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已发表论文
产时发热预测分娩方式及新生儿重症监护病房入住的动态列线图的开发与验证：一项回顾性队列研究
Authors Ni J , Zhang D, Ding Y, Ding H , Munemo ZPR , Zhang H
Received 4 June 2025
Accepted for publication 20 September 2025
Published 30 September 2025 Volume 2025:17 Pages 3385—3400
DOI https://doi.org/10.2147/IJWH.S544623
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Vinay Kumar
Jianzhi Ni,1,2 Dan Zhang,3 Yuling Ding,2 Hongmei Ding,4 Zvikomborero Panashe Rejoice Munemo,1 Hongxiu Zhang1 On behalf of the Jiangsu Collaborative Group for Intrapartum Fever & Maternal-Neonatal Outcome Prediction
1Department of Obstetrics and Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China; 2Department of Obstetrics and Gynecology, Huai’an Maternal and Child Health Care Hospital, Huai’an, Jiangsu, People’s Republic of China; 3Department of Obstetrics and Gynecology, Kunshan Maternity and Children’s Health Care Hospital, Kunshan, Jiangsu, People’s Republic of China; 4Department of Obstetrics and Gynecology, Taizhou Fourth People’s Hospital, Taizhou, Jiangsu, People’s Republic of China
Correspondence: Hongxiu Zhang, Department of Obstetrics and Gynecology, the First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, Jiangsu, 210029, People’s Republic of China, Email hongxiuz@njmu.edu.cn
Background: While maternal intrapartum fever is linked to adverse neonatal outcomes, predictive tools for delivery mode and neonatal intensive care unit (NICU) admission in this population remain scarce.
Objective: To develop and validate a dynamic nomogram predicting cesarean delivery and NICU admissions in women with intrapartum fever, facilitating individualized intrapartum decision-making.
Methods: This retrospective cohort study analyzed 24,784 deliveries (2019– 2021) at a tertiary center. After exclusions, 1,047 women with intrapartum fever were included in the study cohort. The dataset was randomly partitioned into training (n=837) and testing (n=210) sets. Backward stepwise multivariable logistic regression models were developed to predict cesarean delivery and neonatal intensive care unit admission. The discriminative capacity of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Calibration performance was assessed via 1000 nonparametric bootstrap resamples to generate calibration curves, with systematic quantification of agreement between predicted probabilities and observed outcomes through the Brier score and Hosmer-Lemeshow goodness-of-fit test.
Results: Predictors of cesarean delivery included advanced maternal age, hypertensive disorders, Intrapartum Antibiotic Prophylaxis (IAP), Meconium-Stained Amniotic Fluid (MSAF), Macrosomia, Postpartum Hemorrhage (PPH), Oligohydramnios, assisted reproductive technology (ART), Hypertensive Disorders of Pregnancy (HDP), Maternal tachycardia, Placental histopathology, intrapartum temperature and Method of inducing labor. Low Birth Weight (LBW), adverse obstetric history (AOH), Fetal tachycardia, Fetal bradycardia, Scarred uterus, Maternal tachycardia and MSAF predicted neonatal intensive care unit admission. The cesarean delivery model achieved AUC of 0.8 (training) and 0.783 (testing); the neonatal intensive care unit admission model showed AUC of 0.681 (training) and 0.748 (testing).
Conclusion: This nomogram provides a clinically useful tool to predict delivery mode and neonatal intensive care unit admission in women with intrapartum fever, aiding risk stratification and improving perinatal outcomes.
Keywords: cesarean delivery, dynamic risk assessment, intrapartum fever, intrapartum decision-making, neonatal outcomes
1Department of Obstetrics and Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China; 2Department of Obstetrics and Gynecology, Huai’an Maternal and Child Health Care Hospital, Huai’an, Jiangsu, People’s Republic of China; 3Department of Obstetrics and Gynecology, Kunshan Maternity and Children’s Health Care Hospital, Kunshan, Jiangsu, People’s Republic of China; 4Department of Obstetrics and Gynecology, Taizhou Fourth People’s Hospital, Taizhou, Jiangsu, People’s Republic of China
Correspondence: Hongxiu Zhang, Department of Obstetrics and Gynecology, the First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, Jiangsu, 210029, People’s Republic of China, Email hongxiuz@njmu.edu.cn
Background: While maternal intrapartum fever is linked to adverse neonatal outcomes, predictive tools for delivery mode and neonatal intensive care unit (NICU) admission in this population remain scarce.
Objective: To develop and validate a dynamic nomogram predicting cesarean delivery and NICU admissions in women with intrapartum fever, facilitating individualized intrapartum decision-making.
Methods: This retrospective cohort study analyzed 24,784 deliveries (2019– 2021) at a tertiary center. After exclusions, 1,047 women with intrapartum fever were included in the study cohort. The dataset was randomly partitioned into training (n=837) and testing (n=210) sets. Backward stepwise multivariable logistic regression models were developed to predict cesarean delivery and neonatal intensive care unit admission. The discriminative capacity of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Calibration performance was assessed via 1000 nonparametric bootstrap resamples to generate calibration curves, with systematic quantification of agreement between predicted probabilities and observed outcomes through the Brier score and Hosmer-Lemeshow goodness-of-fit test.
Results: Predictors of cesarean delivery included advanced maternal age, hypertensive disorders, Intrapartum Antibiotic Prophylaxis (IAP), Meconium-Stained Amniotic Fluid (MSAF), Macrosomia, Postpartum Hemorrhage (PPH), Oligohydramnios, assisted reproductive technology (ART), Hypertensive Disorders of Pregnancy (HDP), Maternal tachycardia, Placental histopathology, intrapartum temperature and Method of inducing labor. Low Birth Weight (LBW), adverse obstetric history (AOH), Fetal tachycardia, Fetal bradycardia, Scarred uterus, Maternal tachycardia and MSAF predicted neonatal intensive care unit admission. The cesarean delivery model achieved AUC of 0.8 (training) and 0.783 (testing); the neonatal intensive care unit admission model showed AUC of 0.681 (training) and 0.748 (testing).
Conclusion: This nomogram provides a clinically useful tool to predict delivery mode and neonatal intensive care unit admission in women with intrapartum fever, aiding risk stratification and improving perinatal outcomes.
Keywords: cesarean delivery, dynamic risk assessment, intrapartum fever, intrapartum decision-making, neonatal outcomes