ArticlePloS one2018
Predictive modeling of emergency cesarean delivery.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 30 citations in OpenAlex.
- Developing and validating a risk prediction model for caesarean delivery in Northwest Amhara comprehensive specialized hospitals.BMC pregnancy and childbirth · 2025Article
- Development and validation of a risk prediction model for caesarean delivery among multiparous women.Scientific reports · 2025Article
- Labour monitoring and decision support: a machine-learning-based paradigm.Frontiers in global women's health · 2025Article
- Perinatal predictors of clinical instability at birth in late-preterm and term infants.European journal of pediatrics · 2023Article
- Prediction of Emergency Cesarean Section Using Detectable Maternal and Fetal Characteristics Among Saudi Women.International journal of women's health · 2023Article
- Office hours and caesarean section: systematic review and Meta-analysis.Research in health services & regions · 2022Review
- Use of an artificial intelligence-based rule extraction approach to predict an emergency cesarean section.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2022Article
- Prediction of odds for emergency cesarean section: A secondary analysis of the CHILD term birth cohort study.PloS one · 2022Article
- Respiratory support after delayed cord clamping: a prospective cohort study of at-risk births at ≥35Archives of disease in childhood. Fetal and neonatal edition · 2021Article
- Article
- Development of caesarean section prediction models: secondary analysis of a prospective cohort study in two sub-Saharan African countries.Reproductive health · 2019Article
- Multilevel analysis of birthplace effect on the proportion of C-Section in Colombia".Colombia medica (Cali, Colombia)Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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