Linked Life Data

21309494

Source:http://linkedlifedata.com/resource/pubmed/id/21309494

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2011-2-11
pubmed:abstractText
The aim of this study was to develop an improved binary logistic regression model for predicting the risk of intracranial aneurysm rupture. A cohort of patients (n=37) with aneurysms underwent three-dimensional digital subtraction angiography examination to measure several morphological parameters of the aneurysm. The aspect ratio (height/neck size) and the size ratio (length/mean diameter of parent vessel) were also calculated. All the morphological parameters combined with the aneurysm location and the patient's baseline data were used to derive a backward binary logistic regression model. In order to validate the model, it was applied to another independent cohort of 19 patients with aneurysms. The model had sensitivity, specificity and accuracy of 84.6%, 66.7% and 78.9%, respectively. This binary logistic regression model of aneurysm rupture risk identified the status of an aneurysm with high accuracy and could form the basis of more complex models in the future.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0300-0605
pubmed:author
pubmed:issnType
Print
pubmed:volume
38
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1785-94
pubmed:meshHeading
pubmed:articleTitle
Assessment of the risk of rupture of intracranial aneurysms using three-dimensional cerebral digital subtraction angiography.
pubmed:affiliation
Department of Cerebral Surgery, The Second Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou, China.
pubmed:publicationType
Journal Article