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(Circulation. 1995;91:54-65.)
© 1995 American Heart Association, Inc.
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
From the Carlyle Fraser Heart Center, Emory:Crawford Long Hospital, Department of Medicine, Division of Cardiology and Department of Radiology, Emory University School of Medicine, Atlanta, Ga, and Thomas Killip Division of Cardiology, Department of Medicine, Beth Israel Medical Center, Albert Einstein College of Medicine, New York, NY (S.F.H.).
Correspondence to Randolph E. Patterson, MD, Professor of Medicine (Cardiology) and Radiology, Emory University School of Medicine, Carlyle Fraser Heart Center, Emory:Crawford Long Hospital, 550 Peachtree St, NE, Atlanta, GA 30365.
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 
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/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.
Key Words: cost-effectiveness • electrocardiography • imaging • angiography • coronary artery disease
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