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dc.contributor.authorSexton, Jessica K.
dc.contributor.authorCoory, Michael
dc.contributor.authorKumar, Sailesh
dc.contributor.authorSmith, Gordon
dc.contributor.authorGordon, Adrienne
dc.contributor.authorChambers, Georgina
dc.contributor.authorPereira, Gavin
dc.contributor.authorRaynes-Greenow, Camille
dc.contributor.authorHilder, Lisa
dc.contributor.authorMiddleton, Philippa
dc.contributor.authorBowman, Anneka
dc.contributor.authorLieske, Scott N.
dc.contributor.authorWarrilow, Kara
dc.contributor.authorMorris, Jonathan
dc.contributor.authorEllwood, David
dc.contributor.authorFlenady, Vicki
dc.date.accessioned2020-12-22T18:55:31Z
dc.date.available2020-12-22T18:55:31Z
dc.date.issued2020-12-16
dc.date.submitted2020-03-03
dc.identifier.others41512-020-00089-w
dc.identifier.other89
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/315418
dc.description.abstractAbstract: Background: Despite advances in the care of women and their babies in the past century, an estimated 1.7 million babies are born still each year throughout the world. A robust method to estimate a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform decision-making around the timing of birth to reduce the risk of stillbirth from 35 weeks of gestation in Australia, a high-resource setting. Methods: This is a protocol for a cross-sectional study of all late-pregnancy births in Australia (2005–2015) from 35 weeks of gestation including 5188 stillbirths among 3.1 million births at an estimated rate of 1.7 stillbirths per 1000 births. A multivariable logistic regression model will be developed in line with current TransparentReporting of a multivariable prediction model forIndividualPrognosis orDiagnosis (TRIPOD) guidelines to estimate the gestation-specific probability of stillbirth with prediction intervals. Candidate predictors were identified from systematic reviews and clinical consultation and will be described through univariable regression analysis. To generate a final model, elimination by backward stepwise multivariable logistic regression will be performed. The model will be internally validated using bootstrapping with 1000 repetitions and externally validated using a temporally unique dataset. Overall model performance will be assessed with R2, calibration, and discrimination. Calibration will be reported using a calibration plot with 95% confidence intervals (α = 0.05). Discrimination will be measured by the C-statistic and area underneath the receiver-operator curves. Clinical usefulness will be reported as positive and negative predictive values, and a decision curve analysis will be considered. Discussion: A robust method to predict a pregnant woman’s individualized risk of late-pregnancy stillbirth is needed to inform timely, appropriate care to reduce stillbirth. Among existing prediction models designed for obstetric use, few have been subject to internal and external validation and many fail to meet recommended reporting standards. In developing a risk prediction model for late-gestation stillbirth with both providers and pregnant women in mind, we endeavor to develop a validated model for clinical use in Australia that meets current reporting standards.
dc.languageen
dc.publisherBioMed Central
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectProtocol
dc.subjectFetal death
dc.subjectStillbirth
dc.subjectPrediction
dc.subjectPrognostic
dc.subjectRisk
dc.subjectObstetrics
dc.subjectPerinatal
dc.subjectAUROC
dc.titleProtocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia
dc.typeArticle
dc.date.updated2020-12-22T18:55:30Z
prism.issueIdentifier1
prism.publicationNameDiagnostic and Prognostic Research
prism.volume4
dc.identifier.doi10.17863/CAM.62525
dcterms.dateAccepted2020-10-29
rioxxterms.versionofrecord10.1186/s41512-020-00089-w
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn2397-7523
pubs.funder-project-idNational Health and Medical Research Council (1116640)


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)