Logistic discriminant analysis improves diagnostic accuracy of exercise testing for coronary artery disease in women.

Abstract

Diagnostic accuracy of the exercise electrocardiogram in women has been shown to be limited for the detection of coronary artery disease. New diagnostic methods based on computer analysis of the exercise electrocardiogram and multivariate analysis have improved the diagnostic value of exercise testing in male subjects. The aim of the present study was to assess whether the diagnostic value of exercise testing can be enhanced in women by using multivariate analysis of exercise data. Between 1978 and 1984, 135 infarct-free women underwent exercise testing and coronary angiography. Significant coronary artery disease was present in 41% of the patients. In this first group, maximal exercise variables were submitted to a stepwise logistic analysis. Work load, heart rate, and ST60X were selected to build a diagnostic model. The model was tested in a second group of 115 catheterized women (significant coronary artery disease in 47%) and of 76 volunteers. We compared the present model with conventional analysis of the exercise electrocardiogram, with ST changes adjusted for heart rate, and with a previously described analysis. In both groups, sensitivity was better with the present model (66% and 70%) than by conventional (68% and 59%) and by the previously described analysis (57% and 44%) without a loss of specificity (85% and 93%). Receiver-operator characteristic curves showed also a better diagnostic accuracy with the present model. In women, logistic analysis of exercise variables improves the diagnostic value of exercise testing. It yields a significantly better sensitivity without a loss of specificity.