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 @entity453 in clinical populations. DESIGN: Retrospective analysis of consecutive @entity1 with complete data from exercise testing and coronary angiography referred for evaluation of possible @entity453 . 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. @entity1 : 1080 consecutive @entity1 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 @entity1 and a test set of 379 @entity1 . @entity1 with previous coronary artery bypass surgery, @entity340 , marked degrees of resting ST @entity308 , and @entity714 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 @entity453 . 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 ( @entity1 with all three equations predicting <21% probability of severe @entity301 ), no agreement, and high risk (all three equations with >39% probability) for severe @entity453 . Because the @entity1 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 @entity301 to be dependent on agreement between these three equations has made them likely to function in all clinical populations. The agreement approach should be an efficient method for the evaluation of populations with varying prevalence of @entity453 , limiting the use of more expensive noninvasive and invasive testing to @entity1 with a higher probability of left main or @entity453 . 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 @entity1 access to subspecialty care.

An agreement approach to predict severe XXXX with clinical and exercise test data.

@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'PATIENTS', 'men'] @entity308 :: ('MESH:D003866', 'Disease') :: ['depression'] @entity714 :: ('MESH:D002037', 'Disease') :: ['left bundle branch block'] @entity301 :: ('MESH:D003327', 'Disease') :: ['coronary disease'] @entity453 :: ('MESH:D003324', 'Disease') :: ['triple-vessel coronary artery disease', 'coronary artery disease'] @entity340 :: ('MESH:D006349', 'Disease') :: ['valvular heart disease']
@entity453 :: (MESH:D003324,Disease) :: ['angiographic coronary artery disease']
