Predictive modeling of emergency cesarean delivery.

ABSTRACT:

Objective

To increase discriminatory accuracy (DA) for emergency cesarean sections (ECSs).

Study design

We 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.

Results

The 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

SUBMITTER: Campillo-Artero C 

PROVIDER: S-EPMC5779661 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature