Evidence map›Paper›PMID 41885065›Full record

ArticleInternational journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics2026

Development and validation of a multivariable logistic regression model for predicting epidural-related maternal fever during labor analgesia: A retrospective cohort study.

Junyang Ma, Ping Lu, Meiqi Sun, Jing Cheng

Abstract readValidation Study
In one paragraph

Article in International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Junyang MaDepartment of Anesthesiology, Maternal and Child Health Hospital of Hubei Province, Wuhan, Hubei, China.ORCID https://orcid.org/0009-0003-9288-9483
Ping LuDepartment of Anesthesiology, Maternal and Child Health Hospital of Hubei Province, Wuhan, Hubei, China.
Meiqi SunHuazhong University of Science and Technology Tongji Medical College Union Hospital, Wuhan, Hubei, China.
Jing ChengDepartment of Anesthesiology, Maternal and Child Health Hospital of Hubei Province, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEpidural labor analgesia is associated with intrapartum maternal fever, termed epidural-related maternal fever (ERMF). Reliable risk stratification tools to support intrapartum monitoring and management are limited. We aimed to develop and internally validate a multivariable prediction model for ERMF among parturients receiving programmed intermittent epidural bolus (PIEB).

methodsWe conducted a single-center retrospective cohort study of term (37-41 weeks), singleton, cephalic pregnancies with vaginal delivery who received PIEB between January and December 2024. ERMF was defined as axillary temperature ≥38.0°C confirmed by two measurements 30 min apart after PIEB initiation. Candidate predictors were prespecified based on clinical plausibility. Univariable and multivariable logistic regression were performed with multicollinearity assessed by variance inflation factor. Model discrimination was evaluated by area under the receiver operating characteristic curve (AUC) and precision-recall AUC (PR-AUC). Internal validation used 1000 bootstrap resamples. Calibration was assessed by bootstrap-corrected calibration curves and Brier score. Clinical utility was examined using decision curve analysis (DCA).

resultsAmong 2800 women, 308 (11.0%) developed ERMF. Independent predictors included longer first-stage labor duration, primiparity, artificial rupture of membranes, amniotic fluid contamination ≥grade 2, and higher cumulative oxytocin dose. The model achieved an AUC of 0.75, PR-AUC of 0.254, and Brier score of 0.090; bootstrap validation showed stable performance. DCA suggested net benefit over treat-all/treat-none strategies across a wide range of threshold probabilities.

conclusionA multivariable logistic regression model using routinely available intrapartum variables demonstrated stable discrimination, calibration, and potential clinical utility for ERMF risk assessment and bedside implementation.

Indexed as

Analgesia, EpiduralAnalgesia, ObstetricalFeverAdultFemaleHumansLogistic ModelsPregnancyRetrospective StudiesRisk AssessmentROC Curveepidural analgesiaepidural‐related maternal feverlabor analgesialogistic regressionmaternal feverobstetric anesthesiarisk prediction model

Identifiers

PMID41885065
PMCPMC13505213

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.