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Our findings suggest an involvement of miRNA-200b and miRNA-429 in the pituitary regulation of human ovulation. Although it is unclear whether this altered miRNA expression profile is a cause or a result of anovulation, the levels of these molecules in the serum of anovulatory women may serve as serum biomarkers for the ovulation process.

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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.