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Updated: Jul 10, 2026
Isolation of Neonatal Extrahepatic Cholangiocytes
Published on: June 5, 2014
Establishment and validation of a nomogram for predicting preterm birth in intrahepatic cholestasis during pregnancy:
Wenchi Xie1, Landie Ji2, Dan Luo1
1Department of Obstetrics and Gynecology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Insights
A new nomogram effectively predicts preterm birth in intrahepatic cholestasis of pregnancy (ICP) patients using total bile acid levels, twin pregnancy status, height, and gestational age at diagnosis. This tool aids clinical management to improve maternal and infant safety.
Area of Science:
- Obstetrics and Gynecology
- Maternal-Fetal Medicine
- Clinical Prediction Modeling
Background:
- Intrahepatic cholestasis of pregnancy (ICP) is a serious condition that can lead to adverse pregnancy outcomes, including preterm birth.
- Accurate prediction of preterm birth in ICP patients is crucial for timely intervention and improved maternal-fetal outcomes.
- Existing prediction methods may not fully capture the complexity of preterm birth risk in ICP.
Purpose of the Study:
- To develop and validate a nomogram for predicting the risk of preterm birth in pregnant women diagnosed with ICP.
- To identify key clinical and laboratory factors associated with preterm birth in the ICP population.
- To provide a practical tool for clinicians to assist in the management and intervention strategies for ICP patients.
Main Methods:
- Retrospective observational study of 257 pregnant women with ICP.
- Utilized least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression to identify predictive factors.
- Developed and validated a nomogram using height, twin pregnancy, gestational age at diagnosis, and total bile acid levels; evaluated using C-index, AUC, ROC curves, calibration curves, and DCA.
Main Results:
- A nomogram incorporating height, twin pregnancy, gestational age at diagnosis, and total bile acid level was developed.
- The nomogram demonstrated good predictive accuracy and consistency in both training (C-index=0.864, AUC=0.864) and test (C-index=0.835, AUC=0.836) sets.
- Decision curve analysis confirmed the clinical applicability of the developed nomogram.
Conclusions:
- The combination of total bile acid level, twin pregnancy, height, and gestational age at diagnosis effectively predicts preterm birth in ICP patients.
- The developed nomogram serves as a valuable tool for identifying high-risk pregnancies, guiding clinical management.
- Implementation of this nomogram can potentially reduce maternal and infant safety issues associated with preterm birth in ICP.
Objective:
This study aimed to develop and evaluate a nomogram for predicting preterm birth in patients with intrahepatic cholestasis of pregnancy (ICP), with a view to assisting clinical management and intervention.
Methods:
This retrospective observational study included 257 pregnant women with ICP from Sichuan Provincial People's Hospital between January 1, 2022 and July 30, 2024. The routine clinical and laboratory information of these patients were also collected. We used the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analysis to investigate the association between clinical and laboratory data and preterm birth in ICP patients. A nomogram was developed to predict the likelihood of preterm birth in ICP patients. The prediction accuracy of the model was evaluated by consistency index (C-index), receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration curve. Decision curve analysis (DCA) was used to evaluate its applicability in clinical practice.
Results:
Among the 257 ICP patients, 56 (21.79%) were diagnosed with preterm birth. Cases were randomly divided into a training set (154 cases) and a test set (103 cases). A nomogram was developed to predict preterm birth in ICP patients based on height, twin pregnancy (TP), gestational age at diagnosis (GA at diagnosis), and total bile acid level (TBA) at diagnosis. The calibration curve of the training set was close to the diagonal (C-index = 0.864), and the calibration curve of the test set was also close to the diagonal (C-index = 0.835). These results indicate that the model has a good consistency. The AUC of the training group and the test group were 0.864 and 0.836, respectively, indicating the good accuracy of the model. The DCA reveals that this nomogram could be applied to clinical practice.
Conclusion:
The combination of TBA level, TP, height and GA at diagnosis is an effective model for identifying preterm birth in ICP patients. These results will help guide the clinical management and treatment of patients with ICP, thereby reducing maternal and infant safety issues caused by preterm birth.
