Developing and validating a risk prediction model for caesarean delivery in Northwest Amhara comprehensive specialized hospitals

Abstract

Despite its benefit in improving outcomes, emergency cesarean delivery is not without maternal and neonatal risks. Early identification of a laboring woman’s risk of cesarean delivery is crucial for reducing complications associated with cesarean delivery. Therefore, this study aimed to develop and validate a risk prediction model for predicting caesarean delivery risk using maternal demographic and obstetric variables. A cross-sectional study was conducted involving a total of 702 laboring mothers. Data were entered into EpiData, Version 4.6, and exported into STATA version 17 and R version 4.2.2 for data management and analysis. A stepwise backward elimination technique was employed to select predictors for the final model. Model was developed using multivariable logistic regression analysis. The final prediction model included three modifiable predictors (place of antenatal follow-up, onset of labor, partograph use) and six non modifiable predictors (age, previous cesarean delivery, number of fetus,, fetal presentation, parity, and gestational age). The model’s performance was assessed using discrimination and calibration plots. Internal validation of the model was conducted using bootstrapping technique. The net benefit of the model was evaluated using decision curve analysis, and a nomogram was developed to calculate the individualized risk of laboring mothers. The model exhibited a discriminatory value of 85.1% (95% CI: 82.1–88.1%) with a good calibration performance. Internal validation showed low over-optimism coefficient (0.003), indicating low risk of over fitting. These findings suggest that clinicians can utilize this model to personalize patient management strategies, focusing on interventions that target modifiable risks while considering the inherent limitations of non-modifiable factors. By integrating this model into clinical practice, healthcare providers can enhance decision-making processes and potentially improve patient outcomes.