Comparison of computer ST criteria for diagnosis of severe coronary artery disease
- PMID: 8094938
- DOI: 10.1016/0002-9149(93)90509-b
Comparison of computer ST criteria for diagnosis of severe coronary artery disease
Abstract
To determine which computer ST criteria are superior for predicting patterns and severity of coronary artery disease during exercise testing, 230 male veterans were studied who had both coronary angiography and a treadmill exercise test. Significant (p < or = 0.05) differences in computer-scored ST criteria were observed among patients with progressively increasing disease severity. Three-vessel/left main disease produced responses significantly different from 1- and 2-vessel disease or those with < 70% occlusion. Discriminant function analysis revealed that horizontal or downsloping ST depression measured at the J junction during exercise or recovery, or both, was the most powerful predictor of severe disease. With use of a cut point of 0.075 mV ST depression, horizontal or downsloping ST depression alone yielded a sensitivity of 50% (95% confidence interval = 35 to 65%) and specificity of 71% for prediction of severe disease; the only additional variable that added significantly to the prediction was exercise capacity, which improved sensitivity to 57% (95% confidence interval = 41 to 72%) with no change in specificity. Measurements of ST amplitude at the J junction and at 60 ms after the J point without slope considered and other scores, including the Treadmill Exercise Score, ST Integral, and ST/heart rate index, had a lower but comparable predictive accuracy when compared with horizontal or downsloping ST depression. Prediction of coronary artery disease severity can be achieved using computerized electrocardiographic measurements obtained during exercise testing. The most powerful marker for severe coronary artery disease is the amount of horizontal or downsloping ST-segment depression during exercise or recovery, or both, a measurement that stimulates the traditional visual approach.
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