Why the study?
Can a tree-based random forest model accurately predict changes in fetal heart rate in healthy parturients during labor?
Can a tree-based random forest model accurately predict changes in fetal heart rate in healthy parturients during labor?
A machine learning approach using a random forest model can accurately predict fetal heart rate decreases associated with labor neuraxial analgesia.
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May inform risk stratification during neuraxial analgesia; leaves open prospective validation before clinical use.
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.
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Riveros-Pérez et al. (2023) studied this question.
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