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Assessing patients with possible heart disease using scores
K Shetler1, A Karlsdottir, V Froelicher
1Cardiology Division, Veterans Affairs Palo Alto Healthcare System, Stanford University, California 94304, USA.
Insights
Multivariable analysis using clinical and exercise data aids in diagnosing coronary artery disease and assessing prognosis. These methods, presented as physician-friendly scores, improve patient care and reduce costs.
Area of Science:
- Cardiology
- Medical Informatics
- Biostatistics
Background:
- General practitioners act as gatekeepers for specialist cardiac care.
- Optimal use of basic clinical tools (history, physical exam, exercise test) is crucial.
- Current diagnostic methods for coronary artery disease (CAD) can be improved.
Purpose of the Study:
- To evaluate the utility of multivariable analysis of clinical and exercise test data for CAD diagnosis and prognosis.
- To explore the effectiveness of derived scores compared to traditional methods.
- To identify areas for improvement in prediction algorithms for cardiac patients.
Main Methods:
- Multivariable statistical techniques applied to clinical and exercise test data.
- Development and representation of scores as nomograms or additive tables for physician use.
- Comparison of score-derived estimates with physician judgment.
Main Results:
- Multivariable scores show superior discriminating power over simple ST response classification.
- Scores effectively stratify patients by disease probability and prognosis.
- Physician-reluctant computer-derived scores, when presented accessibly, perform as well as or better than expert cardiologists.
Conclusions:
- Multivariable analysis of clinical and exercise data offers a valuable tool for CAD diagnosis, prognosis assessment, and cost reduction.
- Further research is needed to clarify the discriminating power of specific variables and improve prediction algorithms, especially for women.
- Validated, portable prediction equations can empower clinicians to ensure appropriate and cost-effective cardiac care.
Abstract:
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 safe-guarded. 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.