Development and validation of a risk prediction model for caesarean delivery among multiparous women.

While caesarean risk prediction models exist for nulliparous and high-risk pregnancies, there is a lack of models that predict the risk of caesarean delivery among multiparous women. This study aimed to develop and validate a risk prediction model for caesarean delivery and assess its clinical utility among multiparous women. Using data from 460 participants, a prediction model was developed to predict the risk of caesarean delivery. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration plot, and the model was internally validated using bootstrapping technique. A simplified risk score was calculated, and a nomogram was developed for the individual caesarean delivery risk guide. Additionally, a decision curve analysis was performed to assess the clinical utility of the model. The final model included four predictors: maternal age, previous caesarean delivery, pregnancy-induced hypertension, and antepartum hemorrhage. The model had an AUC of 78.0% (95% CI 71.1-84.8), indicating good discrimination capacity. The model also exhibited good calibration and a low overoptimism coefficient, indicating minimal risk of overfitting. The risk prediction model has good clinical utility for discriminating multiparous women at risk of caesarean delivery. The tool can guide clinicians in estimating the risk of caesarean delivery among multiparous women that could lead to improved maternal and neonatal outcomes, ultimately enhancing the quality of care delivered in low-resource settings.