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

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.