Maternal and fetal characteristics to predict c-section delivery: A scoring system for pregnant women

Abstract

Introduction: Cesarean section is one of the most common obstetrical interventions that has been performed at an increasing rate globally, due to both medical and non-medical reasons. This study aims to develop a prediction tool for pregnant women potentially needing c-section, such that necessary preparations from the mothers, families, and health providers can be made. Methods: A total of 603 pregnant women were recruited in the first phase of c-section prediction tool development. The association between the maternal and fetal factors on the risk of c-section were analyzed, followed by a stepwise multivariate regression analysis. In the next phase, 61 pregnant women were enrolled for external validation. Discrimination was assessed using area under the curve. The calibration plot was then made and assessed using the Hosmer–Lemeshow test. Results: There were 251 (41.6%) cases of vaginal delivery and 352 (58.4%) of c-section assessed. Multivariate analysis showed that gestational age < 37 wg (OR: 1.66, 95% CI: 1.10–2.51), pre-pregnancy body mass index (underweight) (OR: 0.40, 95% CI: 0.22–0.76), no history of vaginal delivery (OR: 2.66, 95% CI: 1.76–4.02), history of uterine surgery (OR: 8.34, 95% CI: 4.54–15.30), obstetrical complications (OR: 5.61, 95% CI: 3.53–8.90), birthweight ⩾ 3500 g (OR: 4.28, 95% CI: 2.16–8.47), and non-cephalic presentation (OR: 2.74, 95% CI: 1.53–4.89) were independently associated with c-section delivery. Those parameters were included in a 7-item scoring tool, with consecutive predictive scores of 1,–1,2,3,3,2,2,1. The area under the curve result was 0.813 (95% CI: 0.779–0.847), indicating a good predictive ability. The external validation showed AUC: 0.806, 95% CI: 0.694–0.917, Hosmer–Lemeshow test p = 0.666 and calibration plot coefficient of r = 0.939. Conclusion: A total of 7 maternal-fetal factors were found to be strongly associated with c-section delivery, including gestational age < 37, maternal underweight body mass index, previous uterine surgery, obstetrical complications, birthweight ⩾ 3500, history of vaginal delivery, and non-cephalic presentation. Using these factors, a prediction tool was developed and validated with good quality.

Association between Placenta Accreta and History of Previous C-Section and position of Placenta in Pregnant Women presenting at A Public Sector Hospital of Punjab, Pakistan: a cross-sectional analysis

“…As it was a cross sectional study hence the study participants were matched for age. There are recent studies which show that the incidence of C-section among pregnant ladies is on a rise generally in the world as those studies show the benefits of a planned C-Section (9). In contrast we cannot take history of the study participants that whether they had planned C-section or it was done as an emergency measure.…”

Prevalence of emergency caesarean delivery and its predictors among women who give birth in Ethiopia using further analysis of EDHS 2016 data: A mixed effect model

“…This study also showed that, women who had history of fistula were more likely to give birth by emergency caesarean delivery as compared to women who had no history of fistulas. A previous study also suggested that obstetric complications increase the risk of emergency caesarean delivery [ 10 ]. This might be associated with the fact that women with a history of fistulas may face a problem of failure in labour progress because of prolonged and obstructed labour.…”

“…But specifically, the magnitude of emergency caesarean delivery and its predictors are not well addressed in Ethiopia. In developing countries, including Ethiopia, lack of knowledge about childbirth-related complications, delays in referrals, and resource-limited health care systems, including the low access to emergency caesarean delivery, are highly correlated with maternal and fetal health [ 10 ]. In Ethiopia, access to emergency caesarean delivery is available only at hospitals and specialty clinics [ 11 , 12 ], and still the birth-related complications and deaths are high [ 9 ].…”

Fast Healthcare Interoperability Resources–Based Support System for Predicting Delivery Type: Model Development and Evaluation Study

“…From the overall data sets that comprised over 200 columns, only a few columns were selected (see Table 1 ). We used a mixture of features selected by surveying the literature [ 21 - 23 ] and features with a high correlation with the outcome. The tested models were logistic regression, decision tree, random forest, 3 different boosting methods (as implemented by extreme gradient boosting [XGBoost], light gradient-boosting machine [LightGBM], and scikit learn), and a linear model based on stochastic gradient descent.…”