Multivariable analysis of clinical and exercise test variables has the potential to become both a useful tool for assisting in the diagnosis of coronary artery disease and reducing the cost of evaluating patients with suspected coronary disease. Managed care and capitation require that tests such as the exercise test or its replacements, be used only when they can accurately and reliably identify which patients need medications, counseling, or further evaluation or intervention. The replacements for the standard exercise electrocardiogram test require expensive equipment and personnel, and their incremental value is currently being evaluated. Because general practitioners are to function as gatekeepers and decide which patients must be referred to the cardiologist, they will need to use the basic tools they have available (ie, history, physical exam, and the exercise test) in an optimal fashion. However, the discriminating power of the variables from the medical history and exercise test remains unclear because of inadequate study design and differences in study populations. There is a need for further evaluation of these routinely obtained variables to improve the accuracy of prediction algorithms especially in women. Of paramount concern is the need to avoid workup bias by having patients agree to testing before the decision for angiography is made. The portability and reliability of these equations must be shown because access to specialized care must be safeguarded. By reviewing the available studies considering clinical and exercise test variables to predict coronary angiographic findings, we have attempted to provide guidelines and recommendations for a more uniform approach to this endeavor in future investigations. Hopefully, the next generation of multivariable equations will be robust and portable, and empower the clinician to assure the cardiac patient access to appropriate cardiac care.