Comparison of cost-effectiveness and utility of exercise ECG, single photon emission computed tomography, positron emission tomography, and coronary angiography for diagnosis of coronary artery disease

R E Patterson, R L Eisner, S F Horowitz
Circulation 1995 January 1, 91 (1): 54-65

BACKGROUND: To compare cost-effectiveness and utility of four clinical algorithms to diagnose obstructive coronary atherosclerotic heart disease (CAD), we compared exercise ECG (ExECG), stress single photon emission computed tomography (SPECT), positron emission tomography (PET), and coronary angiography.

METHODS AND RESULTS: Published data and a straightforward mathematical model based on Bayes' theorem were used to compare strategies. Effectiveness was defined as the number of patients with diagnosed CAD, and utility was defined as the clinical outcome, ie, the number of quality-adjusted life years (QALY) extended by therapy after the diagnosis of CAD. Our model used published values for costs, accuracy, and complication rates of tests. Analysis of the model indicates the following results. (1) The direct cost (fee) for each test differs considerably from total cost per delta QALY. (2) As pretest likelihood of CAD (pCAD) in the population increases, there is a linear increase in cost per patient tested but a hyperbolic decrease in cost per effect and cost per utility unit, ie, increased cost-effectiveness and decreased cost per utility unit. (3) At pCAD < 0.70, analysis of the model indicates that stress PET is the most cost-effective test, with the lowest cost per utility, followed by SPECT, ExECG, and angiography, in that order. (4) Above a threshold value of pCAD of 0.70 (for example, middle-aged men with typical angina), proceeding directly to angiography as the first test showed the lowest cost per effect or utility. This quantitative model has the advantage of estimating a threshold value of pCAD (0.70) at which the rank order of cost-effectiveness and cost per utility unit change. The model also allows substitution of different values for any variable as a way to account for the uncertainties of clinical data, ie, changing costs, test accuracy and risk, etc. This procedure, called sensitivity analysis, showed that the rank order of cost-effectiveness did not change despite changes in several variables.

CONCLUSIONS: (1) Estimation of total costs of diagnostic tests for CAD requires consideration not only of the direct cost of the test per se (eg, test fees) but also of the indirect and induced costs of management algorithms based on the test (eg, cost/delta QALY). (2) It is essential to consider the clinical history (pCAD) when selecting the clinical algorithm to make a diagnosis with the lowest cost per effect or cost per utility unit. (3) Stress PET shows the lowest cost per effect or cost per utility unit in patients with pCAD < 0.70. (4) Angiography shows the lowest cost per effect or cost per utility unit in patients with pCAD > 0.70.

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