Optimal mode of delivery in pregnancy: Individualized predictions using national vital statistics data

Child birth via Cesarean section accounts for approximately 32% of all births each year in the United States. A variety of risk factors and complications can lead caregivers and patients to plan for a Cesarean delivery in advance before onset of labor. However, a non-trivial subset of Cesarean sections (∼25%) are unplanned and occur after an initial trial of labor is attempted. Unfortunately, patients who deliver via unplanned Cesarean sections have increased maternal morbidity and mortality rates and higher rates of neonatal intensive care admissions. In an effort to develop models aimed at improving health outcomes in labor and delivery, this work seeks to explore the use of national vital statistics data to quantify the likelihood of an unplanned Cesarean section based on 22 maternal characteristics. Machine learning techniques are used to ascertain influential features, train and evaluate models, and assess accuracy against available test data. Based on cross-validation results from a large training cohort (n = 6,530,467 births), the gradient-boosted tree algorithm was identified as the best performer and was evaluated on a large test cohort (n = 10,613,877 births) for two prediction scenarios. Area under the receiver operating characteristic curves of 0.77 or higher and recall scores of 0.78 or higher were obtained and the resulting models are well calibrated. Combined with feature importance analysis to explain why certain maternal characteristics lead to a specific prediction in individual patients, the developed analysis pipeline provides additional quantitative information to aid in the decision process on whether to plan for a Cesarean section in advance, a substantially safer option among women at a high risk of unplanned Cesarean delivery during labor.Author summary: Child birth via Cesarean section accounts for almost one third of all births each year in the United States. While many of these Cesarean deliveries are planned for before the onset of labor, a subset arise from complications during labor and occur after an initial trial of labor. These unplanned Cesarean sections unfortunately have increased maternal and neonatal morbidity and mortality rates. This work leverages vital statistics data to develop predictive models that quantify the risk of having an unplanned Cesarean section based on 22 maternal characteristics. Multiple derived models were benchmarked against a large testing cohort to identify a clinically practical model that also demonstrated high calibration accuracy. Ultimately, this model can be used to provide a quantitative aid in the decision process on whether to plan for a Cesarean section in advance, a substantially safer option among women at a high risk of encountering an unplanned Cesarean delivery during labor.