No abstract

We developed and externally validated a machine-learning model integrating maternal risk modifiers with fetal biometry for prediction of shoulder dystocia (ShD). The model was significantly more accurate than was prediction based on estimated fetal weight either alone or combined with maternal diabetes and was able to stratify the risk of ShD and neonatal injury in the context of suspected macrosomia. What are the clinical implications of this work? We demonstrated the potential clinical efficacy of applying this model to stratify the risk of ShD and related newborn injury among women carrying a fetus with estimated fetal weight ≥ 4000 g.

No abstract