ArticleReproductive health2019
Development of caesarean section prediction models: secondary analysis of a prospective cohort study in two sub-Saharan African countries.
Article in Reproductive health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Dual-output, web-based risk assessment system for cesarean section due to dystocia: integration of logistic regression and risk scoring models.Obstetrics & gynecology science · 2026Article
- Labour monitoring and decision support: a machine-learning-based paradigm.Frontiers in global women's health · 2025Article
- Prediction of Emergency Cesarean Section Using Detectable Maternal and Fetal Characteristics Among Saudi Women.International journal of women's health · 2023Article
- A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification.Computational intelligence and neuroscience · 2022Article
- Magnitude of cesarean-section and associated factors among diabetic mothers in Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia: A cross-sectional study.Frontiers in public health · 2022Article
- Prediction of odds for emergency cesarean section: A secondary analysis of the CHILD term birth cohort study.PloS one · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundCaesarean section is recommended in situations in which vaginal birth presents a greater likelihood of adverse maternal or perinatal outcomes than normal. However, it is associated with a higher risk of complications, especially when performed without a clear medical indication. Since labour attendants have no standardised clinical method to assist in this decision, statistical tools developed based on multiple labour variables may be an alternative. The objective of this paper was to develop and evaluate the accuracy of models for caesarean section prediction using maternal and foetal characteristics collected at admission and through labour.
methodThis is a secondary analysis of the World Health Organization's Better Outcomes in Labour Difficulty prospective cohort study in two sub-Saharan African countries. Data were collected from women admitted for labour and childbirth in 13 hospitals in Nigeria as well as Uganda between 2014 and 2015. We applied logistic regression to develop different models to predict caesarean section, based on the time when intrapartum assessment was made. To evaluate discriminatory capacity of the various models, we calculated: area under the curve, diagnostic accuracy, positive predictive value, negative predictive value, sensitivity and specificity.
resultsA total of 8957 pregnant women with 12.67% of caesarean births were used for model development. The model based on labour admission characteristics showed an area under the curve of 78.70%, sensitivity of 63.20%, specificity of 78.68% and accuracy of 76.62%. On the other hand, the models that applied intrapartum assessments performed better, with an area under the curve of 93.66%, sensitivity of 80.12%, specificity of 89.26% and accuracy of 88.03%.
conclusionIt is possible to predict the likelihood of intrapartum caesarean section with high accuracy based on labour characteristics and events. However, the accuracy of this prediction is considerably higher when based on information obtained throughout the course of labour.
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