Sports Medicine

, Volume 31, Issue 6, pp 387–408

Assessing Patients with Possible Heart Disease Using Scores


  • Katerina Shetler
    • Cardiology Division (111C)Veterans Affairs Palo Alto Healthcare System
  • Arna Karlsdottir
    • Cardiology Division (111C)Veterans Affairs Palo Alto Healthcare System
    • Cardiology Division (111C)Veterans Affairs Palo Alto Healthcare System
Current Opinion

DOI: 10.2165/00007256-200131060-00001

Cite this article as:
Shetler, K., Karlsdottir, A. & Froelicher, V. Sports Med (2001) 31: 387. doi:10.2165/00007256-200131060-00001


Multivariable analysis of 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 are functioning as gatekeepers and decide which patients must be referred to the cardiologist, they need to use the basic tools they have available (i.e. history, physical examination and the exercise test), in an optimal fashion. Scores derived from multivariable statistical techniques considering clinical and exercise data have demonstrated superior discriminating power compared with simple classification of the ST 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 run complicated equations and derive these 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. Their results have also been compared with physician judgment and found to estimate the presence of coronary disease and prognosis as well as expert cardiologists and often better than nonspecialists.

However, the discriminating power of specific variables from the medical history and exercise test remains unclear because of inadequate study design and differences in study populations. Should expired gases be substituted for estimated metabolic equivalents (METs)? Should ST/heart rate (HR) index be used instead of putting these measurements separately into the models? Should right-sided chest leads and HR in recovery be considered? There is a need for further evaluation of these routinely obtained variables to improve the accuracy of prediction algorithms especially in women. The portability and reliability of these equations must be demonstrated since access to specialised care must be safeguarded. Hopefully, sequential assessment of the clinical and exercise test data and application of the newer generation of multivariable equations can empower the clinician to assure the cardiac patient access to appropriate and cost-effective cardiological care.

Copyright information

© Adis International Limited 2001