Prediction of vaginal birth after cesarean deliveries using machine learning

Lipschuetz, Michal; Guedalia, Joshua; Rottenstreich, Amihai; Persky, Michal Novoselsky; Cohen, S. M.; Kabiri, Doron; Levin, Gabriel; Yagel, Simcha; Unger, Ron; Sompolinsky, Yishai

doi:10.1016/j.ajog.2019.12.267

Cited by 66 publications

(63 citation statements)

References 48 publications

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“…The inclusion and exclusion criteria, employed in this study, were based on recent clinical guidelines, whereas the pre-delivery variables were increased by modifying the Grobman's model. These results are consistent with recent studies reporting that maternal BMI at delivery, history of vaginal delivery and maternal age at delivery are relevant or independent risk factors for successful TOLAC [12,[19][20][21][22]. Among these factors, history of vaginal delivery for predicting TOLAC success has been extensively reported [23].…”

Section: Discussionsupporting

confidence: 91%

Prediction of vaginal birth after cesarean delivery in Southeast China: a retrospective cohort study

Zhang

Zheng

Cheng

et al. 2020

BMC Pregnancy Childbirth

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Background We aimed to develop and validate a nomogram for effective prediction of vaginal birth after cesarean (VBAC) and guide future clinical application. Methods We retrospectively analyzed data from hospitalized pregnant women who underwent trial of labor after cesarean (TOLAC), at the Fujian Provincial Maternity and Children’s Hospital, between October 2015 and October 2017. Briefly, we included singleton pregnant women, at a gestational age above 37 weeks who underwent a primary cesarean section, in the study. We then extracted their sociodemographic data and clinical characteristics, and randomly divided the samples into training and validation sets. We employed the least absolute shrinkage and selection operator (LASSO) regression to select variables and construct VBAC success rate in the training set. Thereafter, we validated the nomogram using the concordance index (C-index), decision curve analysis (DCA), and calibration curves. Finally, we adopted the Grobman’s model to perform comparisons with published VBAC prediction models. Results Among the 708 pregnant women included according to inclusion criteria, 586 (82.77%) patients were successfully for VBAC. Multivariate logistic regression models revealed that maternal height (OR, 1.11; 95% CI, 1.04 to 1.19), maternal BMI at delivery (OR, 0.89; 95% CI, 0.79 to 1.00), fundal height (OR, 0.71; 95% CI, 0.58 to 0.88), cervix Bishop score (OR, 3.27; 95% CI, 2.49 to 4.45), maternal age at delivery (OR, 0.90; 95% CI, 0.82 to 0.98), gestational age (OR, 0.33; 95% CI, 0.17 to 0.62) and history of vaginal delivery (OR, 2.92; 95% CI, 1.42 to 6.48) were independently associated with successful VBAC. The constructed predictive model showed better discrimination than that from the Grobman’s model in the validation series (c-index 0.906 VS 0.694, respectively). On the other hand, decision curve analysis revealed that the new model had better clinical net benefits than the Grobman’s model. Conclusions VBAC will aid in reducing the rate of cesarean sections in China. In clinical practice, the TOLAC prediction model will help improve VBAC’s success rate, owing to its contribution to reducing secondary cesarean section.

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Section: Discussionsupporting

confidence: 91%

Prediction of vaginal birth after cesarean delivery in Southeast China: a retrospective cohort study

Zhang

Zheng

Cheng

et al. 2020

BMC Pregnancy Childbirth

View full text Add to dashboard Cite

show abstract

“…Some studies have been carried out to predict risks during pregnancy, such as the probability of premature birth, vaginal delivery after cesarean section, and the suchlike. For example, Lipschuetz and coworkers [24] conducted a study to develop a personalized tool for predicting vaginal births after cesarean deliveries using different machine learning methods: gradient boosting, RF, balanced RF, and Ad-aBoost ensembles. Similarly, Tessmer-Tuck and team [25] developed a model to predict vaginal births after cesarean sections using multivariate analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The number of features listed in each article varied from 11 to 161. In [20] mentioned allergies and gestation, while Yagel and coworkers [24] used previous abortions, interpregnancy interval, inter-pregnancy interval with cesarean delivery, gestational stage at previous deliveries, gestational stage in last delivery and the likes. Some features, however, were common across many of these articles, such as age, body mass index (BMI), parity, and gestational stage [32] [24], while some very uncommon features were found in other articles, such as failed pregnancy termination, mother/sister with preeclampsia, mother/sister with gestational diabetes, Apgar score in previous deliveries, femur length, and humerus length.…”

Section: ) Study Findingsmentioning

confidence: 99%

“…x Not all the features used in this study were explicitly stated Lipschuetz et al [24] To predict vaginal births after cesarean deliveries 34 Maternal age, gravidity, parity, previous abortions, previous ectopic pregnancies, number of live children .…”

Section: ) Study Findingsmentioning

confidence: 99%

“…Different risks associated with pregnancy were predicted by Brandão and colleagues [27] and Goodwin and Maher [29]. Some studies were conducted to predict vaginal deliveries after cesarean sections; for example, Lipschuetz and coworkers [24] and Tessmer-Tuck and team [35]. Campillo-Artero [30] predicted the indicators of emergency cesarean sections and identified the factors that placed mothers at risk of emergency cesarean section.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

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Prediction of vaginal birth after cesarean for labor dystocia by sonographic estimated fetal weight

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Elective repeat cesarean delivery (ERCD) carries an inherent risk for maternal morbidity and economic costs. Therefore, reducing the rising rates of ERCD worldwide is paramount. 1,2 Trial of labor after cesarean (TOLAC) is an alternative to ERCD and represents an effective option to reduce cesarean delivery rates. When successful vaginal birth after cesarean (VBAC) is achieved, it is associated with lower rates of perinatal adverse outcomes. 1,3 As failure of TOLAC is associated with adverse neonatal and maternal outcomes, including postpartum hemorrhage, uterine rupture, perinatal mortality, and hypoxic-ischemic encephalopathy, 4,5 efforts

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Prediction of vaginal birth after cesarean deliveries using machine learning

Cited by 66 publications

References 48 publications

Prediction of vaginal birth after cesarean delivery in Southeast China: a retrospective cohort study

Prediction of vaginal birth after cesarean delivery in Southeast China: a retrospective cohort study

Exploring Machine Learning Algorithms to Find the Best Features for Predicting Modes of Childbirth

Prediction of vaginal birth after cesarean for labor dystocia by sonographic estimated fetal weight

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