ArticleBMC pregnancy and childbirth2021
Nomogram for perinatal prediction of intrapartum fever: a retrospective case-control study.
Article in BMC pregnancy and childbirth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 16 citations in OpenAlex.
- Chorioamnionitis treatment for women with isolated intrapartum fever: A cost-effectiveness analysis.Pregnancy (Hoboken, N.J.) · 2026Article
- Multicenter validation of a machine learning model for predicting intrapartum high fever in parturients receiving labor analgesia.Frontiers in medicine · 2026Article
- Development and validation of a machine learning model for predicting intrapartum fever using pre-labor analgesia clinical indicators: a multicenter retrospective study.BMC pregnancy and childbirth · 2025Article
- Machine learning algorithms to predict epidural-related maternal fever: a retrospective study.Frontiers in pharmacology · 2025Article
- Machine Learning-Driven Intrapartum Fever Prediction: A Comprehensive Large Retrospective Cohort Study Integrating Inflammatory and Obstetric Markers.International journal of general medicine · 2025Article
- Development of a Machine Learning Model to Predict Epidural-Related Maternal Fever During Labor Analgesia: A Multi-Algorithm Comparative Study with Prospective Implementation Framework.International journal of women's health · 2025Article
- Article
- Retrospective study of intrapartum fever in term pregnancies and adverse obstetric and neonatal outcomes.PeerJ · 2022Article
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8 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveTo explore the risk factors for intrapartum fever and to develop a nomogram to predict the incidence of intrapartum fever.
methodsThe general demographic characteristics and perinatal factors of 696 parturients who underwent vaginal birth at the Affiliated Hospital of Xuzhou Medical University from May 2019 to April 2020 were retrospectively analysed. Data was collected from May 2019 to October 2019 on 487 pregnant women who formed a training cohort. A multivariate logistic regression model was used to identify the independent risk factors associated with intrapartum fever during vaginal birth, and a nomogram was developed to predict the occurrence. To verify the nomogram, data was collected from January 2020 to April in 2020 from 209 pregnant women who formed a validation cohort.
resultsThe incidence of intrapartum fever in the training cohort was found in 72 of the 487 parturients (14.8%), and the incidence of intrapartum fever in the validation cohort was 31 of the 209 parturients (14.8%). Multivariate logistic regression analysis showed that the following factors were significantly related to intrapartum fever: primiparas (odds ratio [OR] 2.43; 95% confidence interval [CI] 1.15-5.15), epidural labour analgesia (OR 2.89; 95% CI 1.23-6.82), premature rupture of membranes (OR 2.37; 95% CI 1.13-4.95), second stage of labour ≥ 120 min (OR 4.36; 95% CI 1.42-13.41), amniotic fluid pollution degree III (OR 10.39; 95% CI 3.30-32.73), and foetal weight ≥ 4000 g (OR 7.49; 95% CI 2.12-26.54). Based on clinical experience and previous studies, the duration of epidural labour analgesia also appeared to be a meaningful factor for intrapartum fever; therefore, these seven variables were used to develop a nomogram to predict intrapartum fever in parturients. The nomogram achieved a good area under the ROC curve of 0.86 and 0.81 in the training and in the validation cohorts, respectively. Additionally, the nomogram had a well-fitted calibration curve, which also showed excellent diagnostic performance.
conclusionWe constructed a model to predict the occurrence of fever during childbirth and developed an accessible nomogram to help doctors assess the risk of fever during childbirth. Such assessment may be helpful in implementing reasonable treatment measures.
trial registrationClinical Trial Registration: ( www.chictr.org.cn ChiCTR2000035593 ).
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