Noninvasive and angiographic evaluation of coronary artery disease in patients with peripheral vascular disease

Abstract The accuracy of exercise testing for detection of coronary artery disease in a population with a high incidence of claudication was evaluated in 58 consecutive patients with abdominal aortic aneurysms or lower extremity occlusive disease. Each patient was evaluated by history and physical examination, symptom-limited testing with exercise treadmill, arm ergometry and exercise radionuclide ventriculography. An algorithm was designed that retrospectively examined the results of each test in a stepwise fashion to simulate a clinical decisionmaking process. The results of the clinical examination, each of the exercise tests and the noninvasive diagnostic algorithm were compared with the results of coronary arteriography. The predictive accuracy of the clinical evaluation was 36%, treadmill stress testing 57%, treadmill stress plus arm ergometry 74%, exercise radionuclide ventriculography 57% and the noninvasive diagnostic algorithm 89%. When discriminant analysis was applied to all of the exercise variables, no individual test improved the accuracy of the noninvasive diagnostic algorithm. When the analysis considered only individual variables without the algorithm, the model correctly classified only 67% of the patients. Thus, accurate noninvasive evaluation of coronary artery disease is possible in patients with severe peripheral vascular disease when care is taken to design exercise protocols that allow adequate stress on the cardiovascular system.