Risk of caesarean delivery in labour induction: a systematic review and external validation of predictive models

Abstract

Background: Despite the existence of numerous published models predicting the risk of caesarean delivery in women undergoing induction of labour (IOL), validated models are scarce.

Objectives: To systematically review and externally assess the predictive capacity of caesarean delivery risk models in women undergoing IOL.

Search strategy: Studies published up to 15 January 2021 were identified through PubMed, CINAHL, Scopus and ClinicalTrials.gov, without temporal or language restrictions.

Selection criteria: Studies describing the derivation of new models for predicting the risk of caesarean delivery in labour induction.

Data collection and analysis: Three authors independently screened the articles and assessed the risk of bias (ROB) according to the prediction model risk of bias assessment tool (PROBAST). External validation was performed in a prospective cohort of 468 pregnancies undergoing IOL from February 2019 to August 2020. The predictive capacity of the models was assessed by creating areas under the receiver operating characteristic curve (AUCs), calibration plots and decision curve analysis (DCA).

Main results: Fifteen studies met the eligibility criteria; 12 predictive models were validated. The quality of most of the included studies was not adequate. The AUC of the models varied from 0.520 to 0.773. The three models with the best discriminative capacity were those of Levine et al. (AUC 0.773, 95% CI 0.720-0.827), Hernández et al. (AUC 0.762, 95% CI 0.715-0.809) and Rossi et al. (AUC 0.752, 95% CI 0.707-0.797).

Conclusions: Predictive capacity and methodological quality were limited; therefore, we cannot currently recommend the use of any of the models for decision making in clinical practice.

Tweetable abstract: Predictive models that predict the risk of cesarean section in labor inductions are currently not applicable.

Keywords: Caesarean delivery; induction of labour; predictive model.