Please use this identifier to cite or link to this item: https://repository.monashhealth.org/monashhealthjspui/handle/1/40860
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dc.contributor.authorTeede H.J.en
dc.contributor.authorLombard C.B.en
dc.contributor.authorHarrison C.L.en
dc.contributor.authorEast C.en
dc.contributor.authorBoyle J.en
dc.date.accessioned2021-05-14T13:59:08Zen
dc.date.available2021-05-14T13:59:08Zen
dc.date.copyright2015en
dc.date.created20150224en
dc.date.issued2015-02-24en
dc.identifier.citationDiabetes Research and Clinical Practice. 107 (1) (pp 61-68), 2015. Date of Publication: 2015.en
dc.identifier.issn0168-8227en
dc.identifier.urihttps://repository.monashhealth.org/monashhealthjspui/handle/1/40860en
dc.description.abstractAim: To evaluate the addition of fasting glucose and lipids to a simple, validated risk prediction tool for gestational diabetes (GDM) applied in early pregnancy. Method(s): Women at risk of developing GDM on a validated risk prediction tool were recruited in early pregnancy into a large randomised controlled trial. Outcome measures included fasting biochemical markers (glucose, lipids) at 12-15 weeks gestation and GDM diagnosis (28 weeks gestation). Multivariable logistic regression was used to identify additional predictive biochemical variables for GDM, with corresponding receiver operator characteristic (ROC) curves generated. Unadjusted and adjusted models were derived for both the Australasian Diabetes in Pregnancy (ADIPS) and the International Association for Diabetes in Pregnancy Study Group (IADPSG) GDM diagnostic criteria. Result(s): 51 (23%) Women were diagnosed with GDM based on ADIPS criteria, with 60 (30%) diagnosed based on IADPSG criteria. In all four regression models, fasting glucose was the strongest predictor for GDM development with an odds ratio range of 4.7-6.3 (ADIPS) and 8.8-10 (IADPSG). ROC curves revealed an area under the curve of 0.79 (95% CI: 0.72-0.86) for ADIPS criteria and 0.83 (95% CI: 0.77-0.90) for IADPSG criteria for adjusted models. Conclusion(s): In a two-step approach, when applied with a validated risk prediction tool, fasting glucose in early pregnancy was predictive of GDM and incrementally improved risk identification, presenting potential for an early pregnancy, GDM risk screening strategy for streamlining of pregnancy care and opportunity for preventive intervention.Copyright © 2014 Elsevier Ireland Ltd.en
dc.languageEnglishen
dc.languageenen
dc.publisherElsevier Ireland Ltden
dc.relation.ispartofDiabetes Research and Clinical Practiceen
dc.titleRisk stratification in early pregnancy for women at increased risk of gestational diabetes.en
dc.typeArticleen
dc.type.studyortrialRandomised controlled trial-
dc.identifier.doihttp://monash.idm.oclc.org/login?url=http://dx.doi.org/10.1016/j.diabres.2014.09.006en
dc.publisher.placeIrelanden
dc.identifier.pubmedid25444356 [http://www.ncbi.nlm.nih.gov/pubmed/?term=25444356]en
dc.identifier.source600335459en
dc.identifier.institution(Harrison, Lombard, Boyle, Teede) Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia (East) Monash Women's Maternity Services, Monash Health, Melbourne, Australia (Teede) Diabetes and Vascular Medicine Unit, Monash Health, Melbourne, Australiaen
dc.description.addressC.L. Harrison, School of Public Health and Preventive Medicine-Monash Univ. Level 1, 43-51 Kanooka Grove, Clayton, VIC 31168, Australiaen
dc.description.publicationstatusEmbaseen
dc.rights.statementCopyright 2015 Elsevier B.V., All rights reserved.en
dc.subect.keywordsGestational diabetes Pregnancy Risk stratification Screeningen
dc.identifier.authoremailTeede H.J.; Helena.teede@monash.eduen
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptDiabetes and Vascular Medicine-
crisitem.author.deptNursing and Midwifery Education and Strategy (NaMES)-
crisitem.author.deptObstetrics and Gynaecology (Monash Women's)-
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