included sociodemographic factors and perceptions regarding TOLAC (selection criteria for TOLAC, basis underlying the selection criteria for TOLAC, and perceived challenges regarding promoting TOLAC).

Results

A total of 426 obstetricians were surveyed, with a response rate of ≥83%. The results showed that 31.0% of the obstetricians had no intention to recommend TOLAC to pregnant women with a history of CS. Their decisions were associated with the perceived lack of confidence regarding undergoing TOLAC among pregnant women with a history of CS and their families (odds ratio [OR] = 2.31; 95% CI: 1.38–1.38); obstetricians’ uncertainty about the safety of TOLAC for pregnant women with a history of CS (OR = 0.49; 95% CI: 0.27–0.96), and worries about medical lawsuits due to adverse delivery outcomes (OR = 0.14; 95% CI: 0.07–0.31). The main reported challenges regarding performing TOLAC were lack of clear guidelines for predicting or avoiding the risks associated with TOLAC (83.4%), obstetricians’ uncertainty about the safety of TOLAC for women with a history of CS (81.2%), pregnant women’s unwillingness to accept the risks associated with TOLAC (81.0%) or demand for ERCS (80.7%), and the perceived lack of confidence (77.5%) or understanding (69.7%) regarding undergoing TOLAC among pregnant women and their families.

Conclusion

A proportion of Chinese obstetricians did not intend to recommend TOLAC to pregnant women with a history of CS. This phenomenon was closely associated with obstetricians’ concerns about TOLAC safety and perceived attitudes of the pregnant women and their families regarding TOLAC. Effective measures are needed to help obstetricians predict and reduce the risks associated with TOLAC, clearly specify the indications for TOLAC, improve labor management, and popularize TOLAC in China. Additionally, public health education on TOLAC is necessary to improve the understanding of TOLAC among pregnant women with a history of CS and their families, and to improve their interactions with their obstetricians regarding shared decision making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-021-03559-1.

Predicting vaginal birth after previous cesarean: Using machine-learning models and a population-based cohort in Sweden

Introduction

Predicting a woman’s probability of vaginal birth after cesarean could facilitate the antenatal decision‐making process. Having a previous vaginal birth strongly predicts vaginal birth after cesarean. Delivery outcome in women with only a cesarean delivery is more unpredictable. Therefore, to better predict vaginal birth in women with only one prior cesarean delivery and no vaginal deliveries would greatly benefit clinical practice and fill a key evidence gap in research. Our aim was to predict vaginal birth in women with one prior cesarean and no vaginal deliveries using machine‐learning methods, and compare with a US prediction model and its further developed model for a Swedish setting.

Material and methods

A population‐based cohort study with a cohort of 3116 women with only one prior birth, a cesarean, and a subsequent trial of labor during 2008‐2014 in the Stockholm‐Gotland region, Sweden. Three machine‐learning methods (conditional inference tree, conditional random forest and lasso binary regression) were used to predict vaginal birth after cesarean among women with one previous birth. Performance of the new models was compared with two existing models developed by Grobman et al (USA) and Fagerberg et al (Sweden). Our main outcome measures were area under the receiver‐operating curve (AUROC), overall accuracy, sensitivity and specificity of prediction of vaginal birth after previous cesarean delivery.

Results

The AUROC ranged from 0.61 to 0.69 for all models, sensitivity was above 91% and specificity below 22%. The majority of women with an unplanned repeat cesarean had a predicted probability of vaginal birth after cesarean >60%.

Conclusions

Both classical regression models and machine‐learning models had a high sensitivity in predicting vaginal birth after cesarean in women without a previous vaginal delivery. The majority of women with an unplanned repeat cesarean delivery were predicted to succeed with a vaginal birth (ie specificity was low). Additional covariates combined with machine‐learning techniques did not outperform classical regression models in this study.