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

Tópicos

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Resumo

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.

Resumo Limpo

prevenir studi artifici neural network nn appli larg sampl patient recent first acut coronari syndrom ac identifi determin persist evidencebas cardiovascular medic ebcm antithrombot betablock statin angiotensin convert enzym inhibitoracei andor angiotensinii receptor blockerarb octob april general practition recruit patient mean time ac occurr month patient profil ebcm persist determin use automat rule generat nn predict accuraci nn compar logist regress lr use area receiveroper characteristicsauroc hospit discharg ebcm prescrib patient ebcm persist rate month ac ebcm persist profil combin overweight hypercholesterolemia coronari arteri bypass graft low educ level posit predict valu auroc curv show better predict accuraci nn compar lr model

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