Applying automated machine learning to predict mode of delivery using ongoing intrapartum data in laboring patients.

OBJECTIVE To develop and validate a machine learning model to predict the probability of a vaginal delivery (Partometer) using data iteratively obtained during labor from the electronic health record. STUDY DESIGN A retrospective cohort study of deliveries at an academic, tertiary care, hospital from 2013-2019 who had at least 2 cervical exams. The population was divided into those delivered by physicians with nulliparous term singleton vertex (NTSV) cesarean delivery rates < 23.9% (Partometer cohort) and the remainder (Control cohort). The cesarean rate among this population of lower risk patients is a standard metric by which to compare provider rates; <23.9% was the Healthy People 2020 goal. A supervised automated machine learning approach was applied to generate a model for each population. The primary outcome was accuracy of the model developed on the Partometer cohort at 4 hours from admission. Secondary outcomes included discrimination ability (ROC-AUC), precision-recall AUC, and calibration of the Partometer. To assess generalizability, we compared the performance and clinical predictors identified by the Partometer to the Control model. RESULTS There were 37,932 deliveries during the study period; after exclusions, 9,385 deliveries were included in the Partometer cohort and 19,683 in the Control cohort. Accuracy of predicting vaginal delivery at 4 hours was 87.1% for the Partometer (ROC-AUC 0.82). Clinical predictors of greatest importance in the stacked Intrapartum Partometer Model included the Admission Model prediction and ongoing measures of dilatation and station, which mirrored those found in the Control population. CONCLUSION Using automated machine learning and intrapartum factors improved the accuracy of prediction of probability of a vaginal delivery over both previously published models based on logistic regression. Harnessing real-time data and machine learning could represent the bridge to generating a truly prescriptive tool to augment clinical decision making, predict labor outcomes, and reduce maternal and neonatal morbidity.