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.

Prediction of Failure to Progress after Labor Induction: A Multivariable Model Using Pelvic Ultrasound and Clinical Data

“…The authors concluded that the AUC of prediction ranged from 0.596 to 0.773. The model with the highest predictive power was that of Levine et al (AUC 0.773), followed by those of Hernandez et al (AUC 0.762) and Rossi et al (AUC 0.752) [28]. However, another study evaluating 78 multivariate models concluded that none of them should be applied clinically [27].…”

“…We acknowledge that the proposed model is pending validation. In this regard, very few published models have been externally validated [27,28]. Many authors have proposed models to predict a successful delivery.…”

“…However, most were retrospective and had poor reproducibility [20,[29][30][31]. In a Spanish study, external validation of 12 prediction models was performed using a cohort of 468 patients undergoing labor induction [28]. The authors concluded that the AUC of prediction ranged from 0.596 to 0.773.…”

External validation and updating of the Rossi nomogram for predicting cesarean delivery following induction: Is the Bishop score valuable?

“…This nomogram has previously been validated in a different cohort. In 2021, Lopez-Jimenez et al [17]. validated this nomogram in a prospective cohort of Spanish women, with an AUC of 0.752 (95% CI 0.707-0.797) when compared with an AUC of 0.787 for the model of Rossi et al, showing that the model's performance was modest.…”

“…The strengths of our study include the comparison of the original and adapted models with the use of DCA to aid in counseling patients as they make decisions regarding induction. In the validation study performed by Lopez-Jimenez et al [17], after the DCA, they demonstrated that the Rossi model was only adequate, with probabilities ranging between 0.10 and 0.55. However, in our study, the Rossi model and the modi ed models added net bene ts when decisions were made using the model by a clinician or patient, with predicted probabilities of 0-60%.…”