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. 2025 Jun 11:16:1614770.
doi: 10.3389/fphar.2025.1614770. eCollection 2025.

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

Xiaohui Guo  1   2   3 , Haixia Zhang  1   3   4 , Hongliang Mei  1   3   4
Affiliations

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

Xiaohui Guo et al. Front Pharmacol. .

Abstract

Introduction: The epidural-related maternal fever (ERMF) induced by patient-controlled epidural analgesia (PCEA) remains unpredictable. Our objective is to develop ERMF prediction models using real-world data, aiming to identify pertinent contributing factors and support obstetricians in making personalized clinical decisions.

Methods: Women who used patient-controlled epidural analgesia between October 2021 and March 2023 at a tertiary hospital in Jiangsu Province were retrospectively documented. The primary outcome was the occurrence of maternal fever associated with epidural use. We developed six machine learning (ML) models and assessed the area under curve (AUC) for characteristics of subjects' performance, calibration curves, and decision curve analyses.

Results: A total of 1,492 women were enrolled, with 24.3% experiencing ERMF (362 cases). The AUC ratios between the logistic regression (LR) model and the stochastic gradient descent (SGD) models showed statistical significance (p < 0.05), while the differences between the other models were not statistically significant. In comparison to the SVM model, the LR model exhibited better calibration (Brier score: 0.193; calibration slope: 0.715; calibration intercept: 0.062). Consequently, the LR model was selected as the prediction model. Furthermore, the LR-based nomogram identified eight significant predictors of ERMF, including neutrophil percentage, first stage of labor, amniotic fluid contamination during membrane rupture, artificial rupture of membranes, chorioamnionitis, post-analgesic antimicrobials, pre-analgesic oxytocin, post-analgesic oxytocin, and dinoprostone suppositories.

Conclusion: Optimally applying logistic regression models can enable rapid and straightforward identification of ERMF risk and the implementation of rational therapeutic measures, in contrast to machine learning models.

Keywords: Nomograms; epidural-related maternal fever; machine learning; predictive model; risk assessment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flow chart illustrating patient selection.
FIGURE 2
FIGURE 2
AUC, the area under curve; LR, logistic regression; RFC, random forest classifier; SVM, support vector machine; XGB, extreme gradient boosting; MLP, specifically multi-layer perceptron; SGD, stochastic gradient descent.
FIGURE 3
FIGURE 3
The calibration curve of the machine learning models (A) and decision curve analysis of the machine learning models (B). LR, logistic regression; RFC, random forest classifier; SVM, support vector machine; XGB, extreme gradient boosting; MLP, specifically multi-layer perceptron; SGD, stochastic gradient descent.
FIGURE 4
FIGURE 4
Nomogram for the prediction of epidural-associated maternal fever after Patient-controlled epidural analgesia.
See this image and copyright information in PMC

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