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dc.contributor.authorAllen, REen_US
dc.contributor.authorZamora, Jen_US
dc.contributor.authorArroyo-Manzano, Den_US
dc.contributor.authorVelauthar, Len_US
dc.contributor.authorAllotey, Jen_US
dc.contributor.authorThangaratinam, Sen_US
dc.contributor.authorAquilina, Jen_US
dc.date.accessioned2017-09-25T10:12:14Z
dc.date.available2017-08-23en_US
dc.date.issued2017-10en_US
dc.date.submitted2017-09-22T08:21:38.007Z
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/25846
dc.description.abstractOBJECTIVE: To validate the increasing number of prognostic models being developed for preeclampsia using our own prospective study. STUDY DESIGN: A systematic review of literature that assessed biomarkers, uterine artery Doppler and maternal characteristics in the first trimester for the prediction of preeclampsia was performed and models selected based on predefined criteria. Validation was performed by applying the regression coefficients that were published in the different derivation studies to our cohort. We assessed the models discrimination ability and calibration. RESULTS: Twenty models were identified for validation. The discrimination ability observed in derivation studies (Area Under the Curves) ranged from 0.70 to 0.96 when these models were validated against the validation cohort, these AUC varied importantly, ranging from 0.504 to 0.833. Comparing Area Under the Curves obtained in the derivation study to those in the validation cohort we found statistically significant differences in several studies. CONCLUSION: There currently isn't a definitive prediction model with adequate ability to discriminate for preeclampsia, which performs as well when applied to a different population and can differentiate well between the highest and lowest risk groups within the tested population. The pre-existing large number of models limits the value of further model development and future research should be focussed on further attempts to validate existing models and assessing whether implementation of these improves patient care.en_US
dc.format.extent119 - 125en_US
dc.languageengen_US
dc.language.isoenen_US
dc.relation.ispartofEur J Obstet Gynecol Reprod Biolen_US
dc.rights© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPrediction modelsen_US
dc.subjectPreeclampsiaen_US
dc.subjectScreeningen_US
dc.subjectValidationen_US
dc.subjectAdulten_US
dc.subjectFemaleen_US
dc.subjectHumansen_US
dc.subjectModels, Theoreticalen_US
dc.subjectPre-Eclampsiaen_US
dc.subjectPregnancyen_US
dc.subjectPregnancy Trimester, Firsten_US
dc.subjectPrognosisen_US
dc.titleExternal validation of preexisting first trimester preeclampsia prediction models.en_US
dc.typeArticle
dc.identifier.doi10.1016/j.ejogrb.2017.08.031en_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/28888181en_US
pubs.notesNo embargoen_US
pubs.publication-statusPublisheden_US
pubs.volume217en_US
dcterms.dateAccepted2017-08-23en_US


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