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dc.contributor.authorDunkerton, Suzanna E.-
dc.contributor.authorJeve, Yadava B.-
dc.contributor.authorWalkinshaw, Neil-
dc.contributor.authorBreslin, Eamonn-
dc.contributor.authorSinghal, Tanu-
dc.identifier.citationAmerican Journal of Perinatology, 2017, DOI: 10.1055/s-0037-1606332en
dc.descriptionThe 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.en
dc.description.abstractObjective: 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.en
dc.publisherThieme Publishingen
dc.rightsCopyright © 2017, Thieme Publishing. Deposited with reference to the publisher’s open access archiving policy.en
dc.titlePredicting Postpartum Hemorrhage (PPH) during Cesarean Delivery Using the Leicester PPH Predict Tool: A Retrospective Cohort Study.en
dc.typeJournal Articleen
dc.type.subtypeJournal Article-
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERINGen
pubs.organisational-group/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Computer Scienceen
Appears in Collections:Published Articles, Dept. of Computer Science

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