Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/40427
Title: Predicting Postpartum Hemorrhage (PPH) during Cesarean Delivery Using the Leicester PPH Predict Tool: A Retrospective Cohort Study.
Authors: Dunkerton, Suzanna E.
Jeve, Yadava B.
Walkinshaw, Neil
Breslin, Eamonn
Singhal, Tanu
First Published: 28-Aug-2017
Publisher: Thieme Publishing
Citation: American Journal of Perinatology, 2017, DOI: 10.1055/s-0037-1606332
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 delivery. 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 hospital within the United Kingdom serving a multiethnic population. Data were extracted from hospital databases, and risk factors for PPH were identified. Hothorn et al 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 producing ≥1,000 and ≥ 2,000 mL blood loss. Results: The Leicester PPH predict score was then tested on the randomly selected remaining 25% (n = 6,095) of the data for internal validity. Reliability testing showed an intraclass correlation of 0.98 and mean absolute error of 239.8 mL with the actual outcome. Conclusion: The proposed toolkit enables clinicians to predict the risk of postpartum hemorrhage. As a result, preventative measures for postpartum hemorrhage could be undertaken. Further external validation of the current toolkit is required.
DOI Link: 10.1055/s-0037-1606332
ISSN: 0735-1631
eISSN: 1098-8785
Links: https://www.thieme-connect.de/products/ejournals/abstract/10.1055/s-0037-1606332
http://hdl.handle.net/2381/40427
Embargo on file until: 28-Aug-2018
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2017, Thieme Publishing. Deposited with reference to the publisher’s open access archiving policy.
Description: The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.
Appears in Collections:Published Articles, Dept. of Computer Science

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