International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
Development and validation of a multivariable logistic regression model for predicting epidural-related maternal fever during labor analgesia: A retrospective cohort study.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Ma Junyang, Lu Ping, Sun Meiqi, Cheng Jing
Аннотация
BACKGROUND: Epidural 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).
METHODS: We 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).
RESULTS: Among 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.
CONCLUSION: A multivariable logistic regression model using routinely available intrapartum variables demonstrated stable discrimination, calibration, and potential clinical utility for ERMF risk assessment and bedside implementation.
METHODS: We 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).
RESULTS: Among 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.
CONCLUSION: A multivariable logistic regression model using routinely available intrapartum variables demonstrated stable discrimination, calibration, and potential clinical utility for ERMF risk assessment and bedside implementation.