Vaginal birth after cesarean section prediction model for Jordanian population.

The rate of cesarean section has increased significantly worldwide, creating a group of women with one lower segment cesarean section concerned about the mode of delivery in their future pregnancies. This group of mothers will face a complex discussion because the likelihood for a successful vaginal birth after cesarean section provided to them is a general one. The probability of having a successful vaginal birth is the cornerstone factor of the mothers' decision. Therefore, providing a case-specific likelihood that respects the characteristics of each pregnancy will refine counseling, lower the decision conflict, and improve the success rate of vaginal birth trials eventually improving maternal and fetal outcomes. This paper aims to develop a clinical decision support system to evaluate the individualized likelihood mode of delivery for pregnant women with a previous lower segment cesarean section based on their unique characteristics. The study included six hundred fifty-nine pregnant women, where three hundred twenty-seven records had missing values. Various pre-processing steps, including missing data imputation and feature selection, were applied to the original dataset before model development to improve the data quality. Missing values were handled first, then a feature selection process using a genetic algorithm was applied to select the relevant features and to exclude features that may have been affected negatively by missing data imputation. After that, four machine learning classifiers, namely Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression, were used to build the prediction model. The results showed that imputing missing values followed by feature selection was more efficient than deleting them since the Area Under the Curve (AUC) has increased from 0.655 to 0.812 using the KNN classifier.

Validation of the Grobman model for successful vaginal birth after cesarean section in Jordanian women.

BACKGROUND: To validate both models of Grobman nomogram (The antenatal and the intrapartum model) for predicting successful intended Vaginal Birth After Caesarean delivery (VBAC) in a Jordanian population. METHODS: A retrospective study has identified all live, singleton, term, cephalic pregnancies with a previous lower segment cesarean section who opted for a Trial Of Labour After Caesarean Section (TOLAC) between January 2014 to December 2020. Five variables were used for the antenatal model, while ten variables were used for the intrapartum model. Two sets of patients were created: one for the antenatal model and the other for the intrapartum model. The predicted probability for each woman was calculated and compared with the successful VBAC for each category. The predictive ability was assessed with a receiver operating characteristic, and the area under the curve (AUC) was determined. RESULTS: There were seven hundred and fourteen complete cases for the antenatal model and six hundred ninety-seven for the intrapartum model. Our population's overall number of VBAC is 83.89% for the antenatal group and 82.92% for the intrapartum group. The mean predicted probability for a successful intended VBAC using the antenatal and intrapartum models were 79.53 ± 13.47 and 78.64 ± 14.03, respectively. The antenatal and intrapartum predictive models ROC had an AUC of 65% (95% CI: 60%-71%) and 64% (95% CI: 58%-69%), respectively. CONCLUSIONS: Both models are validated in the Jordanian population. Adapting the antenatal model as supporting evidence can lead to a higher rate of TOLAC.