Prediction of neonatal subgaleal hemorrhage using first stage of labor data: A machine-learning based model

Joshua Guedalia; Michal Lipschuetz; Lina Daoud-Sabag; Sarah M Cohen; Michal NovoselskyPersky; Simcha Yagel; Ron Unger; Gilad Karavani

doi:10.1016/j.jogoh.2022.102320

Prediction of neonatal subgaleal hemorrhage using first stage of labor data: A machine-learning based model

J Gynecol Obstet Hum Reprod. 2022 Mar;51(3):102320. doi: 10.1016/j.jogoh.2022.102320. Epub 2022 Jan 19.

Authors

Joshua Guedalia¹, Michal Lipschuetz², Lina Daoud-Sabag³, Sarah M Cohen³, Michal NovoselskyPersky³, Simcha Yagel³, Ron Unger¹, Gilad Karavani⁴

Affiliations

¹ The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
² The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel; Department of Obstetrics and Gynecology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Israel.
³ Department of Obstetrics and Gynecology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Israel.
⁴ Department of Obstetrics and Gynecology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Israel. Electronic address: giladk84@gmail.com.

PMID: 35063719
DOI: 10.1016/j.jogoh.2022.102320

Abstract

Background: 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.

Materials and methods: 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.

Results: 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.

Conclusions: 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.

Keywords: Machine learning; Personalized medicine; Prediction; Subgaleal hemorrhage; Vacuum assisted delivery; obstetrics.

MeSH terms

Delivery, Obstetric*
Female
Hemorrhage
Humans
Infant, Newborn
Machine Learning
Pregnancy
Retrospective Studies
Vacuum Extraction, Obstetrical* / adverse effects