Prediction of persistence of combined evidence-based cardiovascular medications in patients with acute coronary syndrome after hospital discharge using neural networks

• Published in: Medical & biological engineering & computing 2011-08, Vol.49 (8), p.947-955
• Main Authors: Bourdès, Valérie, Ferrières, Jean, Amar, Jacques, Amelineau, Elisabeth, Bonnevay, Stéphane, Berlion, Maryse, Danchin, Nicolas
• Format: Article
• Language: English
• Subjects: Accuracy; Acute Coronary Syndrome - drug therapy; Acute coronary syndromes; Aged; Analysis; Angina pectoris; Beta blockers; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Cardiology; Cardiology and cardiovascular system; Cardiovascular Agents - administration & dosage; Cardiovascular disease; Clinical medicine; Computer Applications; Computer Science; Coronary vessels; Cross-Sectional Studies; Drug Administration Schedule; Enzymes; Evidence-Based Medicine - methods; Family physicians; Feature selection; Female; Genetic algorithms; Heart attacks; Hospitals; Human health and pathology; Human Physiology; Humans; Imaging; Life Sciences; Male; Medical Imaging; Medical treatment; Medication Adherence - statistics & numerical data; Middle Aged; Neural networks; Neural Networks (Computer); Original Article; Patient Discharge; Patients; Radiology; Regression analysis; Statins; Statistical methods; Studies; Variables
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-011-0785-4

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.