Abstract:
: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.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Bourdès V,Ferrières J,Amar J,Amelineau E,Bonnevay S,Berlion M,Danchin Ndoi
10.1007/s11517-011-0785-4subject
Has Abstractpub_date
2011-08-01 00:00:00pages
947-55issue
8eissn
0140-0118issn
1741-0444journal_volume
49pub_type
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