Abstract
ObjectiveTo increase discriminatory accuracy (DA) for emergency cesarean sections (ECSs).Study designWe prospectively collected data on and studied all 6,157 births occurring in 2014 at four public hospitals located in three different autonomous communities of Spain. To identify risk factors (RFs) for ECS, we used likelihood ratios and logistic regression, fitted a classification tree (CTREE), and analyzed a random forest model (RFM). We used the areas under the receiver-operating-characteristic (ROC) curves (AUCs) to assess their DA.ResultsThe magnitude of the LR+ for all putative individual RFs and ORs in the logistic regression models was low to moderate. Except for parity, all putative RFs were positively associated with ECS, including hospital fixed-effects and night-shift delivery. The DA of all logistic models ranged from 0.74 to 0.81. The most relevant RFs (pH, induction, and previous C-section) in the CTREEs showed the highest ORs in the logistic models. The DA of the RFM and its most relevant interaction terms was even higher (AUC = 0.94; 95% CI: 0.93–0.95).ConclusionPutative fetal, maternal, and contextual RFs alone fail to achieve reasonable DA for ECS. It is the combination of these RFs and the interactions between them at each hospital that make it possible to improve the DA for the type of delivery and tailor interventions through prediction to improve the appropriateness of ECS indications.
Highlights
Maternal, and contextual risk factors (RFs) alone fail to achieve reasonable discriminant accuracy (DA) for emergency cesarean sections (ECSs). It is the combination of these RFs and the interactions between them at each hospital that make it possible to improve the DA for the type of delivery and tailor interventions through prediction to improve the appropriateness of ECS indications
A worrisome issue in obstetrics is the longstanding increase in cesarean section rates, as well as the unjustified variations in these rates in clinical practice across public and private hospitals worldwide[1,2,3]
Most RFs for ECSs should be considered putative, since they have mainly been selected by means of logistic regression models that usually lack information regarding both their goodness-of-fit and their DA [30,31,32,33,34,35,36,37,38]
Summary
A worrisome issue in obstetrics is the longstanding increase in cesarean section rates, as well as the unjustified variations in these rates in clinical practice across public and private hospitals worldwide[1,2,3]. Few current clinical guidelines and interventions target these objectives [23,24,25,26,27,28,29] Those that do are neither based on a comprehensive set of proven fetal and maternal risk factors (RFs) with high discriminant accuracy (DA) nor designed to take into account contextual factors that have been shown to be associated with both an increased rate of unnecessary ECSs and unjustified variations in clinical practice. Interventions based on average risk estimates for people both exposed and unexposed to spurious RFs could be ineffective, inefficient, and even potentially harmful [12,13,14,15,16,17,18,19,20,21,22]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
