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dc.contributor.authorMoraitis, Alexandros A
dc.contributor.authorShreeve, Norman
dc.contributor.authorSovio, Ulla
dc.contributor.authorPeter, Brocklehurst
dc.contributor.authorAlexander, Heazell EP
dc.contributor.authorJim, Thornton G
dc.contributor.authorRobson, Stephen C
dc.contributor.authorPapageorghiou, Aris
dc.contributor.authorSmith, Gordon CS
dc.date.accessioned2020-10-13T23:07:57Z
dc.date.available2020-10-13T23:07:57Z
dc.date.issued2020
dc.date.submitted2020-01-10
dc.identifier.issn1933-7191
dc.identifier.otherpmedicine-d-20-00081
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/311441
dc.description.abstractBackground: The effectiveness of screening for macrosomia is not well established. One of the critical elements of an effective screening program is the diagnostic accuracy of a test at predicting the condition. The objective of this study is to investigate the diagnostic effectiveness of universal ultrasonic fetal biometry in predicting the delivery of a macrosomic infant, shoulder dystocia, and associated neonatal morbidity in low- and mixed-risk populations. Methods and findings: We conducted a predefined literature search in Medline, Excerpta Medica database (EMBASE), the Cochrane library and ClinicalTrials.gov from inception to May 2020. No language restrictions were applied. We included studies where the ultrasound was performed as part of universal screening and those that included low- and mixed-risk pregnancies and excluded studies confined to high risk pregnancies. We used the estimated fetal weight (EFW) (multiple formulas and thresholds) and the abdominal circumference (AC) to define suspected large for gestational age (LGA). Adverse perinatal outcomes included macrosomia (multiple thresholds), shoulder dystocia, and other markers of neonatal morbidity. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Meta-analysis was carried out using the hierarchical summary receiver operating characteristic (ROC) and the bivariate logit-normal (Reitsma) models. We identified 41 studies that met our inclusion criteria involving 112,034 patients in total. These included 11 prospective cohort studies (N = 9986), one randomized controlled trial (RCT) (N = 367), and 29 retrospective cohort studies (N = 101,681). The quality of the studies was variable, and only three studies blinded the ultrasound findings to the clinicians. Both EFW >4,000 g (or 90th centile for the gestational age) and AC >36 cm (or 90th centile) had >50% sensitivity for predicting macrosomia (birthweight above 4,000 g or 90th centile) at birth with positive likelihood ratios (LRs) of 8.74 (95% confidence interval [CI] 6.84–11.17) and 7.56 (95% CI 5.85–9.77), respectively. There was significant heterogeneity at predicting macrosomia, which could reflect the different study designs, the characteristics of the included populations, and differences in the formulas used. An EFW >4,000 g (or 90th centile) had 22% sensitivity at predicting shoulder dystocia with a positive likelihood ratio of 2.12 (95% CI 1.34–3.35). There was insufficient data to analyze other markers of neonatal morbidity. Conclusions: In this study, we found that suspected LGA is strongly predictive of the risk of delivering a large infant in low- and mixed-risk populations. However, it is only weakly (albeit statistically significantly) predictive of the risk of shoulder dystocia. There was insufficient data to analyze other markers of neonatal morbidity.
dc.languageen
dc.publisherPublic Library of Science
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectResearch Article
dc.subjectMedicine and health sciences
dc.subjectResearch and analysis methods
dc.subjectPhysical sciences
dc.subjectBiology and life sciences
dc.titleUniversal Third Trimester Ultrasonic Screening Using Fetal Macrosomia in the Prediction of Adverse Perinatal Outcome, a Systematic Review and Meta-analysis of Diagnostic Test Accuracy.
dc.typeArticle
dc.date.updated2020-10-13T23:07:57Z
prism.issueIdentifier10
prism.publicationNameREPRODUCTIVE SCIENCES
prism.volume17
dc.identifier.doi10.17863/CAM.58534
dcterms.dateAccepted2020-09-09
rioxxterms.versionofrecord10.1371/journal.pmed.1003190
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Pajkrt, Eva
dc.contributor.orcidMoraitis, Alexandros [0000-0003-4634-1129]
dc.contributor.orcidSovio, Ulla [0000-0002-0799-1105]
dc.contributor.orcidSmith, Gordon [0000-0003-2124-0997]
dc.identifier.eissn1933-7205
pubs.funder-project-idDepartment of Health (via National Institute for Health Research (NIHR)) (15/105/01)


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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)