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

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

SUBMITTER: Riveros-Perez E 

PROVIDER: S-EPMC10201770 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature