651 Clinical efficacy of a machine learning model for prediction of shoulder dystocia

Abstract

A newly introduced shoulder dystocia (SD) machine learning (ML) model (Tsur et al, 2019) uses maternal demographics, obstetric history, and sonographic evaluation within 5 weeks from delivery. Our primary aim was to externally validate the calculator for prediction of SD or birth injury in patients who attempted a vaginal delivery. Secondary objective was to assess the clinical utility in optimizing the number of cesarean delivery (CD) for suspected large for gestational age. We conducted a multicenter retrospective study of all non-anomalous singleton pregnancies with a sonographic estimated fetal weight (EFW) ≥ 35 weeks between January 2013 and June 2019. All people who attempted labor or underwent a cesarean delivery due to suspected fetal macrosomia with EFW ≥4000g within 4 weeks from delivery were included. A matched number of people with EFW between 3500g and 3999g were also included in the analysis. The ROC area under the curve (AUC) was used as the primary metric of the model performance and was compared to EFW (ACOG recommendations). A total of 490 people met inclusion criteria, of whom 381 (77.8%) attempted labor and 109 (22.2%) had a CD for suspected fetal macrosomia. We had 19 cases of SD or birth injury in our cohort. The ML model achieved an AUC of 0.77, significantly better than EFW alone (0.61, p-value = 0.028). Furthermore, we evaluated the clinical performance of various risk scores above 0.5 (Table). A risk score of 0.92 could identify and prevent 53% of SD or neonatal injury cases while reducing the rate of CD for suspected macrosomia by 6%. In this validation cohort, using a ML model was found to be more accurate than EFW alone in prediction of SD or neonatal birth injury. Based on these findings, we are planning a prospective study investigating efficacy of the ML model compared to the current recommendations in reducing the rate of SD, associated neonatal morbidity and cesarean delivery for the indication of macrosomia.