Fetal heart rate changes and labor neuraxial analgesia: a machine learning approach

Abstract

Abstract Background Neuraxial labor analgesia has been associated with fetal heart rate changes. Fetal bradycardia is multifactorial, and predicting it poses a significant challenge to clinicians. Machine learning algorithms may assist the clinician to predict fetal bradycardia and identify predictors associated with its presentation. Methods A retrospective analysis of 1077 healthy laboring parturients receiving neuraxial analgesia was conducted. We compared a principal components regression model with tree-based random forest, ridge regression, multiple regression, a general additive model, and elastic net in terms of prediction accuracy and interpretability for inference purposes. Results Multiple regression identified combined spinal-epidural (CSE) (p = 0.02), interaction between CSE and dose of phenylephrine (p < 0.0001), decelerations (p < 0.001), and the total dose of bupivacaine (p = 0.03) as associated with decrease in fetal heart rate. Random forest exhibited good predictive accuracy (mean standard error of 0.92). Conclusion Use of CSE, presence of decelerations, total dose of bupivacaine, and total dose of vasopressors after CSE are associated with decreases in fetal heart rate in healthy parturients during labor. Prediction of changes in fetal heart rate can be approached with a tree-based random forest model with good accuracy with important variables that are key for the prediction, such as CSE, BMI, duration of stage 1 of labor, and dose of bupivacaine.

Keywords

• Fetal heart rate
• Anesthesia and Analgesia
• Machine learning