Fetal Heart Rate Changes and Labor Neuraxial Analgesia: a Machine Learning Approach
- Conditions
- Fetal Bradycardia
- Registration Number
- NCT05399979
- Lead Sponsor
- Augusta University
- Brief Summary
This study aims to perform statistical inference and prediction of changes in fetal heart rate during active labor in healthy pregnant women by comparing three different machine learning methods
- Detailed Description
Purpose: This study aims to perform statistical inference and prediction of changes in fetal heart rate during active labor in healthy pregnant women by comparing three different machine learning methods. Methods: A retrospective analysis of 1077 healthy laboring parturients receiving neuraxial analgesia was conducted. We compared a principal components regression model with treebased random forest, ridge regression, multiple regression, a general additive model, and elastic net in terms of prediction accuracy and interpretability for inference purposes.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- Female
- Target Recruitment
- 1077
- Older than 18 years
- Pregnancy requiring labor analgesia
- Active labor
- Request of neuraxial analgesia per patient and/or obstetrician
- Received combined spinal-epidural technique
- Uterine tachysystole before neuraxial analgesia.
- Baseline blood pressure <90/60 mmHg.
- Third trimester hemorrhage
- Eclampsia
- Allergies to local anesthetics or fentanyl.
- Maternal fever.
- Pruritus before performance of neuraxial analgesia
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method fetal bradycardia 15 minutes fetal heart rate under 120 lpm for more than 10 minutes
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Augusta University Medical Center
🇺🇸Augusta, Georgia, United States