title: A consensus approach to diagnosing coronary artery disease based on clinical and exercise test data.
abstract: OBJECTIVE
  To demonstrate that a consensus approach for combining prediction equations based on clinical and exercise test variables derived from different populations can stratify patients referred for possible coronary artery disease (CAD) into low-, intermediate-, and high-risk groups.
  
  
  METHODS
  Retrospective analysis of consecutive patients with complete data from exercise testing and coronary angiography referred for evaluation of possible CAD. After derivation of a logistic equation in our own training set of patients, this equation, along with two other equations developed independently by other investigators, was validated in a test set. The validation strategy for the consensus approach included the following: (1) calculation of probability scores for each patient using each logistic equation independently; (2) determination of probability thresholds in the training set to divide the patients into three groups-low risk (prevalence CAD <5%), intermediate risk (5 to 70%), and high risk (>70% prevalence of CAD); (3) using agreement among at least two of three of the prediction equations to generate "consensus" for each patient; and (4) application of the consensus approach thresholds to the test set of patients.
  
  
  METHODS
  Two university-affiliated Veteran's Affairs medical centers.
  
  
  METHODS
  We studied 718 consecutive men between 1985 and 1995 who had coronary angiography within 3 months of an exercise treadmill test for suspected CAD. The population was randomly divided into a training set of 429 patients and a test set of 289 patients. Patients with previous myocardial infarction or coronary artery bypass surgery, valvular heart disease, left bundle branch block, or any Q waves present on their resting ECG were excluded from the study.
  
  
  METHODS
  Recording of clinical and exercise test data along with visual interpretation of the ECG recordings on standardized forms and abstraction of visually interpreted angiographic data from clinical catheterization reports.
  
  
  RESULTS
  We demonstrated that by using simple clinical and exercise test variables, we could improve on the standard use of ECG criteria during exercise testing for diagnosing CAD. Using the consensus approach divided the test set into populations with low, intermediate, and high risk for CAD. Since the patients in the intermediate group would be sent for further testing and would eventually be correctly classified, the sensitivity of the consensus approach is 94% and the specificity is 92%. The consensus approach controls for varying disease prevalence, missing data, inconsistency in variable definition, and varying angiographic criterion for stenosis severity. The percent of correct diagnoses increased from the 67% for standard exercise ECG analysis and from the 80% for multivariable predictive equations alone to >90% correct diagnoses for the consensus approach.
  
  
  CONCLUSIONS
  The consensus approach has made population-specific logistic regression equations portable to other populations. Excellent diagnostic characteristics can be obtained using simple data and measurements. The consensus approach is best applied utilizing a programmable calculator or a computer program to simplify the process of calculating the probability of CAD using the three equations.
authors.affiliation: Cardiology Division, Veterans Affairs Palo Alto Health Care System, Stanford University, Calif 94304, USA.
authors.forename: D
authors.initials: D
authors.lastname: Do
authors.forename: J A
authors.initials: JA
authors.lastname: West
authors.forename: A
authors.initials: A
authors.lastname: Morise
authors.forename: E
authors.initials: E
authors.lastname: Atwood
authors.forename: V
authors.initials: V
authors.lastname: Froelicher
mesh_terms: D017023:Coronary Angiography / Q000706:statistics & numerical data; D003327:Coronary Disease / Q000145:classification / Q000175:diagnosis*; D004562:Electrocardiography; D005080:Exercise Test / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D008297:Male; D011336:Probability; D011897:Random Allocation; D012189:Retrospective Studies; D012307:Risk Factors