IMR Press / CEOG / Volume 49 / Issue 3 / DOI: 10.31083/j.ceog4903076
Open Access Original Research
Validation of three models (Tolcher, Levine, and Burke) for predicting term cesarean section in Chinese population
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1 The Department of Obstetrics and Gynecology, The First Affiliated Hospital of Soochow University, 215006 Suzhou, Jiangsu, China
2 The Department of Obstetrics and Gynecology, Suzhou Ninth People's Hospital, 215200 Suzhou, Jiangsu, China
*Correspondence: (Bing Han)
These authors contributed equally.
Academic Editor: Michael H. Dahan
Clin. Exp. Obstet. Gynecol. 2022, 49(3), 76;
Submitted: 27 May 2021 | Revised: 4 August 2021 | Accepted: 31 August 2021 | Published: 19 March 2022
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

Background: Some models predicting cesarean section (CS) have been proposed, with Tolcher, Levine, and Burke model well acknowledged. Tolcher model targets nulliparous women with term labor induction; Levine model targets women with term labor induction with intact membranes and an unfavorable cervix. Burke model targets term nulliparous woman with an uncomplicated pregnancy. Our objective was to assess the predictive performance of these three models, and to disclose the variables which may predict the risk of CS in Chinese population. Methods: A retrospective study was conducted on women with singleton, term, cephalic pregnancies at a tertiary academic center (2011–2017). A predicted probability for CS was calculated for women in the dataset by the algorithm of each model. The performance of the model was evaluated for discrimination. Univariate analysis was used to screen out the factors that may increase the risk of CS. Results: The three models predicted CS as following (expressed by an area under the receiver operating characteristic curve [AUC ROC]) (in the population defined/employed by each model): Tolcher model with AUC ROC of 0.659; Levine model with 0.697; and Burke model as 0.623. Different interventional measures or characteristics of labor were also evaluated; the nulliparous and multiparous were analyzed separately. Still, most of the results were unsatisfactory (AUC ROC <0.7). Univariate analyses on the clinical parameters that may affect the incidence of CS were performed. The followings affected the incidence/probability of CS: maternal age, height, body mass index (BMI), weight gain during pregnancy, gestational age, mode of labor induction, meconium-stained amniotic fluid, presence of complications, neonatal weight/gender. Conclusion: These three models may not be suitable for predicting CS for Chinese population. Some maternal and fetal characteristics increased the risk of CS, which should be taken into account in creating some appropriate models for predicting CS in Chinese population.

Cesarean section
Induction of labor
Prediction algorithm
Prediction tool
Fig. 1.
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