Linked Life Data

21598000

Source:http://linkedlifedata.com/resource/pubmed/id/21598000

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2011-7-20
pubmed:abstractText
In the PREVENIR-5 study, artificial neural networks (NN) were applied to a large sample of patients with recent first acute coronary syndrome (ACS) to identify determinants of persistence of evidence-based cardiovascular medications (EBCM: antithrombotic + beta-blocker + statin + angiotensin converting enzyme inhibitor-ACEI and/or angiotensin-II receptor blocker-ARB). From October 2006 to April 2007, 1,811 general practitioners recruited 4,850 patients with a mean time of ACS occurrence of 24 months. Patient profile for EBCM persistence was determined using automatic rule generation from NN. The prediction accuracy of NN was compared with that of logistic regression (LR) using Area Under Receiver-Operating Characteristics-AUROC. At hospital discharge, EBCM was prescribed to 2,132 patients (44%). EBCM persistence rate, 24 months after ACS, was 86.7%. EBCM persistence profile combined overweight, hypercholesterolemia, no coronary artery bypass grafting and low educational level (Positive Predictive Value = 0.958). AUROC curves showed better predictive accuracy for NN compared to LR models.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1741-0444
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
49
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
947-55
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Prediction of persistence of combined evidence-based cardiovascular medications in patients with acute coronary syndrome after hospital discharge using neural networks.
pubmed:affiliation
THEMIS-ICTA Group, Bioparc-60 Avenue Rockefeller, Lyon 69008, France. valerie.bourdes@themis-rd.fr
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't