Development and validation of a nomogram to predict the risk of cesarean delivery in macrosomia

Objective. To develop and validate a nomogram that predicts individual probability of cesarean delivery in cases of macrosomia (> 4,000 g). Methods. The nomogram was built based on the data from 246 patients who delivered macrosomic infants at Conception Hospital (Marseille, France), and was validated on an external population of 206 patients. Logistic regression was used to construct a model to predict the probability of cesarean section. The calculations were based on actual birth weight. Main outcome measures. The accuracy of the model was evaluated by area under the receiver operator curve. Results. In the multivariate analysis performed on the training set, maternal age (p=0.002), parity (p=0.003), and maternal height < 1.65 m (p=0.01) were found to be significantly associated with the occurrence of cesarean delivery and included in the nomogram. The final variables included in the nomogram were: age (p=0.01), maternal height (p=0.02), parity (p < 0.001), and previous cesarean section (p=0.009). Area under the ROCs was 0.80 and 0.78 in the training set before and after bootstrapping, respectively, and 0.88 in the validation set. The calibration of the nomogram was good. Conclusion. We have developed a nomogram based on actual birth weight that accurately predicts the risk of cesarean delivery in cases of macrosomia. This tool might be useful for decision-making.