Dunkerton, S.E., Jeve, Y.B., Walkinshaw, N. et al. (2 more authors) (2018) Predicting postpartum hemorrhage (PPH) during cesarean delivery using the Leicester PPH Predict tool: a retrospective cohort study. American Journal of Perinatology, 35 (2). pp. 163-169. ISSN 0735-1631
Abstract
Objective: The aim of the present study was to develop a toolkit combining various risk factors to predict the risk of developing a postpartum hemorrhage (PPH) during a cesarean section. Study Design: A retrospective cohort study of 24,230 women who had cesarean delivery between January 2003 and December 2013 at a tertiary care teaching ho spital within the United Kingdom serving a multi-ethnic population. Data was extracted from hospital databases and risk factors for PPH were identified. Hothorn et al.s Recursive Partitioning algorithm was used to infer a conditional decision tree. For each of the identified combinations of risk factors two probabilities were calculated: the probability of a patient pro ducing 1000ml blood loss and 2000ml blood loss. Results: The Leicester PPH Predict Score was then tested on the randomly s elected remaining 25% (n=6095) of the data for internal validity. Reliability te sting showed intraclass correlation of 0.98 and mean absolute error 239.8ml with the actual outcome. Conclusion: The proposed toolkit, which is available online, enables clinicians to predict the risk of postpartum hemorrhage. As a result, preventative measures f or postpartum hemorrhage could be undertaken. Further external validation of the current toolkit is required.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Thieme Medical Publishers. This is an author produced version of a paper subsequently published in the American Journal of Perinatology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Postpartum hemorrhage; Score; Cesarean delivery; Risk assessment tool; Machine learning; Recursive partitioning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Jan 2019 09:20 |
Last Modified: | 22 Jan 2019 09:23 |
Published Version: | https://doi.org/10.1055/s-0037-1606332 |
Status: | Published |
Publisher: | Thieme Publishing |
Refereed: | Yes |
Identification Number: | 10.1055/s-0037-1606332 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:140384 |