Linked Life Data

20661845

Source:http://linkedlifedata.com/resource/pubmed/id/20661845

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2011-2-7
pubmed:abstractText
We aimed to develop a predictive model for the chance of a successful external cephalic version (ECV). We performed a prospective cohort study of women with a singleton fetus in breech presentation with a gestational age of 36 weeks or more. Data on parity, maternal age, body mass index, ethnicity, gestational age, placental location, fetal position, estimated fetal weight, and amniotic fluid were recorded in all participants. Multivariable logistic regression analysis with a stepwise backward selection procedure was used to construct a prediction model for the occurrence of successful ECV. We included a total of 310 women. Multivariable logistic regression analysis demonstrated that multiparity, increasing estimated fetal weight, and normal amniotic fluid were favorable predictors of successful ECV. Anterior placenta location was an unfavorable predictor for ECV outcome. Discrimination of the model was fair (area under the curve 0.71), and the calibration of the model was acceptable. Our prediction model appears to discriminate between women with a poor chance of successful ECV (less than 20%) and women with a good chance of success (more than 60%). When this model is validated externally, it could be used for patient counseling and clinical decision making.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1098-8785
pubmed:author
pubmed:copyrightInfo
© Thieme Medical Publishers.
pubmed:issnType
Electronic
pubmed:volume
28
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
103-10
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Prediction of success of external cephalic version after 36 weeks.
pubmed:affiliation
Department of Obstetrics and Gynecology, Academic Medical Centre, University of Amsterdam, The Netherlands. mkok@amc.uva.nl
pubmed:publicationType
Journal Article