Risk of cesarean delivery in labor induction: a systematic review and external validation of predictive models.

BACKGROUND Despite the existence of numerous published models predicting the risk of cesarean delivery in women undergoing induction of labor (IOL), validated models are scarce. OBJECTIVES To systematically review and externally assess the predictive capacity of cesarean delivery risk models in women undergoing IOL. SEARCH STRATEGY Studies published up to until January 15, 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 cesarean delivery in labor induction. DATA COLLECTION AND ANALYSIS Three authors independently screened the articles and assessed Risk Of Bias (ROB) according to 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 (AUC), 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. AUC of the models varied from 0.520 to 0.773. The 3 models with the best discriminative capacity were those of Levine et al. (AUC: 0.773, 95% CI: 0.720-0.827), Hernandez 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.