Prediction of persistence of combined evidence-based cardiovascular medications in patients with acute coronary syndrome after hospital discharge using neural networks.
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 convert...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 49; no. 8; pp. 947 - 956 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104660400&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104660400 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2011 vid: 49 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104660400 NLM21598000 2011221665 10.1007/s11517-011-0785-4 NLM21598000 104660400 ppf: 947 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Prediction of persistence of combined evidence-based cardiovascular medications in patients with acute coronary syndrome after hospital discharge using neural networks. aug: au: Bourdès V Ferrières J Amar J Amelineau E Bonnevay S Berlion M Danchin N Bourdès, Valérie Ferrières, Jean Amar, Jacques Amelineau, Elisabeth Bonnevay, Stéphane Berlion, Maryse Danchin, Nicolas affil: THEMIS-ICTA Group, Bioparc-60 Avenue Rockefeller, Lyon 69008, France sug: subj: Acute Coronary Syndrome Drug Therapy Cardiovascular Agents Administration and Dosage Aged Cross Sectional Studies Drug Administration Schedule Medical Practice, Evidence-Based Methods Female Male Medication Compliance Middle Age Neural Networks (Computer) Patient Discharge Aged: 65+ years Middle Aged: 45-64 years Female Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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