Exercise testing scores as an example of better decisions through science.
BACKGROUND
The application of common statistical techniques to clinical and exercise test data has the potential to become a useful tool for assisting in the diagnosis of coronary artery disease, assessing prognosis, and reducing the cost of evaluating patients with suspected coronary disease. Since general practitioners function as gatekeepers and decide which patients must be referred to the cardiologist, they need to optimally use the basic tools they have available (i.e., history, physical exam, and the exercise test).


METHODS
Review of the literature with a focus on the scientific techniques for aiding the decision-making process.


RESULTS
Scores derived from multivariable statistical techniques considering clinical and exercise data have demonstrated superior discriminating power when compared using receiver-operating-characteristic curves with the ST segment response. In addition, by stratifying patients as to probability of disease and prognosis, they provide a management strategy. While computers as part of information management systems can calculate complicated equations to provide scores, physicians are reluctant to trust them. Thus, these scores have been represented as nomograms or simple additive tables so physicians are comfortable with their application. Scores have also been compared with physician judgment and been found to estimate the presence of coronary disease and prognosis as well as expert cardiologists, and often better than nonspecialists.


CONCLUSIONS
Multivariate scores can empower the clinician to assure the cardiac patient with access to appropriate and cost-effective cardiological care.
Ashley
Euan
E
Cardiology Division (111C), Veterans Affairs Palo Alto Health Care System, Stanford University, 3801 Miranda Avenue, Palo Alto, CA 94304, USA.
Myers
Jonathan
J
Froelicher
Victor
V
D000328:Adult; D000367:Age Factors; D003324:Coronary Artery Disease / Q000175:diagnosis*; D019317:Evidence-Based Medicine / Q000592:standards; D005080:Exercise Test / Q000592:standards* / Q000706:statistics & numerical data*; D005260:Female; D017408:Guidelines as Topic; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D012372:ROC Curve; D012107:Research Design; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012737:Sex Factors