Subgaleal hemorrhage (SGH) is a rare neonatal condition, mainly associated with instrumental delivery, mainly vacuum extractor (VE). The aim of this study was to develop a machine learning model that would allow a personalized prediction algorithm for Subgaleal hemorrhage (SGH) following vacuum extraction (VE), based on maternal and fetal variables collected during the first stage of labor.

A retrospective cohort study on data from a university affiliated hospital, recorded between January 2013 and February 2017. Balanced random forest algorithm was used to develop a machine learning model to predict personalized risk of the neonate developing SGH, in the eventuality that vacuum extraction was used during delivery.

During the study period, 35,552 term, singleton spontaneous or induced trials of labor deliveries were included in this study. Neonatal SGH following vacuum extraction (SGH-VE) occurred in 109 cases (0.3%). Two machine learning models were developed: a proof of concept model (model A), based on a cohort limited to the (n=2955) instances of vacuum extraction, and the clinical support model (model B), based on all spontaneous or induced trials of labor (n=35,552). The models stratified parturients into high- and low-risk groups for development of SGH-VE.

Model A showed a 2-fold increase in the high-risk group of parturients compared to the low risk group (OR=2.76, CI 95% 1.85-4.11). In model B, a 4-fold increase in the odds of SGH was observed in the high-risk group of parturients compared to the low risk group (OR=4.2, CI 2.2-8.1), while identifying 90.8% (99/109) of the SGH cases.

Our machine learning-based model stratified births to high or low risk for SGH, making it an applicable tool for personalized decision-making during labor regarding the application of VE. This model may contribute to improved neonatal outcomes.