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Machine learning algorithms to predict epidural-related maternal fever: a retrospective study.

Xiaohui Guo1,2,3, Haixia Zhang1,3,4, Hongliang Mei1,3,4

  • 1Department of Pharmacy, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.

Frontiers in Pharmacology
|June 26, 2025
PubMed
Summary

Epidural-related maternal fever (ERMF) prediction is challenging. A logistic regression model effectively identified eight key risk factors, enabling better clinical decision-making for pregnant patients receiving epidural analgesia.

Keywords:
Nomogramsepidural-related maternal fevermachine learningpredictive modelrisk assessment

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Area of Science:

  • Obstetrics and Gynecology
  • Clinical Informatics
  • Epidemiology

Background:

  • Epidural-related maternal fever (ERMF) is an unpredictable complication of patient-controlled epidural analgesia (PCEA).
  • Accurate prediction of ERMF is crucial for personalized obstetric care and timely intervention.

Purpose of the Study:

  • To develop and validate predictive models for ERMF using real-world data.
  • To identify significant contributing factors for ERMF to aid clinical decision-making.

Main Methods:

  • Retrospective analysis of 1,492 women receiving PCEA between October 2021 and March 2023.
  • Development and comparison of six machine learning models, including logistic regression (LR) and support vector machine (SVM).
  • Evaluation of model performance using Area Under the Curve (AUC), calibration curves, and decision curve analyses.

Main Results:

  • 24.3% of women (362 cases) experienced ERMF.
  • The LR model demonstrated superior calibration compared to the SVM model (Brier score: 0.193).
  • Eight significant predictors of ERMF were identified: neutrophil percentage, labor stage 1, amniotic fluid contamination, artificial membrane rupture, chorioamnionitis, and specific oxytocin/antimicrobial/dinoprostone uses.

Conclusions:

  • Logistic regression models offer a practical and effective approach for predicting ERMF risk.
  • Identifying key predictors allows for more targeted clinical management and potentially reduces ERMF incidence.