Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results

Author(s)

P.S. Duarte ^[1] ; L.E. Mastrocolla ^[2] ; P.S. Farsky ^[3] ; C.R.E.P.S. Sampaio ^[4] ; P.A. Tonelli ^[5] ; L.C. Barros ^[6] ; N.R. Ortega ^[7] ; J.C.R. Pereira ^[8]

Affiliation

^[1] Centro de Medicina Diagnóstica. Seção de Medicina Nuclear

^[2] Centro de Medicina Diagnóstica. Seção de Cardiologia, Fleury - Brasil

^[3] Centro de Medicina Diagnóstica. Seção de Cardiologia, Fleury - Brasil

^[4] Centro de Medicina Diagnóstica. Seção de Cardiologia, Fleury - Brasil

^[5] Universidade de São Paulo. Faculdade de Medicina. Instituto de Matemática e Estatística

^[6] Universidade Estadual de Campinas. Instituto de Matemática, Estatística e Computação Científica - Brasil

^[7] Universidade de São Paulo. Faculdade de Medicina. Informática Médica - Brasil

^[8] Universidade de São Paulo. Faculdade de Saúde Pública. Departamento de Epidemiologia

• Document type: Journal article
• Source: Brazilian Journal of Medical and Biological Research; v. 39, n. 1, p. 9-18, 2006-01-00.

Abstract

Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS. (AU)