Review of Models Predicting Emergency Caesarean Births
Author Information
Author(s): Alexandra Hunt, Laura Bonnett, Jon Heron, Michael Lawton, Gemma Clayton, Gordon Smith, Jane Norman, Louise Kenny, Deborah Lawlor, Abi Merriel, Sheelagh McGuiness, Anna Davies, Dame Tina Lavender, Christy Burden, Jonathan Ives, Simon Grant, Sherif Abdel‐Fattah, Danya Bakhbakhi, Andrew Demetri, Mairead Black, Sam Finnikin, Amie Wilson, Alexandra Freeman, Pete Blair, Kate Birchenall, Joanne Johnson, Amber Marshall
Primary Institution: The University of Liverpool
Hypothesis
What are the existing clinical prediction models for the risk of emergency caesarean births and how accurate are they?
Conclusion
There is a pressing need for a model that accurately predicts the timely risk of an emergency caesarean birth for women across diverse clinical backgrounds.
Supporting Evidence
- 8083 studies were screened, resulting in 63 fulfilling the inclusion criteria.
- 56 studies reported the development of new prediction models.
- 33 studies were graded as low risk of bias.
- Only 8 models were externally validated and considered for clinical use.
- Maternal age, height, and gestational age were the most frequently occurring predictors.
Takeaway
This study looked at different ways to predict if a woman might need an emergency caesarean section during childbirth, finding that many models exist but only a few are reliable and easy to use.
Methodology
The review included studies developing and validating multivariable prediction models for emergency caesarean births, using databases like MEDLINE and CINAHL.
Potential Biases
Over half of the models reviewed were assessed as high risk of bias.
Limitations
Many models had high risk of bias and were not recalibrated since their development.
Participant Demographics
The studies included data on 4,476,307 women, with populations varying from nulliparous to mixed nulliparous and multiparous women.
Digital Object Identifier (DOI)
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