TY - JOUR
T1 - Prediction models of vaginal birth after cesarean delivery
T2 - A systematic review
AU - Deng, Bo
AU - Li, Yan
AU - Chen, Jia Yin
AU - Guo, Jun
AU - Tan, Jing
AU - Yang, Yang
AU - Liu, Ning
N1 - Funding Information:
This work was supported by the Guizhou Provincial Key Projects of Teaching Reform for Graduate Education [grant numbers: YJSJGKT (2021) 034 ], the Zhuhai Philosophy and Social Science Planning Project [grant numbers: 2021YBA037 ], and Key Project of Teaching Reform for Graduate Education, Zunyi Medical University [grant numbers: ZYK95 ].
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - Prediction model
KW - Risk prediction
KW - Systematic review
KW - Vaginal birth after cesarean
UR - http://www.scopus.com/inward/record.url?scp=85138511260&partnerID=8YFLogxK
U2 - 10.1016/j.ijnurstu.2022.104359
DO - 10.1016/j.ijnurstu.2022.104359
M3 - Review article
C2 - 36152466
AN - SCOPUS:85138511260
SN - 0020-7489
VL - 135
JO - International Journal of Nursing Studies
JF - International Journal of Nursing Studies
M1 - 104359
ER -