A random forest machine learning model using fetal heart rate features accurately identified high-risk pre-term pregnancies with an AUC of 0.88 (95% CI 0.86-0.90) on internal validation.
Case-Control (n=8,881)
Does machine learning analysis of antepartum fetal heart rate accurately predict adverse outcomes in pre-term pregnancies?
Machine learning analysis of fetal heart rate can accurately stratify risk in pre-term pregnancies, potentially supporting earlier clinical decision-making to prevent adverse outcomes.
Effect estimate: AUC 0.88 (95% CI 0.86-0.90)
Fetal heart rate (FHR) monitoring is ubiquitous in antenatal care, yet human visual interpretation poorly predicts adverse pregnancy outcomes. Meanwhile, preterm gestations carry a high burden of stillbirth and severe fetal compromise, where earlier identification of high-risk pregnancies may justify iatrogenic preterm delivery to prevent avoidable fetal death. We analyzed 4867 antepartum FHR recordings from pre-term pregnancies meeting at least one of ten adverse outcome criteria alongside 4014 term uncomplicated controls. Seven clinically validated FHR features were extracted from each trace, and six machine-learning classifiers were trained on 80% of the data (7105 samples) using k-fold cross-validation; the remaining 20% (1776 samples) formed an internal validation cohort. The random forest demonstrated the best performance, achieving an area under the receiver-operating characteristic curve (AUC) of 0.88 (95% confidence interval CI 0.87–0.88) during training and 0.88 (95% CI 0.86–0.90) on validation, with good calibration (Brier score 0.14). Median AUC across individual adverse outcomes was 0.85 (interquartile range IQR 0.81–0.89) and exceeded 0.80 at all gestational ages assessed; sensitivity and specificity at the Youden threshold were 76.2% and 87.5%, respectively. Decision-curve analysis demonstrated net benefit across a range of clinically relevant probability thresholds. These findings indicate that data-driven interpretation of antepartum FHR can stratify risk in pre-term pregnancies with high accuracy and may support earlier, evidence-based clinical decision-making, particularly in resource-limited settings where specialist expertise is limited.
Jones et al. (Wed,) conducted a case-control in High-risk pre-term pregnancies (n=8,881). Machine learning classification of fetal heart rate (FHR) features was evaluated on Prediction of adverse pregnancy outcomes (AUC 0.88, 95% CI 0.86-0.90). A random forest machine learning model using fetal heart rate features accurately identified high-risk pre-term pregnancies with an AUC of 0.88 (95% CI 0.86-0.90) on internal validation.