Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study

J. Guedalia, Y. Sompolinsky, M. Novoselsky Persky, S. M. Cohen, D. Kabiri, S. Yagel, R. Unger, M. Lipschuetz

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Objective: To create a personalised machine learning model for prediction of severe adverse neonatal outcomes (SANO) during the second stage of labour. Design: Retrospective Electronic-Medical-Record (EMR) -based study. Population: A cohort of 73 868 singleton, term deliveries that reached the second stage of labour, including 1346 (1.8%) deliveries with SANO. Methods: A gradient boosting model was created, analysing 21 million data points from antepartum features (e.g. gravidity and parity) gathered at admission to the delivery unit, and intrapartum data (e.g. cervical dilatation and effacement) gathered during the first stage of labour. Deliveries were allocated to high-risk and low-risk groups based on the Youden index to maximise sensitivity and specificity. Main outcome measures: SANO was defined as either umbilical cord pH levels ≤7.1 or 1-minute or 5-minute Apgar score ≤7. Results: The model for prediction of SANO yielded an area under the receiver operating curve (AUC) of 0.761 (95% CI 0.748–0.774). A third of the cohort (33.5%, n = 24 721) were allocated to a high-risk group for SANO, which captured up to 72.1% of these cases (odds ratio 5.3, 95% CI 4.7–6.0; high-risk versus low-risk groups). Conclusions: Data acquired throughout the first stage of labour can be used to predict SANO during the second stage of labour using a machine learning model. Stratifying parturients at the beginning of the second stage of labour in a ‘time out’ session, can direct a personalised approach to management of this challenging aspect of labour, as well as improve allocation of staff and resources. Tweetable abstract: Personalised prediction score for severe adverse neonatal outcomes in labour using machine learning model.

Original languageEnglish
Pages (from-to)1824-1832
Number of pages9
JournalBJOG: An International Journal of Obstetrics and Gynaecology
Volume128
Issue number11
DOIs
StatePublished - Oct 2021

Bibliographical note

Publisher Copyright:
© 2021 John Wiley & Sons Ltd.

Funding

This research was supported by the Ministry of Science & Technology, Israel (ML)

FundersFunder number
Ministry of science and technology, Israel

    Keywords

    • Machine learning
    • neonatal outcomes
    • obstetrics
    • personalised medicine
    • second stage of labour

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