An agreement approach to predict severe angiographic coronary artery disease with clinical and exercise test data

Objective To demonstrate that an agreement approach to applying equations on the basis of clinical and exercise test variables is an accurate, self-calibrating; and cost-efficient method for predicting severe coronary artery disease in clinical populations. Design Retrospective analysis of consecutive patients with complete data from exercise testing and coronary angiography referred For evaluation of possible coronary artery disease. After developing an equation in a training set this equation and two other equations developed by other investigators were validated in a test set. The study was performed at two university-affiliated Veteran's Affairs medical centers. Patients 1080 consecutive men studied between 1985 and 1995 who had coronary angiography within 3 months of the treadmill test. The population was randomly divided into a training set of 701 patients and a test set of 379 patients. Patients with previous coronary artery bypass surgery valvular heart disease, marked degrees of resting ST depression, and left bundle branch block were excluded. Measurements Recording of clinical and exercise test data along with visual interpretation of the electrocardiogram recordings on standardized forms and abstraction of visually interpreted angiographic data from clinical catheterization reports. Results simple clinical and exercise test variables improved the standard application of exercise-induced ST criteria for predicting severe coronary artery disease. By setting probability thresholds for severe disease of <20% and >40% for the three prediction equations, the agreement approach divided the test set into three groups: low risk (patients with all three equations predicting <21% probability of severe coronary disease), no agreement and high risk (all three equations with >39% probability) for severe coronary artery disease. Because the patients in the no agreement group would be sent for further testing and would eventually be correctly classified, the sensitivity of the agreement approach was 89% and the specificity was 96%. The agreement approach appeared to be unaffected by disease prevalence, missing data, variable definitions, or even angiographic criteria. Conclusions Requiring diagnosis of severe coronary disease to be dependent on agreement between these three equations has made them likely to function in all clinical populations. The agreement approach should be on efficient method for the evaluation of populations with varying prevalence of coronary artery disease, limiting the use of more expensive noninvasive and invasive testing to patients with a higher probability of left main or triple-vessel coronary artery disease. This approach provides a strategy that can be applied by inputting the results of basic clinical assessment into a programmable calculator or a computer to assist the practitioner in deciding when further evaluation is appropriate, thus assuring patients access to subspecialty care.