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

• Published in: BJOG : an international journal of obstetrics and gynaecology Vol. 129; no. 5; pp. 685 - 695
• Main Authors: López‐Jiménez, N, García‐Sánchez, F, Hernández‐Pailos, R, Rodrigo‐Álvaro, V, Pascual‐Pedreño, A, Moreno‐Cid, M, Delgado‐Rodríguez, M, Hernández‐Martínez, A
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01-04-2022
• Subjects: Caesarean delivery; Cesarean section; Decision making; Health risks; Induced labor; induction of labour; Prediction models; Predictive analytics; predictive model; Validity; Caesarean delivery; induction of labour; predictive model
• ISSN: 1470-0328; 1471-0528
• DOI: 10.1111/1471-0528.16947

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