Performance characteristics of various exercise ECG classifiers in different clinical populations

Abstract To improve the diagnostic power of the exercise electrocardiographic test in detecting myocardial ischemia, the authors have recently developed a diagnostic method called multivariate ST-segment/heart rate (ST/HR) analysis (MUSTA). The goal of this study was to evaluate the validity of MUSTA in different clinical populations and to compare its performance characteristics with ST-segment depression, the ST/HR slope, and the ΔST/HR index in these populations. The computerized exercise electrocardiographic measurements were performed on 1,507 cases, and 382 patients were selected as the study population: 161 with significant coronary artery disease according to coronary angiography and 221 with a low likelihood of coronary artery disease. The diagnostic accuracy of MUSTA in the pooled population was 77.7% (297 out of 382 patients), which was clearly better than the accuracy of 69.6% (266 out of 382 patients) using the conventional ST-segment depression criterion of 0.10 mV in detecting coronary artery disease and exercise-induced myocardial ischemia. According to receiver operating characteristics analysis, MUSTA had significantly better diagnostic power than the other classifiers. These findings suggest that multivariate and compartmental analysis methods like MUSTA can further improve the clinical importance of the exercise electrocardiogram.