Literature DB >> 19670397

Validation of models that predict Cesarean section after induction of labor.

C J M Verhoeven1, A Oudenaarden, M A A Hermus, M M Porath, S G Oei, B W J Mol.   

Abstract

OBJECTIVE: Models for the prediction of Cesarean delivery after induction of labor can be used to improve clinical decision-making. The objective of this study was to validate two existing models, published by Peregrine et al. and Rane et al., for the prediction of Cesarean section after induction of labor.
METHODS: We studied consecutive women in whom labor was induced. In all women, we recorded maternal age, height, body mass index, parity, gestational age and the Bishop score prior to induction. Cervical length was measured by transvaginal ultrasound immediately prior to induction of labor. The primary end-point was delivery by Cesarean section. The calibration of the two prediction models was assessed by comparison of predicted and observed Cesarean delivery rates. The discriminative capacity of the models, i.e. the ability of the models to distinguish subjects who had Cesarean section from those who did not (discrimination), was assessed by receiver-operating characteristics (ROC) analysis.
RESULTS: We included 240 women in the study, of whom 27 (11%) had Cesarean delivery. The capacity of cervical length in the prediction of Cesarean delivery was limited. In our study population, both prediction models overestimated the risk of Cesarean delivery. Calibration was better for the Peregrine et al. model than for the Rane et al. model, and the two models had areas under the ROC curve of 0.76 and 0.67, respectively.
CONCLUSION: Current models that predict the occurrence of Cesarean section after induction of labor have only a moderate predictive capacity when applied within a Dutch practice. We do not recommend the use of these prediction models in clinical practice.

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Year:  2009        PMID: 19670397     DOI: 10.1002/uog.7315

Source DB:  PubMed          Journal:  Ultrasound Obstet Gynecol        ISSN: 0960-7692            Impact factor:   7.299


  8 in total

Review 1.  Traditional statistical methods for evaluating prediction models are uninformative as to clinical value: towards a decision analytic framework.

Authors:  Andrew J Vickers; Angel M Cronin
Journal:  Semin Oncol       Date:  2010-02       Impact factor: 4.929

2.  Manipal Cervical Scoring System by Transvaginal Ultrasound in Predicting Successful Labour Induction.

Authors:  Neha Bajpai; Rajesh Bhakta; Pratap Kumar; Lavanya Rai; Shripad Hebbar
Journal:  J Clin Diagn Res       Date:  2015-05-01

Review 3.  Timing of delivery in women with diabetes in pregnancy.

Authors:  Howard Berger; Nir Melamed
Journal:  Obstet Med       Date:  2014-01-15

4.  A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification.

Authors:  Ashir Javeed; Liaqat Ali; Abegaz Mohammed Seid; Arif Ali; Dilpazir Khan; Yakubu Imrana
Journal:  Comput Intell Neurosci       Date:  2022-06-06

5.  Changes in shear wave speed pre- and post-induction of labor: a feasibility study.

Authors:  L C Carlson; S T Romero; M L Palmeri; A Muñoz Del Rio; S M Esplin; V M Rotemberg; T J Hall; H Feltovich
Journal:  Ultrasound Obstet Gynecol       Date:  2015-07       Impact factor: 7.299

6.  Development and Validation of a Risk Prediction Model for Cesarean Delivery After Labor Induction.

Authors:  Valery A Danilack; Jennifer A Hutcheon; Elizabeth W Triche; David D Dore; Janet H Muri; Maureen G Phipps; David A Savitz
Journal:  J Womens Health (Larchmt)       Date:  2019-10-29       Impact factor: 2.681

7.  "Early rupture of membranes" during induced labor as a risk factor for cesarean delivery in term nulliparas.

Authors:  Seung Mi Lee; Jeong Woo Park; Chan-Wook Park; Bo Hyun Yoon
Journal:  PLoS One       Date:  2012-06-29       Impact factor: 3.240

8.  [The opening of the internal cervical os predicts cervical ripening better than Bishop's score in nulliparous women at 41 weeks gestation].

Authors:  Mehdi Kehila; Hassine Saber Abouda; Rim Ben Hmid; Omar Touhami; Cyrine Ben Miled; Imen Godcha; Sami Mahjoub; Mohamed Badis Chanoufi
Journal:  Pan Afr Med J       Date:  2016-11-29
  8 in total

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